@article{WuHuangRamirez-2024-One-bitspectrumsensingforcognitive,
title = {One-bit spectrum sensing for cognitive radio},
author = {P. -W. Wu and L. Huang and David Ram\'{i}rez and Y. -H. Xiao and H. C. So},
doi = {10.1109/TSP.2023.3343569},
issn = {1053-587X},
year  = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Trans. Signal Process.},
volume = {72},
pages = {549\textendash564},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{sztamfater2024novel,
title = {Novel method for the Computation of In-Orbit Collision Probability by Multilevel Splitting and Surrogate Modelling},
author = {Yannick Sztamfater Garcia and Manuel Sanjurjo Rivo and Joaquin Miguez and Guillermo Escribano},
year  = {2024},
date = {2024-01-01},
booktitle = {AIAA SCITECH 2024 Forum},
pages = {1813},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{PORRASSEGOVIA2024284,
title = {Smartphone-based safety plan for suicidal crisis: The SmartCrisis 2.0 pilot study},
author = {Alejandro Porras-Segovia and Ana Maria De Granda-Beltr\'{a}n and Claudia Gallardo and Sof\'{i}a Abascal-Peir\'{o} and Mar\'{i}a Luisa Barrig\'{o}n and Antonio Art\'{e}s-Rodr\'{i}guez and Jorge L\'{o}pez-Castroman and Philippe Courtet and Enrique Baca-Garc\'{i}a},
url = {https://www.sciencedirect.com/science/article/pii/S0022395623005526},
doi = {https://doi.org/10.1016/j.jpsychires.2023.11.039},
issn = {0022-3956},
year  = {2024},
date = {2024-01-01},
journal = {Journal of Psychiatric Research},
volume = {169},
pages = {284-291},
abstract = {Here we present the findings of the pilot phase of the SmartCrisis 2.0 Randomized Clinical Trial. This pilot study aimed to explore the feasibility and acceptability of a safety plan contained in a smartphone app. Our sample consisted patients with a history of recent suicidal behaviour who installed a smartphone-based safety plan. To explore the satisfaction with of the safety plan, two patient satisfaction surveys were conducted: one qualitative and one quantitative. To explore the objective use of the safety plan, we gained access to texts contained in the safety plans completed by the patients. Participation rate was 77%, while 48.9% patients completed both satisfaction surveys at the end of the pilot phase. N = 105 successfully installed the safety plan. In a scale from 1 to 10, users rated the usefulness of the security plan at 7.4, the usability at 8.9, the degree to which they would recommend it to others at 8.6 and the overall satisfaction with the project including evaluations at 9.6. The most widely completed tab was warning signs. Feeling sad or lonely was the warning sign most commonly reported by patients. The second most completed tab was internal coping strategies. Walking or practicing any other exercise was the strategy most commonly resorted to. Our smartphone-based safety plan appears to be a feasible intervention. Data obtained from this pilot study showed high participation rates and high acceptability by patients. This, together with the general satisfaction with the project, supports its implementation in the clinical practice.},
keywords = {Ecological momentary intervention, Experience-sampling method, Suicide, Suicide attempt, Suicide ideation, Time-sampling procedures},
pubstate = {published},
tppubtype = {article}
}
@article{XiaoHuangRamirez-2023-Covariancematrixrecoveryfromone-bit,
title = {Covariance matrix recovery from one-bit data with non-zero quantization thresholds: Algorithm and performance analysis},
author = {Y. -H. Xiao and L. Huang and David Ram\'{i}rez and C. Qian and H. C. So},
doi = {10.1109/TSP.2023.3325664},
issn = {1053-587X},
year  = {2023},
date = {2023-11-01},
journal = {IEEE Trans. Signal Process.},
volume = {71},
pages = {4060\textendash4076},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{info:doi/10.2196/47167,
title = {Continuous Assessment of Function and Disability via Mobile Sensing: Real-World Data-Driven Feasibility Study},
author = {Emese S\"{u}kei and Lorena Romero-Medrano and Santiago Leon-Martinez and Jes\'{u}s Herrera L\'{o}pez and Juan Jos\'{e} Campa\~{n}a-Montes and Pablo M Olmos and Enrique Baca-Garcia and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.ncbi.nlm.nih.gov/pubmed/37902823},
doi = {10.2196/47167},
issn = {2561-326X},
year  = {2023},
date = {2023-10-30},
journal = {JMIR Form Res},
volume = {7},
pages = {e47167},
abstract = {Background: Functional limitations are associated with poor clinical outcomes, higher mortality, and disability rates, especially in older adults. Continuous assessment of patients' functionality is important for clinical practice; however, traditional questionnaire-based assessment methods are very time-consuming and infrequently used. Mobile sensing offers a great range of sources that can assess function and disability daily. Objective: This work aims to prove the feasibility of an interpretable machine learning pipeline for predicting function and disability based on the World Health Organization Disability Assessment Schedule (WHODAS) 2.0 outcomes of clinical outpatients, using passively collected digital biomarkers. Methods: One-month-long behavioral time-series data consisting of physical and digital activity descriptor variables were summarized using statistical measures (minimum, maximum, mean, median, SD, and IQR), creating 64 features that were used for prediction. We then applied a sequential feature selection to each WHODAS 2.0 domain (cognition, mobility, self-care, getting along, life activities, and participation) in order to find the most descriptive features for each domain. Finally, we predicted the WHODAS 2.0 functional domain scores using linear regression using the best feature subsets. We reported the mean absolute errors and the mean absolute percentage errors over 4 folds as goodness-of-fit statistics to evaluate the model and allow for between-domain performance comparison. Results: Our machine learning\textendashbased models for predicting patients' WHODAS functionality scores per domain achieved an average (across the 6 domains) mean absolute percentage error of 19.5%, varying between 14.86% (self-care domain) and 27.21% (life activities domain). We found that 5-19 features were sufficient for each domain, and the most relevant being the distance traveled, time spent at home, time spent walking, exercise time, and vehicle time. Conclusions: Our findings show the feasibility of using machine learning\textendashbased methods to assess functional health solely from passively sensed mobile data. The feature selection step provides a set of interpretable features for each domain, ensuring better explainability to the models' decisions\textemdashan important aspect in clinical practice.},
keywords = {WHODAS; functional limitations; mobile sensing; passive ecological momentary assessment; predictive modeling; interpretable machine learning; machine learning; disability; clinical outcome},
pubstate = {published},
tppubtype = {article}
}
@article{info:doi/10.2196/43719,
title = {One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study},
author = {Maria Luisa Barrigon and Lorena Romero-Medrano and Pablo Moreno-Mu\~{n}oz and Alejandro Porras-Segovia and Jorge Lopez-Castroman and Philippe Courtet and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garcia},
url = {http://www.ncbi.nlm.nih.gov/pubmed/37656498},
doi = {10.2196/43719},
issn = {1438-8871},
year  = {2023},
date = {2023-05-01},
journal = {J Med Internet Res},
volume = {25},
pages = {e43719},
abstract = {Background: Suicide is a major global public health issue that is becoming increasingly common despite preventive efforts. Though current methods for predicting suicide risk are not sufficiently accurate, technological advances provide invaluable tools with which we may evolve toward a personalized, predictive approach. Objective: We aim to predict the short-term (1-week) risk of suicide by identifying changes in behavioral patterns characterized through real-time smartphone monitoring in a cohort of patients with suicidal ideation. Methods: We recruited 225 patients between February 2018 and March 2020 with a history of suicidal thoughts and behavior as part of the multicenter SmartCrisis study. Throughout 6 months of follow-up, we collected information on the risk of suicide or mental health crises. All participants underwent voluntary passive monitoring using data generated by their own smartphones, including distance walked and steps taken, time spent at home, and app usage. The algorithm constructs daily activity profiles for each patient according to these data and detects changes in the distribution of these profiles over time. Such changes are considered critical periods, and their relationship with suicide-risk events was tested. Results: During follow-up, 18 (8%) participants attempted suicide, and 14 (6.2%) presented to the emergency department for psychiatric care. The behavioral changes identified by the algorithm predicted suicide risk in a time frame of 1 week with an area under the curve of 0.78, indicating good accuracy. Conclusions: We describe an innovative method to identify mental health crises based on passively collected information from patients' smartphones. This technology could be applied to homogeneous groups of patients to identify different types of crises.},
keywords = {e-health; m-health; Ecological Mometary Asssessment; risk prediction; sensor monitoring; suicidal; suicide attempt; suicide},
pubstate = {published},
tppubtype = {article}
}
@article{LOPEZGALDO2023106840,
title = {Detecting train driveshaft damages using accelerometer signals and Differential Convolutional Neural Networks},
author = {Ant\'{i}a L\'{o}pez Galdo and Alejandro Guerrero-L\'{o}pez and Pablo M. Olmos and Mar\'{i}a Jes\'{u}s G\'{o}mez Garc\'{i}a},
url = {https://www.sciencedirect.com/science/article/pii/S0952197623010242},
doi = {https://doi.org/10.1016/j.engappai.2023.106840},
issn = {0952-1976},
year  = {2023},
date = {2023-01-01},
journal = {Engineering Applications of Artificial Intelligence},
volume = {126},
pages = {106840},
abstract = {Maintaining railway axles is crucial to prevent catastrophic failures and enormous human and economic costs. In recent years, there has been a growing interest in the railway industry to adopt condition monitoring techniques to enhance the safety and efficiency of the rail transport system, which maintenance is currently based on periodic inspections. In this context, this work presents a technique for real-time crack diagnosis on railway axles, based on advanced 2D-Convolutional Neural Network (CNN) architectures applied to time\textendashfrequency representations of vibration signals. One of the critical novelties is introducing a differential CNN structure that captures the system’s statistical properties, enabling generalisation between different mechanical sets and conditions. The proposed system has been trained with data corresponding to a unique wheelset assembly, showing that the model is able to diagnose cracks on the three different wheelset tested in operation under 32 different combinations of conditions, such as load, speed, sense of rotation and vibration direction. Four different crack levels have been introduced, representing the maximum one a 0.08% of the axle diameter, and the method proposed achieved Area Under the Curve (AUC) score of 0.85, significantly outperforming results obtained with other architectures proposed in the state-of-the-art, the score of the next below is 0.76. The results demonstrate the effectiveness and practicality of this approach to accurately classify the four crack levels tested within a condition monitoring system for non-stationary conditions, that would enable reliable real-time diagnosis, thus paving the way towards a more robust and efficient railway axle maintenance system.},
keywords = {Condition monitoring, Convolutional Neural Networks, Crack detection, Deep learning, Railway axles, Vibration signal},
pubstate = {published},
tppubtype = {article}
}
@article{SANCHEZMARTIN2023118928,
title = {Enhancing diversity in GANs via non-uniform sampling},
author = {Pablo S\'{a}nchez-Mart\'{i}n and Pablo M. Olmos and Fernando Perez-Cruz},
url = {https://www.sciencedirect.com/science/article/pii/S002002552300498X},
doi = {https://doi.org/10.1016/j.ins.2023.04.007},
issn = {0020-0255},
year  = {2023},
date = {2023-01-01},
journal = {Information Sciences},
volume = {637},
pages = {118928},
abstract = {Recent advances in Generative Adversarial Networks (GANs) have led to impressive results in generating realistic data. However, GANs training is still challenging, often leading to mode-collapse, where a certain type of samples dominates the generated output. To address this issue, we propose a novel training algorithm based on bidirectional GANs (BiGANs) that can be generalized to any implicit generative model. Our algorithm relies on a non-uniform sampling scheme, where data points in a minibatch are sampled with probability inversely proportional to their log-evidence. However, estimating log-evidence is computationally expensive. Instead, we propose to use the reconstruction error, which directly correlates with the log-evidence and only requires a BiGAN network evaluation. Additionally, we combine the aforementioned method with a regularization in the empirical distribution of the encoder that further boosts the performance. Our empirical results show that the proposed methods improve both the quality and diversity of the generated samples.},
keywords = {Deep generative models, Generative adversarial networks, Mode-collapse},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{RamirezSantamariaScharf-2023-Passivedetectionofrank-oneGaussian,
title = {Passive detection of rank-one Gaussian signals for known channel subspaces and arbitrary noise},
author = {David Ram\'{i}rez and I. Santamar\'{i}a and L. L. Scharf},
doi = {10.1109/ICASSP49357.2023.10094671},
year  = {2023},
date = {2023-01-01},
booktitle = {Proc. IEEE Int. Conf. Acoustics, Speech and Signal Process.},
address = {Rhodes, Greece},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{StantonWangRamirez-2023-Identifiabilityofmulti-channelfactoranalysis,
title = {Identifiability of multi-channel factor analysis},
author = {G. Stanton and H. Wang and David Ram\'{i}rez and I. Santamaria and L. L. Scharf},
year  = {2023},
date = {2023-01-01},
booktitle = {Proc. Asilomar Conf. Signals, Syst. and Computers},
address = {Pacific Grove, USA},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@book{RamirezSantamariaScharf-2023-CoherenceInSignalProcessingand,
title = {Coherence: In Signal Processing and Machine Learning},
author = {David Ram\'{i}rez and I. Santamar\'{i}a and L. L. Scharf},
doi = {10.1007/978-3-031-13331-2},
year  = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
publisher = {Springer Nature},
edition = {1st},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
@article{peis2023unsupervised,
title = {Unsupervised learning of global factors in deep generative models},
author = {Ignacio Peis and Pablo M Olmos and Antonio Art\'{e}s-Rodr\'{i}guez},
year  = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Pattern Recognition},
volume = {134},
pages = {109130},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{romero2023multi,
title = {Multi-Source Change-Point Detection over Local Observation Models},
author = {Lorena Romero-Medrano and Antonio Art\'{e}s-Rodr\'{i}guez},
year  = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Pattern Recognition},
volume = {134},
pages = {109116},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{guerrero2023automatic,
title = {Automatic antibiotic resistance prediction in Klebsiella pneumoniae based on MALDI-TOF mass spectra},
author = {Alejandro Guerrero-L\'{o}pez and Carlos Sevilla-Salcedo and Ana Candela and Marta Hern\'{a}ndez-Garc\'{i}a and Emilia Cercenado and Pablo M Olmos and Rafael Cant\'{o}n and Patricia Mu\~{n}oz and Vanessa G\'{o}mez-Verdejo and Rosa Campo},
year  = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Engineering Applications of Artificial Intelligence},
volume = {118},
pages = {105644},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{AGUILERA2023,
title = {Regularizing transformers with deep probabilistic layers},
author = {Aurora Cobo Aguilera and Pablo M Olmos and Antonio Art\'{e}s-Rodr\'{i}guez and Fernando P\'{e}rez-Cruz},
url = {https://www.sciencedirect.com/science/article/pii/S0893608023000448},
doi = {https://doi.org/10.1016/j.neunet.2023.01.032},
issn = {0893-6080},
year  = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Neural Networks},
abstract = {Language models (LM) have grown non-stop in the last decade, from sequence-to-sequence architectures to attention-based Transformers. However, regularization is not deeply studied in those structures. In this work, we use a Gaussian Mixture Variational Autoencoder (GMVAE) as a regularizer layer. We study its advantages regarding the depth where it is placed and prove its effectiveness in several scenarios. Experimental result demonstrates that the inclusion of deep generative models within Transformer-based architectures such as BERT, RoBERTa, or XLM-R can bring more versatile models, able to generalize better and achieve improved imputation score in tasks such as SST-2 and TREC or even impute missing/noisy words with richer text.},
keywords = {Deep learning, Missing data, Natural language processing, Regularization, Transformers, Variational auto-encoder},
pubstate = {published},
tppubtype = {article}
}
@article{SEDANOCAPDEVILA2023115090,
title = {Text mining methods for the characterisation of suicidal thoughts and behaviour},
author = {Alba Sedano-Capdevila and Mauricio Toledo-Acosta and Mar\'{i}a Luisa Barrigon and Eliseo Morales-Gonz\'{a}lez and David Torres-Moreno and Bol\'{i}var Mart\'{i}nez-Zaldivar and Jorge Hermosillo-Valadez and Enrique Baca-Garc\'{i}a and Fuensanta Aroca and Antonio Artes-Rodriguez and Enrique Baca-Garc\'{i}a and Sofian Berrouiguet and Romain Billot and Juan Jose Carballo-Belloso and Philippe Courtet and David Delgado Gomez and Jorge Lopez-Castroman and Mercedes Perez Rodriguez and Julia Aznar-Carbone and Fanny Cegla and Pedro Guti\'{e}rrez-Recacha and Leire Izaguirre-Gamir and Javier Herrera-Sanchez and Marta Migoya Borja and Nora Palomar-Ciria and Adela S\'{a}nchez-Escribano Mart\'{i}nez and Manuel Vasquez and Silvia Vallejo-O\~{n}ate and Constanza Vera-Varela and Susana Amodeo-Escribano and Elsa Arrua and Olga Bautista and Maria Luisa Barrig\'{o}n and Rodrigo Carmona and Irene Caro-Ca\~{n}izares and Sonia Carollo-Vivian and Jaime Chamorro and Marta Gonz\'{a}lez-Granado and Miren Iza and M\'{o}nica Jim\'{e}nez-Gim\'{e}nez and Ana L\'{o}pez-G\'{o}mez and Laura Mata-Iturralde and Carolina Miguelez and Laura Mu\~{n}oz-Lorenzo and Roc\'{i}o Navarro-Jim\'{e}nez and Santiago Ovejero and Mar\'{i}a Luz Palacios and Margarita P\'{e}rez-Fominaya and Inmaculada Pe\~{n}uelas-Calvo and Sonia P\'{e}rez-Colmenero and Ana Rico-Romano and Alba Rodriguez-Jover and Sergio S\'{a}nchezAlonso and Juncal Sevilla-Vicente and Carolina Vigil-L\'{o}pez and Luc\'{i}a Villoria-Borrego and Marisa Martin-Calvo and Ana Alc\'{o}n-Dur\'{a}n and Ezequiel Di Stasio and Juan Manuel Garc\'{i}a-Vega and Pedro Mart\'{i}n-Calvo and Ana Jos\'{e} Ortega and Marta Segura-Valverde and Sara Mar\'{i}a Ba\~{n}\'{o}n-Gonz\'{a}lez and Edurne Crespo-Llanos and Rosana Codesal-Juli\'{a}n and Ainara Frade-Ciudad and Elena Hernando Merino and Raquel \'{A}lvarez-Garc\'{i}a and Jose Marcos Coll-Font and Pablo Portillo-de Antonio and Pablo Puras-Rico and Alba Sedano-Capdevila and Leticia Serrano-Marug\'{a}n},
url = {https://www.sciencedirect.com/science/article/pii/S0165178123000434},
doi = {https://doi.org/10.1016/j.psychres.2023.115090},
issn = {0165-1781},
year  = {2023},
date = {2023-01-01},
journal = {Psychiatry Research},
volume = {322},
pages = {115090},
abstract = {Traditional research methods have shown low predictive value for suicidal risk assessments and limitations to be applied in clinical practice. The authors sought to evaluate natural language processing as a new tool for assessing self-injurious thoughts and behaviors and emotions related. We used MEmind project to assess 2838 psychiatric outpatients. Anonymous unstructured responses to the open-ended question “how are you feeling today?” were collected according to their emotional state. Natural language processing was used to process the patients' writings. The texts were automatically represented (corpus) and analyzed to determine their emotional content and degree of suicidal risk. Authors compared the patients' texts with a question used to assess lack of desire to live, as a suicidal risk assessment tool. Corpus consists of 5,489 short free-text documents containing 12,256 tokenized or unique words. The natural language processing showed an ROC-AUC score of 0.9638 when compared with the responses to lack of a desire to live question. Natural language processing shows encouraging results for classifying subjects according to their desire not to live as a measure of suicidal risk using patients’ free texts. It is also easily applicable to clinical practice and facilitates real-time communication with patients, allowing better intervention strategies to be designed.},
keywords = {Machine learning, Mobile health, Natural language processing, Suicidal ideation, Suicide, Suicide attempt},
pubstate = {published},
tppubtype = {article}
}
@article{bonilla2023multidimensional,
title = {Multidimensional variability in ecological assessments predicts two clusters of suicidal patients},
author = {Pablo Bonilla-Escribano and David Ram\'{i}rez and Enrique Baca-Garc\'{i}a and Philippe Courtet and Antonio Art\'{e}s-Rodr\'{i}guez and Jorge L\'{o}pez-Castrom\'{a}n},
year  = {2023},
date = {2023-01-01},
journal = {Scientific reports},
volume = {13},
number = {1},
pages = {3546},
publisher = {Nature Publishing Group UK London},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{SUKEI2023100657,
title = {Automatic patient functionality assessment from multimodal data using deep learning techniques \textendash Development and feasibility evaluation},
author = {Emese S\"{u}kei and Santiago Leon-Martinez and Pablo M Olmos and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {https://www.sciencedirect.com/science/article/pii/S221478292300057X},
doi = {https://doi.org/10.1016/j.invent.2023.100657},
issn = {2214-7829},
year  = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Internet Interventions},
volume = {33},
pages = {100657},
abstract = {Wearable devices and mobile sensors enable the real-time collection of an abundant source of physiological and behavioural data unobtrusively. Unlike traditional in-person evaluation or ecological momentary assessment (EMA) questionnaire-based approaches, these data sources open many possibilities in remote patient monitoring. However, defining robust models is challenging due to the data's noisy and frequently missing observations. This work proposes an attention-based Long Short-Term Memory (LSTM) neural network-based pipeline for predicting mobility impairment based on WHODAS 2.0 evaluation from such digital biomarkers. Furthermore, we addressed the missing observation problem by utilising hidden Markov models and the possibility of including information from unlabelled samples via transfer learning. We validated our approach using two wearable/mobile sensor data sets collected in the wild and socio-demographic information about the patients. Our results showed that in the WHODAS 2.0 mobility impairment prediction task, the proposed pipeline outperformed a prior baseline while additionally providing interpretability with attention heatmaps. Moreover, using a much smaller cohort via task transfer learning, the same model could learn to predict generalised anxiety severity accurately based on GAD-7 scores.},
keywords = {Attention models, Digital phenotyping, Ecological momentary assessment, In-situ patient monitoring, Time-series modelling, Transfer learning},
pubstate = {published},
tppubtype = {article}
}
@article{doi:10.1137/21M1431497,
title = {Polynomial Propagation of Moments in Stochastic Differential Equations},
author = {Alberto L\'{o}pez Yela and Joaqu\'{i}n M\'{i}guez},
url = {https://doi.org/10.1137/21M1431497},
doi = {10.1137/21M1431497},
year  = {2023},
date = {2023-01-01},
journal = {SIAM Journal on Applied Dynamical Systems},
volume = {22},
number = {2},
pages = {1153-1181},
abstract = {Abstract. We address the problem of approximating the moments of the solution, (boldsymbolX(t)) , of an It\^{o} stochastic differential equation (SDE) with drift and diffusion terms over a time grid (t_0, t_1, …, t_n) . In particular, we assume an explicit numerical scheme for the generation of sample paths (hatboldsymbolX(t_0), hatboldsymbolX(t_1), …, hatboldsymbolX(t_n), …) and then obtain recursive equations that yield any desired noncentral moment of (hatboldsymbolX(t_n)) as a function of the initial condition (hatboldsymbolX(t_0) = boldsymbolX_0) . The core of the methodology is the decomposition of the numerical solution (hatboldsymbolX(t_n)) into a “central part” and an “effective noise” term. The central term is computed deterministically from the ordinary differential equation (ODE) that results from eliminating the diffusion term in the SDE, while the effective noise accounts for the stochastic deviation from the numerical solution of the ODE. For simplicity, we describe the proposed methodology based on an Euler\textendashMaruyama integrator, but other explicit numerical schemes can be exploited in the same way. We also apply the moment approximations to construct estimates of the 1-dimensional marginal probability density functions of (hatboldsymbolX(t_n)) based on a Gram\textendashCharlier expansion. Both for the approximation of moments and 1-dimensional densities, we describe how to handle the cases in which the initial condition is fixed (i.e., (boldsymbolX_0 = boldsymbolx_0) for some deterministic and known (boldsymbolx_0) ) or random. In the latter case, we resort to polynomial chaos expansion (PCE) schemes in order to approximate the target moments. The methodology has been inspired by the PCE and differential algebra methods used for uncertainty propagation in astrodynamics problems. Hence, we illustrate its application for the quantification of uncertainty in a 2-dimensional Keplerian orbit perturbed by a Wiener noise process.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{cano2023catalog,
title = {Catalog-based atmosphere uncertainty quantification},
author = {Alejandro Cano and Alejandro Pastor and Joaqu\'{i}n M\'{i}guez and Manuel Sanjurjo-Rivo and Diego Escobar},
year  = {2023},
date = {2023-01-01},
journal = {The Journal of the Astronautical Sciences},
volume = {70},
number = {5},
pages = {42},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{cano2023covariance,
title = {Covariance determination for improving uncertainty realism in orbit determination and propagation},
author = {Alejandro Cano and Alejandro Pastor and Diego Escobar and Joaqu\'{i}n M\'{i}guez and Manuel Sanjurjo-Rivo},
year  = {2023},
date = {2023-01-01},
journal = {Advances in Space Research},
volume = {72},
number = {7},
pages = {2759\textendash2777},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10132455,
title = {Handling Ill-Conditioned Omics Data With Deep Probabilistic Models},
author = {Mar\'{i}a Mart\'{i}nez-Garc\'{i}a and Pablo M. Olmos},
doi = {10.1109/JBHI.2023.3279493},
year  = {2023},
date = {2023-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
volume = {27},
number = {9},
pages = {4601-4610},
keywords = {Data models;Biological system modeling;Probabilistic logic;Bayes methods;Bioinformatics;Mars;Feature extraction;Bayesian;classification;deep generative model;dimensionality reduction;latent space model;missing data;semi-supervised;VAE},
pubstate = {published},
tppubtype = {article}
}
@article{MORENOPINO2022109014,
title = {Deep Autoregressive Models with Spectral Attention},
author = {Fernando Moreno-Pino and Pablo M Olmos and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {https://www.sciencedirect.com/science/article/pii/S0031320322004940},
doi = {https://doi.org/10.1016/j.patcog.2022.109014},
issn = {0031-3203},
year  = {2023},
date = {2023-01-01},
urldate = {2022-01-01},
journal = {Pattern Recognition},
pages = {109014},
abstract = {Time series forecasting is an important problem across many domains, playing a crucial role in multiple real-world applications. In this paper, we propose a forecasting architecture that combines deep autoregressive models with a Spectral Attention (SA) module, which merges global and local frequency domain information in the model’s embedded space. By characterizing in the spectral domain the embedding of the time series as occurrences of a random process, our method can identify global trends and seasonality patterns. Two spectral attention models, global and local to the time series, integrate this information within the forecast and perform spectral filtering to remove time series’s noise. The proposed architecture has a number of useful properties: it can be effectively incorporated into well-known forecast architectures, requiring a low number of parameters and producing explainable results that improve forecasting accuracy. We test the Spectral Attention Autoregressive Model (SAAM) on several well-known forecast datasets, consistently demonstrating that our model compares favorably to state-of-the-art approaches.},
keywords = {Attention models, Deep learning, Filtering, global-local contexts, Signal processing, spectral domain attention, time series forecasting},
pubstate = {published},
tppubtype = {article}
}
@article{2022A\&A...660A..75P,
title = {X-ray variability of HD 189733 across eight years of XMM-Newton observations},
author = {I. Pillitteri and G Micela and A. Maggio and S Sciortino and J L\'{o}pez-Santiago},
doi = {10.1051/0004-6361/202142232},
year  = {2022},
date = {2022-04-01},
urldate = {2022-04-01},
journal = {Astronomy and Astrophysics},
volume = {660},
pages = {A75},
keywords = {Astrophysics - Earth and Planetary Astrophysics, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics, planetary systems, stars: activity, stars: flare, stars: individual: HD 189733, X-rays: stars},
pubstate = {published},
tppubtype = {article}
}
@article{9594658b,
title = {Medical Data Wrangling With Sequential Variational Autoencoders},
author = {Daniel Barrej\'{o}n and Pablo M Olmos and Antonio Art\'{e}s-Rodr\'{i}guez},
doi = {10.1109/JBHI.2021.3123839},
year  = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
volume = {26},
number = {6},
pages = {2737-2745},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{romero2022multinomial,
title = {Multinomial Sampling of Latent Variables for Hierarchical Change-Point Detection},
author = {Lorena Romero-Medrano and P Moreno-Mu\~{n}oz and Antonio Art\'{e}s-Rodr\'{i}guez},
year  = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Journal of Signal Processing Systems},
volume = {94},
number = {2},
pages = {215--227},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{PORRASSEGOVIA2022145,
title = {Real-world feasibility and acceptability of real-time suicide risk monitoring via smartphones: A 6-month follow-up cohort},
author = {Alejandro Porras-Segovia and Isaac D\'{i}az-Oliv\'{a}n and Maria Luisa Barrig\'{o}n and Manon Moreno and Antonio Art\'{e}s-Rodr\'{i}guez and Mercedes M Perez-Rodriguez and Enrique Baca-Garc\'{i}a},
url = {https://www.sciencedirect.com/science/article/pii/S0022395622001078},
doi = {https://doi.org/10.1016/j.jpsychires.2022.02.026},
issn = {0022-3956},
year  = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Journal of Psychiatric Research},
volume = {149},
pages = {145-154},
abstract = {Active and passive Ecological Momentary Assessment of suicide risk is crucial for suicide prevention. We aimed to assess the feasibility and acceptability of active and passive smartphone-based EMA in real-world conditions in patients at high risk for suicide. We followed 393 patients at high risk for suicide for six months using two mobile health applications: the MEmind (active) and the eB2 (passive). Retention with active EMA was 79.3% after 1 month and 22.6% after 6 months. Retention with passive EMA was 87.8% after 1 month and 46.6% after 6 months. Satisfaction with the MEmind app, uninstalling the eB2 app and diagnosis of eating disorders were independently associated with stopping active EMA. Satisfaction with the eB2 app and uninstalling the MEmind app were independently associated with stopping passive EMA. Smartphone-based active and passive EMA are feasible and may increase accessibility to mental healthcare.},
keywords = {Ecological momentary assessment, eHealth, Mhealth, Suicide, Suicide attempt, Suicide ideation},
pubstate = {published},
tppubtype = {article}
}
@article{Ravi-TIT2022,
title = {Scaling Laws for Gaussian Random Many-Access Channels},
author = {Jithin Ravi and Tobias Koch},
doi = {10.1109/TIT.2021.3139430},
year  = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {68},
number = {4},
pages = {2429-2459},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{XiaoRamirezSchreier-2022-One-bittargetdetectionincollocated,
title = {One-bit target detection in collocated MIMO Radar and performance degradation analysis},
author = {Y -H Xiao and David Ram\'{i}rez and Peter J Schreier and C. Qian and L. Huang},
doi = {10.1109/TVT.2022.3178285},
issn = {0018-9545},
year  = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {IEEE Trans. Vehicular Techn. (To appear)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{vazquez2022validation,
title = {Validation of scientific topic models using graph analysis and corpus metadata},
author = {Manuel A V\'{a}zquez and Jorge Pereira-Delgado and J Cid-Sueiro and Jer\'{o}nimo Arenas-Garc\'{i}a},
year  = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Scientometrics},
pages = {1--18},
publisher = {Springer},
keywords = {yo},
pubstate = {published},
tppubtype = {article}
}
@article{9805755,
title = {Coded Caching with Heterogeneous User Profiles},
author = {Ciyuan Zhang and Su Wang and Vaneet Aggarwal and Borja Peleato},
doi = {10.1109/TIT.2022.3186210},
year  = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {IEEE Transactions on Information Theory},
pages = {1-1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{9791424,
title = {Coded Caching with Full Heterogeneity: Exact Capacity of The Two-User/Two-File Case},
author = {Chih-Hua Chang and Borja Peleato and Chih-Chun Wang},
doi = {10.1109/TIT.2022.3181411},
year  = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {IEEE Transactions on Information Theory},
pages = {1-1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{PANTOJAROSERO2022104430,
title = {Generating LOD3 building models from structure-from-motion and semantic segmentation},
author = {B. G. Pantoja-Rosero and R. Achanta and M. Kozinski and P. Fua and Fernando Perez-Cruz and K. Beyer},
url = {https://www.sciencedirect.com/science/article/pii/S092658052200303X},
doi = {https://doi.org/10.1016/j.autcon.2022.104430},
issn = {0926-5805},
year  = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Automation in Construction},
volume = {141},
pages = {104430},
abstract = {This paper describes a pipeline for automatically generating level of detail (LOD) models (digital twins), specifically LOD2 and LOD3, from free-standing buildings. Our approach combines structure from motion (SfM) with deep-learning-based segmentation techniques. Given multiple-view images of a building, we compute a three-dimensional (3D) planar abstraction (LOD2 model) of its point cloud using SfM techniques. To obtain LOD3 models, we use deep learning to perform semantic segmentation of the openings in the two-dimensional (2D) images. Unlike existing approaches, we do not rely on complex input, pre-defined 3D shapes or manual intervention. To demonstrate the robustness of our method, we show that it can generate 3D building shapes from a collection of building images with no further input. For evaluating reconstructions, we also propose two novel metrics. The first is a Euclidean\textendashdistance-based correlation of the 3D building model with the point cloud. The second involves re-projecting 3D model facades onto source photos to determine dice scores with respect to the ground-truth masks. Finally, we make the code, the image datasets, SfM outputs, and digital twins reported in this work publicly available in github.com/eesd-epfl/LOD3_buildings and doi.org/10.5281/zenodo.6651663. With this work we aim to contribute research in applications such as construction management, city planning, and mechanical analysis, among others.},
keywords = {3D building models, Deep learning, Digital twin, LOD models, Masonry buildings, Structure from motion},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{9965931,
title = {Second-Order Asymptotics of Hoeffding-Like Hypothesis Tests},
author = {K. V. Harsha and Jithin Ravi and Tobias Koch},
doi = {10.1109/ITW54588.2022.9965931},
year  = {2022},
date = {2022-01-01},
booktitle = {2022 IEEE Information Theory Workshop (ITW)},
pages = {654-659},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{PANTOJAROSERO2022128264,
title = {TOPO-Loss for continuity-preserving crack detection using deep learning},
author = {B. G. Pantoja-Rosero and D. Oner and M. Kozinski and R. Achanta and P. Fua and Fernando Perez-Cruz and K. Beyer},
url = {https://www.sciencedirect.com/science/article/pii/S0950061822019250},
doi = {https://doi.org/10.1016/j.conbuildmat.2022.128264},
issn = {0950-0618},
year  = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Construction and Building Materials},
volume = {344},
pages = {128264},
abstract = {We present a method for segmenting cracks in images of masonry buildings damaged by earthquakes. Existing methods of crack detection fail to preserve the continuity of cracks, and their performance deteriorates with imprecise training labels. We address these problems by adapting an approach previously proposed for reconstructing roads in aerial images, in which a Convolutional Neural Network is trained with a loss function specifically designed to encourage the continuity of thin structures and to accommodate imprecise annotations. We evaluate combinations of three loss functions (the Mean Squared Error, the Dice loss and the new connectivity-oriented loss) on two datasets using TernausNet, a deep network shown to attain state-of-the-art accuracy in crack detection. We herein show that combining these three losses significantly improves the topology of the predictions quantitatively and qualitatively. We also propose a new continuity metric, named Cracks Per Patch (CPP), and share a new dataset of images of earthquake-affected urban scenes accompanied by crack annotations. The dataset and implementations are publicly available for future studies and benchmarking (https://github.com/eesd-epfl/topo_crack_detection and https://doi.org/10.5281/zenodo.6769028).},
keywords = {Crack detection, Deep learning, Masonry buildings, Post-earthquake assessment},
pubstate = {published},
tppubtype = {article}
}
@article{10.1182/blood-2022-168461,
title = {Identification of Predictive Models Including Polymorphisms in Cytokines Genes Associated with Post-Transplant Complications after Identical HLA-Allogeneic Stem Cell Transplantation},
author = {Paula Mu\~{n}iz Sevilla and Mar\'{i}a Mart\'{i}nez-Garc\'{i}a and Mi Kwon and Rebeca Bail\'{e}n and Gillen Oarbeascoa and Diego Carbonell and Julia Su\'{a}rez Gonz\'{a}lez and Mar\'{i}a Chicano Lavilla and Cristina Andres and Juan Carlos Trivi\~{n}o and Javier Anguita and Jos\'{e} Luis D\'{i}ez-Mart\'{i}n and Pablo M Olmos and Carolina Martinez-Laperche and Ismael Bu\~{n}o},
url = {https://doi.org/10.1182/blood-2022-168461},
doi = {10.1182/blood-2022-168461},
issn = {0006-4971},
year  = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Blood},
volume = {140},
number = {Supplement 1},
pages = {4795-4796},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{sevilla2022bayesian,
title = {Bayesian sparse factor analysis with kernelized observations},
author = {Carlos Sevilla-Salcedo and Alejandro Guerrero-L\'{o}pez and Pablo M Olmos and Vanessa G\'{o}mez-Verdejo},
year  = {2022},
date = {2022-01-01},
journal = {Neurocomputing},
volume = {490},
pages = {66--78},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nhess-22-2031-2022,
title = {Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland},
author = {C. P\'{e}rez-Guill\'{e}n and Frank Techel and M. Hendrick and Michele Volpi and A. Herwijnen and T. Olevski and G. Obozinski and Fernando Perez-Cruz and J. Schweizer},
url = {https://nhess.copernicus.org/articles/22/2031/2022/},
doi = {10.5194/nhess-22-2031-2022},
year  = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Natural Hazards and Earth System Sciences},
volume = {22},
number = {6},
pages = {2031--2056},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{DBLP:journals/sac/ZhangWP22,
title = {Accelerated parallel non-conjugate sampling for Bayesian non-parametric  models},
author = {Michael Minyi Zhang and Sinead A. Williamson and Fernando Perez-Cruz},
year  = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Stat. Comput.},
volume = {32},
number = {3},
pages = {50},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{porras2022six,
title = {Six-month clinical and ecological momentary assessment follow-up of patients at high risk of suicide: a survival analysis},
author = {Alejandro Porras-Segovia and Manon Moreno and Mar\'{i}a Luisa Barrig\'{o}n and Jorge L\'{o}pez Castroman and Philippe Courtet and Sofian Berrouiguet and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a},
year  = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {The Journal of Clinical Psychiatry},
volume = {84},
number = {1},
pages = {44594},
publisher = {Physicians Postgraduate Press, Inc.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{barrigon2022smartphone,
title = {Smartphone-based Ecological Momentary Intervention for secondary prevention of suicidal thoughts and behaviour: protocol for the SmartCrisis V. 2.0 randomised clinical trial},
author = {Maria Luisa Barrigon and Alejandro Porras-Segovia and Philippe Courtet and Jorge Lopez-Castroman and Sofian Berrouiguet and Mar\'{i}a-Mercedes P\'{e}rez-Rodr\'{i}guez and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garcia},
year  = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {BMJ open},
volume = {12},
number = {9},
pages = {e051807},
publisher = {British Medical Journal Publishing Group},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{cano2022improving,
title = {Improving Orbital Uncertainty Realism Through Covariance Determination in GEO},
author = {Alejandro Cano and Alejandro Pastor and Sergio Fern\'{a}ndez and Joaqu\'{i}n M\'{i}guez and Manuel Sanjurjo-Rivo and Diego Escobar},
year  = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {The Journal of the Astronautical Sciences},
volume = {69},
number = {5},
pages = {1394--1420},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{9764678,
title = {Generalized Perfect Codes for Symmetric Classical-Quantum Channels},
author = {Andreu Blasco Coll and Gonzalo Vazquez-Vilar and Javier Rodr\'{i}guez Fonollosa},
doi = {10.1109/TIT.2022.3170868},
year  = {2022},
date = {2022-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {68},
number = {9},
pages = {5923-5936},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{deLeon-Martineze058486,
title = {Virtual reality and speech analysis for the assessment of impulsivity and decision-making: protocol for a comparison with neuropsychological tasks and self-administered questionnaires},
author = {Santiago de Le\'{o}n and Marta Ruiz and Elena Parra-Vargas and Irene Chicchi-Giglioli and Philippe Courtet and Jorge L\'{o}pez-Castrom\'{a}n and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garcia and Alejandro Porras-Segovia and Maria Luisa Barrigon},
url = {https://bmjopen.bmj.com/content/12/7/e058486},
doi = {10.1136/bmjopen-2021-058486},
issn = {2044-6055},
year  = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {BMJ Open},
volume = {12},
number = {7},
publisher = {British Medical Journal Publishing Group},
abstract = {Introduction Impulsivity is present in a range of mental disorders and has been associated with suicide. Traditional measures of impulsivity have certain limitations, such as the lack of ecological validity. Virtual reality (VR) may overcome these issues. This study aims to validate the VR assessment tool ‘Spheres \& Shield Maze Task’ and speech analysis by comparing them with traditional measures. We hypothesise that these innovative tools will be reliable and acceptable by patients, potentially improving the simultaneous assessment of impulsivity and decision-making.Methods and analysis This study will be carried out at the University Hospital Fundaci\'{o}n Jim\'{e}nez D'iaz (Madrid, Spain). Our sample will consist of adults divided into three groups: psychiatric outpatients with a history of suicidal thoughts and/or behaviours, psychiatric outpatients without such a history and healthy volunteers. The target sample size was established at 300 participants (100 per group). Participants will complete the Barratt Impulsiveness Scale 11; the Urgency, Premeditation, Perseverance, Sensation Seeking, Positive Urgency, Impulsive Behaviour Scale; Iowa Gambling Task; Continuous Performance Test; Stop signal Task, and Go/no-go task, three questions of emotional affect, the Spheres \& Shield Maze Task and two satisfaction surveys. During these tasks, participant speech will be recorded. Construct validity of the VR environment will be calculated. We will also explore the association between VR-assessed impulsivity and history of suicidal thoughts and/or behaviour, and the association between speech and impulsivity and decision-making.Ethics and dissemination This study was approved by the Ethics Committee of the University Hospital Fundaci\'{o}n Jim\'{e}nez D'iaz (PIC128-21_FJD). Participants will be required to provide written informed consent. The findings will be presented in a series of manuscripts that will be submitted to peer-reviewed journals for publication.Trial registration number NCT05109845; Pre-results.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{9771953,
title = {Deep Reinforcement Learning for Random Access in Machine-Type Communication},
author = {Muhammad Awais Jadoon and Adriano Pastore and Monica Navarro and Fernando Perez-Cruz},
doi = {10.1109/WCNC51071.2022.9771953},
year  = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {2022 IEEE Wireless Communications and Networking Conference (WCNC)},
pages = {2553-2558},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{<LineBreak>klimovskaia2022signal,
title = {Signal Domain Learning Approach for Optoacoustic Image Reconstruction from Limited View Data},
author = {Anna Klimovskaia and Berkan Lafci and Firat Ozdemir and Neda Davoudi and Xose Luis Dean-Ben and Fernando Perez-Cruz and Daniel Razansky},
url = {https://openreview.net/forum?id=9NOyrfUBtx1},
year  = {2022},
date = {2022-01-01},
booktitle = {Medical Imaging with Deep Learning},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{PMID:35936756,
title = {Smartphone-Based Ecological Momentary Assessment for the Measurement of the Performance Status and Health-Related Quality of Life in Cancer Patients Under Systemic Anticancer Therapies: Development and Acceptability of a Mobile App},
author = {Vicente Escudero-Vilaplana and Lorena Romero-Medrano and Cristina Villanueva-Bueno and Marta Rodr\'{i}guez de Diago and Alberto Y\'{a}nez-Montesdeoca and Roberto Collado-Borrell and Juan Jos\'{e} Campa\~{n}a-Montes and Bel\'{e}n Marzal-Alfaro and Jos\'{e} Luis Revuelta-Herrero and Antonio Calles and Mar Galera and Rosa \'{A}lvarez and Ana Herranz and Mar\'{i}a Sanjurjo and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {https://europepmc.org/articles/PMC9351705},
doi = {10.3389/fonc.2022.880430},
issn = {2234-943X},
year  = {2022},
date = {2022-01-01},
journal = {Frontiers in oncology},
volume = {12},
pages = {880430},
abstract = {\&lt;h4\&gt;Background\&lt;/h4\&gt;We have defined a project to develop a mobile app that continually records smartphone parameters which may help define the Eastern Cooperative Oncology Group performance status (ECOG-PS) and the health-related quality of life (HRQoL), without interaction with patients or professionals. This project is divided into 3 phases. Here we describe phase 1. The objective of this phase was to develop the app and assess its usability concerning patient characteristics, acceptability, and satisfaction.\&lt;h4\&gt;Methods\&lt;/h4\&gt;The app eB2-ECOG was developed and installed in the smartphone of cancer patients who will be followed for six months. Criteria inclusion were: age over 18-year-old; diagnosed with unresectable or metastatic lung cancer, gastrointestinal stromal tumor, sarcoma, or head and neck cancer; under systemic anticancer therapies; and possession of a Smartphone. The app will collect passive and active data from the patients while healthcare professionals will evaluate the ECOG-PS and HRQoL through conventional tools. Acceptability was assessed during the follow-up. Patients answered a satisfaction survey in the app between 3-6 months from their inclusion.\&lt;h4\&gt;Results\&lt;/h4\&gt;The app developed provides a system for continuously collecting, merging, and processing data related to patient's health and physical activity. It provides a transparent capture service based on all the available data of a patient. Currently, 106 patients have been recruited. A total of 36 patients were excluded, most of them (21/36) due to technological reasons. We assessed 69 patients (53 lung cancer, 8 gastrointestinal stromal tumors, 5 sarcomas, and 3 head and neck cancer). Concerning app satisfaction, 70.4% (20/27) of patients found the app intuitive and easy to use, and 51.9% (17/27) of them said that the app helped them to improve and handle their problems better. Overall, 17 out of 27 patients [62.9%] were satisfied with the app, and 14 of them [51.8%] would recommend the app to other patients.\&lt;h4\&gt;Conclusions\&lt;/h4\&gt;We observed that the app's acceptability and satisfaction were good, which is essential for the continuity of the project. In the subsequent phases, we will develop predictive models based on the collected information during this phase. We will validate the method and analyze the sensitivity of the automated results.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{CANO2022,
title = {Covariance determination for improving uncertainty realism in orbit determination and propagation},
author = {Alejandro Cano and Alejandro Pastor and Diego Escobar and Joaqu\'{i}n M\'{i}guez and Manuel Sanjurjo-Rivo},
url = {https://www.sciencedirect.com/science/article/pii/S0273117722007190},
doi = {https://doi.org/10.1016/j.asr.2022.08.001},
issn = {0273-1177},
year  = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Advances in Space Research},
abstract = {The reliability of the uncertainty characterization, also known as uncertainty realism, is of the uttermost importance for Space Situational Awareness (SSA) services. Among the many sources of uncertainty in the space environment, the most relevant one is the inherent uncertainty of the dynamic models, which is generally not considered in the batch least-squares Orbit Determination (OD) processes in operational scenarios. A classical approach to account for these sources of uncertainty is the theory of consider parameters. In this approach, a set of uncertain parameters are included in the underlying dynamical model, in such a way that the model uncertainty is represented by the variances of these parameters. However, realistic variances of these consider parameters are not known a priori. This work introduces a methodology to infer the variance of consider parameters based on the observed distribution of the Mahalanobis distance of the orbital differences between predicted and estimated orbits, which theoretically should follow a chi-square distribution under Gaussian assumptions. Empirical Distribution Function statistics such as the Cramer-von-Mises and the Kolmogorov\textendashSmirnov distances are used to determine optimum consider parameter variances. The methodology is presented in this paper and validated in a series of simulated scenarios emulating the complexity of operational applications.},
keywords = {Chi-square distribution, Covariance determination, Covariance realism, Cramer-von-Mises, Kolmogorov\textendashSmirnov, Mahalanobis distance, Space situational awareness, Uncertainty realism},
pubstate = {published},
tppubtype = {article}
}
@article{Llorente2022OnTS,
title = {On the safe use of prior densities for Bayesian model selection},
author = {Fernando Llorente and Luca Martino and E. Curbelo and J L\'{o}pez-Santiago and David Delgado},
year  = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {WIREs Computational Statistics},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{PerezViaVielva-2022-OnlinedetectionandSNRestimation,
title = {Online detection and SNR estimation in cooperative spectrum sensing},
author = {J. P\'{e}rez and Javier V\'{i}a and L. Vielva and David Ram\'{i}rez},
doi = {10.1109/TWC.2021.3113089},
issn = {1536-1276},
year  = {2022},
date = {2022-00-01},
urldate = {2022-00-01},
journal = {IEEE Trans. Wireless Comm.},
volume = {21},
number = {4},
pages = {2521--2533},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Ramirez,
title = {Graph-signal reconstruction and blind deconvolution for structured inputs},
author = {David Ram\'{i}rez and Antonio G Marques and Santiago Segarra},
doi = {10.1016/j.sigpro.2021.108180},
year  = {2021},
date = {2021-11-01},
urldate = {2021-11-01},
journal = {Signal Process. (Special issue on Processing and Learning over Graphs)},
volume = {188},
pages = {108180},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{AArtes20g,
title = {Multinomial Sampling for hierarchical Change-Point Detection},
author = {Lorena Romero-Medrano and P Moreno-Mu\~{n}oz and Antonio Art\'{e}s-Rodr\'{i}guez},
year  = {2021},
date = {2021-10-08},
urldate = {2020-09-21},
booktitle = {Journal of Signal Processing Systems},
keywords = {Bayesian inference, change-point detection (CPD), latent variable models, multinomial likelihoods},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Martino_2021,
title = {Compressed Particle Methods for Expensive Models With Application in Astronomy and Remote Sensing},
author = {Luca Martino and Victor Elvira and J L\'{o}pez-Santiago and Gustau Camps-Valls},
url = {https://doi.org/10.1109%2Ftaes.2021.3061791},
doi = {10.1109/taes.2021.3061791},
year  = {2021},
date = {2021-10-01},
urldate = {2021-10-01},
journal = {IEEE Transactions on Aerospace and Electronic Systems},
volume = {57},
number = {5},
pages = {2607-2621},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{info:doi/10.2196/30833,
title = {Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning--Based Ecological Momentary Assessment Study},
author = {J. Ryu and Emese S\"{u}kei and Agnes Norbury and H. Liu, S. and Juan Jos\'{e} Campa\~{n}a-Montes and Enrique Baca-Garc\'{i}a and Antonio Art\'{e}s-Rodr\'{i}guez and Mercedes M Perez-Rodriguez},
url = {http://www.ncbi.nlm.nih.gov/pubmed/34524091},
doi = {10.2196/30833},
issn = {2368-7959},
year  = {2021},
date = {2021-09-15},
urldate = {2021-09-15},
journal = {JMIR Ment Health},
volume = {8},
number = {9},
pages = {e30833},
abstract = {Background: Anxiety symptoms during public health crises are associated with adverse psychiatric outcomes and impaired health decision-making. The interaction between real-time social media use patterns and clinical anxiety during infectious disease outbreaks is underexplored. Objective: We aimed to evaluate the usage pattern of 2 types of social media apps (communication and social networking) among patients in outpatient psychiatric treatment during the COVID-19 surge and lockdown in Madrid, Spain and their short-term anxiety symptoms (7-item General Anxiety Disorder scale) at clinical follow-up. Methods: The individual-level shifts in median social media usage behavior from February 1 through May 3, 2020 were summarized using repeated measures analysis of variance that accounted for the fixed effects of the lockdown (prelockdown versus postlockdown), group (clinical anxiety group versus nonclinical anxiety group), the interaction of lockdown and group, and random effects of users. A machine learning--based approach that combined a hidden Markov model and logistic regression was applied to predict clinical anxiety (n=44) and nonclinical anxiety (n=51), based on longitudinal time-series data that comprised communication and social networking app usage (in seconds) as well as anxiety-associated clinical survey variables, including the presence of an essential worker in the household, worries about life instability, changes in social interaction frequency during the lockdown, cohabitation status, and health status. Results: Individual-level analysis of daily social media usage showed that the increase in communication app usage from prelockdown to lockdown period was significantly smaller in the clinical anxiety group than that in the nonclinical anxiety group (F},
keywords = {\"{a}nxiety disorder; COVID-19; social media; public health; digital phenotype; ecological momentary assessment; smartphone; machine learning; hidden Markov model\&quot;},
pubstate = {published},
tppubtype = {article}
}
@article{info:doi/10.2196/26548,
title = {Use of Ecological Momentary Assessment Through a Passive Smartphone-Based App (eB2) by Patients With Schizophrenia: Acceptability Study},
author = {Javier-David Lopez-Morinigo and Mar\'{i}a Luisa Barrig\'{o}n and Alejandro Porras-Segovia and Ver\'{o}nica Gonz\'{a}lez Ruiz-Ruano and Adela S\'{a}nchez Escribano Mart\'{i}nez and P. -J. Escobedo-Aedo and S. S\'{a}nchez-Alonso and L. Mata-Iturralde and Laura Mu\~{n}oz Lorenzo and Antonio Art\'{e}s-Rodr\'{i}guez and Anthony S David and Enrique Baca-Garc\'{i}a},
url = {http://www.ncbi.nlm.nih.gov/pubmed/34309576},
doi = {10.2196/26548},
issn = {1438-8871},
year  = {2021},
date = {2021-07-26},
urldate = {2021-07-26},
journal = {J Med Internet Res},
volume = {23},
number = {7},
pages = {e26548},
abstract = {Background: Ecological momentary assessment (EMA) tools appear to be useful interventions for collecting real-time data on patients' behavior and functioning. However, concerns have been voiced regarding the acceptability of EMA among patients with schizophrenia and the factors influencing EMA acceptability. Objective: The aim of this study was to investigate the acceptability of a passive smartphone-based EMA app, evidence-based behavior (eB2), among patients with schizophrenia spectrum disorders and the putative variables underlying their acceptance. Methods: The participants in this study were from an ongoing randomized controlled trial (RCT) of metacognitive training, consisting of outpatients with schizophrenia spectrum disorders (F20-29 of 10th revision of the International Statistical Classification of Diseases and Related Health Problems), aged 18-64 years, none of whom received any financial compensation. Those who consented to installation of the eB2 app (users) were compared with those who did not (nonusers) in sociodemographic, clinical, premorbid adjustment, neurocognitive, psychopathological, insight, and metacognitive variables. A multivariable binary logistic regression tested the influence of the above (independent) variables on ``being user versus nonuser'' (acceptability), which was the main outcome measure. Results: Out of the 77 RCT participants, 24 (31%) consented to installing eB2, which remained installed till the end of the study (median follow-up 14.50 weeks) in 14 participants (70%). Users were younger and had a higher education level, better premorbid adjustment, better executive function (according to the Trail Making Test), and higher cognitive insight levels (measured with the Beck Cognitive Insight Scale) than nonusers (univariate analyses) although only age (OR 0.93, 95% CI 0.86-0.99; P=.048) and early adolescence premorbid adjustment (OR 0.75, 95% CI 0.61-0.93; P=.01) survived the multivariable regression model, thus predicting eB2 acceptability. Conclusions: Acceptability of a passive smartphone-based EMA app among participants with schizophrenia spectrum disorders in this RCT where no participant received financial compensation was, as expected, relatively low, and linked with being young and good premorbid adjustment. Further research should examine how to increase EMA acceptability in patients with schizophrenia spectrum disorders, in particular, older participants and those with poor premorbid adjustment. Trial Registration: ClinicalTrials.gov NCT04104347; https://clinicaltrials.gov/ct2/show/NCT04104347},
keywords = {ecological momentary assessment; acceptability; schizophrenia spectrum disorders; eB2; digital tools; mental health; schizophrenia; real-time data; patients; digital health; internet; mobile apps},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{GargRamirez,
title = {Sparse subspace averaging for order estimation},
author = {V. Garg and David Ram\'{i}rez and Ignacio Santamar\'{i}a},
doi = {10.1109/SSP49050.2021.9513773},
year  = {2021},
date = {2021-07-01},
urldate = {2021-07-01},
booktitle = {Proc. IEEE Work. Stat. Signal Process.},
address = {Rio de Janeiro, Brazil},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{2021EGUGA..23.6154P,
title = {Data-driven automatic predictions of avalanche danger in Switzerland},
author = {C. P\'{e}rez-Guill\'{e}n and M. Hendrick and Frank Techel and A. Herwijnen and Michele Volpi and Olevski Tasko and Fernando Perez-Cruz and G. Obozinski and J. Schweizer},
doi = {10.5194/egusphere-egu21-6154},
year  = {2021},
date = {2021-04-01},
urldate = {2021-04-01},
booktitle = {EGU General Assembly Conference Abstracts},
pages = {EGU21-6154},
series = {EGU General Assembly Conference Abstracts},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{info:doi/10.2196/24465,
title = {Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach},
author = {Emese S\"{u}kei and Agnes Norbury and Mercedes M Perez-Rodriguez and Pablo M Olmos and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.ncbi.nlm.nih.gov/pubmed/33749612},
doi = {10.2196/24465},
issn = {2291-5222},
year  = {2021},
date = {2021-03-22},
journal = {JMIR Mhealth Uhealth},
volume = {9},
number = {3},
pages = {e24465},
abstract = {Background: Mental health disorders affect multiple aspects of patients' lives, including mood, cognition, and behavior. eHealth and mobile health (mHealth) technologies enable rich sets of information to be collected noninvasively, representing a promising opportunity to construct behavioral markers of mental health. Combining such data with self-reported information about psychological symptoms may provide a more comprehensive and contextualized view of a patient's mental state than questionnaire data alone. However, mobile sensed data are usually noisy and incomplete, with significant amounts of missing observations. Therefore, recognizing the clinical potential of mHealth tools depends critically on developing methods to cope with such data issues. Objective: This study aims to present a machine learning--based approach for emotional state prediction that uses passively collected data from mobile phones and wearable devices and self-reported emotions. The proposed methods must cope with high-dimensional and heterogeneous time-series data with a large percentage of missing observations. Methods: Passively sensed behavior and self-reported emotional state data from a cohort of 943 individuals (outpatients recruited from community clinics) were available for analysis. All patients had at least 30 days' worth of naturally occurring behavior observations, including information about physical activity, geolocation, sleep, and smartphone app use. These regularly sampled but frequently missing and heterogeneous time series were analyzed with the following probabilistic latent variable models for data averaging and feature extraction: mixture model (MM) and hidden Markov model (HMM). The extracted features were then combined with a classifier to predict emotional state. A variety of classical machine learning methods and recurrent neural networks were compared. Finally, a personalized Bayesian model was proposed to improve performance by considering the individual differences in the data and applying a different classifier bias term for each patient. Results: Probabilistic generative models proved to be good preprocessing and feature extractor tools for data with large percentages of missing observations. Models that took into account the posterior probabilities of the MM and HMM latent states outperformed those that did not by more than 20%, suggesting that the underlying behavioral patterns identified were meaningful for individuals' overall emotional state. The best performing generalized models achieved a 0.81 area under the curve of the receiver operating characteristic and 0.71 area under the precision-recall curve when predicting self-reported emotional valence from behavior in held-out test data. Moreover, the proposed personalized models demonstrated that accounting for individual differences through a simple hierarchical model can substantially improve emotional state prediction performance without relying on previous days' data. Conclusions: These findings demonstrate the feasibility of designing machine learning models for predicting emotional states from mobile sensing data capable of dealing with heterogeneous data with large numbers of missing observations. Such models may represent valuable tools for clinicians to monitor patients' mood states.},
keywords = {mental health; affect; mobile health; mobile phone; digital phenotype; machine learning; Bayesian analysis; probabilistic models; personalized models},
pubstate = {published},
tppubtype = {article}
}
@article{info:doi/10.2196/17116,
title = {Psychiatric Profiles of eHealth Users Evaluated Using Data Mining Techniques: Cohort Study},
author = {Jorge Lopez-Castroman and Diana Abad-Tortosa and Aurora Cobo Aguilera and Philippe Courtet and Maria Luisa Barrig\'{o}n and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a},
url = {http://www.ncbi.nlm.nih.gov/pubmed/33470943},
doi = {10.2196/17116},
issn = {2368-7959},
year  = {2021},
date = {2021-01-20},
journal = {JMIR Ment Health},
volume = {8},
number = {1},
pages = {e17116},
abstract = {Background: New technologies are changing access to medical records and the relationship between physicians and patients. Professionals can now use e-mental health tools to provide prompt and personalized responses to patients with mental illness. However, there is a lack of knowledge about the digital phenotypes of patients who use e-mental health apps. Objective: This study aimed to reveal the profiles of users of a mental health app through machine learning techniques. Methods: We applied a nonparametric model, the Sparse Poisson Factorization Model, to discover latent features in the response patterns of 2254 psychiatric outpatients to a short self-assessment on general health. The assessment was completed through a mental health app after the first login. Results: The results showed the following four different profiles of patients: (1) all patients had feelings of worthlessness, aggressiveness, and suicidal ideas; (2) one in four reported low energy and difficulties to cope with problems; (3) less than a quarter described depressive symptoms with extremely high scores in suicidal thoughts and aggressiveness; and (4) a small number, possibly with the most severe conditions, reported a combination of all these features. Conclusions: User profiles did not overlap with clinician-made diagnoses. Since each profile seems to be associated with a different level of severity, the profiles could be useful for the prediction of behavioral risks among users of e-mental health apps.},
keywords = {mental disorders; suicide prevention; suicidal ideation; data mining; digital phenotyping},
pubstate = {published},
tppubtype = {article}
}
@article{Bonilla-EscribanoRamirezPorras-Segovia-2021,
title = {Assessment of variability in irregularly sampled time series: Applications to mental healthcare},
author = {P Bonilla-Escribano and David Ram\'{i}rez and Alejandro Porras-Segovia and Antonio Art\'{e}s-Rodr\'{i}guez},
doi = {10.3390/math9010071},
issn = {2227-7390},
year  = {2021},
date = {2021-01-01},
journal = {Mathematics (Special issue on Recent Advances in {D}ata Science)},
volume = {9},
number = {1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{akyildiz2021convergence,
title = {Convergence rates for optimised adaptive importance samplers},
author = {O. D. Akyildiz and Joaqu\'{i}n M\'{i}guez},
year  = {2021},
date = {2021-01-01},
journal = {Statistics and Computing},
volume = {31},
number = {2},
pages = {1--17},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{PORRASSEGOVIA2021,
title = {Disturbed sleep as a clinical marker of wish to die: A smartphone monitoring study over three months of observation},
author = {Alejandro Porras-Segovia and Aurora Cobo and Isaac D\'{i}az-Oliv\'{a}n and Antonio Art\'{e}s-Rodr\'{i}guez and Sofian Berrouiguet and Jorge Lopez-Castroman and Philippe Courtet and Maria Luisa Barrig\'{o}n and Mar\'{i}a A Oquendo and Enrique Baca-Garc\'{i}a},
url = {https://www.sciencedirect.com/science/article/pii/S0165032721001932},
doi = {https://doi.org/10.1016/j.jad.2021.02.059},
issn = {0165-0327},
year  = {2021},
date = {2021-01-01},
journal = {Journal of Affective Disorders},
abstract = {Background 
: Smartphone monitoring could contribute to the elucidation of the correlates of suicidal thoughts and behaviors (STB). In this study, we employ smartphone monitoring and machine learning techniques to explore the association of wish to die (passive suicidal ideation) with disturbed sleep, altered appetite and negative feelings. 
Methods 
: This is a prospective cohort study carried out among adult psychiatric outpatients with a history of STB. A daily questionnaire was administered through the MEmind smartphone application. Participants were followed-up for a median of 89.8 days, resulting in 9,878 person-days. Data analysis employed a machine learning technique called Indian Buffet Process. 
Results 
: 165 patients were recruited, 139 had the MEmind mobile application installed on their smartphone, and 110 answered questions regularly enough to be included in the final analysis. We found that the combination of wish to die and sleep problems was one of the most relevant latent features found across the sample, showing that these variables tend to be present during the same time frame (96 hours). 
Conclusions 
: Disturbed sleep emerges as a potential clinical marker for passive suicidal ideation. Our findings stress the importance of evaluating sleep as part of the screening for suicidal behavior. Compared to previous smartphone monitoring studies on suicidal behavior, this study includes a long follow-up period and a large sample.},
keywords = {Mhealth, Sleep, Smartphone, Suicide, Suicide attempt, Suicide ideation},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{9287359,
title = {Kalman-based nested hybrid filters for recursive inference in state-space models},
author = {Sara P\'{e}rez-Vieites and Joaqu\'{i}n M\'{i}guez},
doi = {10.23919/Eusipco47968.2020.9287359},
year  = {2021},
date = {2021-01-01},
booktitle = {2020 28th European Signal Processing Conference (EUSIPCO)},
pages = {2468-2472},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{math9070784,
title = {Automatic Tempered Posterior Distributions for Bayesian Inversion Problems},
author = {Luca Martino and Fernando Llorente and E. Curbelo and J L\'{o}pez-Santiago and Joaqu\'{i}n M\'{i}guez},
url = {https://www.mdpi.com/2227-7390/9/7/784},
doi = {10.3390/math9070784},
issn = {2227-7390},
year  = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Mathematics},
volume = {9},
number = {7},
abstract = {We propose a novel adaptive importance sampling scheme for Bayesian inversion problems where the inference of the variables of interest and the power of the data noise are carried out using distinct (but interacting) methods. More specifically, we consider a Bayesian analysis for the variables of interest (i.e., the parameters of the model to invert), whereas we employ a maximum likelihood approach for the estimation of the noise power. The whole technique is implemented by means of an iterative procedure with alternating sampling and optimization steps. Moreover, the noise power is also used as a tempered parameter for the posterior distribution of the the variables of interest. Therefore, a sequence of tempered posterior densities is generated, where the tempered parameter is automatically selected according to the current estimate of the noise power. A complete Bayesian study over the model parameters and the scale parameter can also be performed. Numerical experiments show the benefits of the proposed approach.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.1093/mnras/stab2303,
title = {A Bayesian inference and model selection algorithm with an optimization scheme to infer the model noise power},
author = {J L\'{o}pez-Santiago and Luca Martino and Manuel A V\'{a}zquez and Joaqu\'{i}n M\'{i}guez},
url = {https://doi.org/10.1093/mnras/stab2303},
doi = {10.1093/mnras/stab2303},
issn = {0035-8711},
year  = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Monthly Notices of the Royal Astronomical Society},
volume = {507},
number = {3},
pages = {3351-3361},
abstract = {Model fitting is possibly the most extended problem in science. Classical approaches include the use of least-squares fitting procedures and maximum likelihood methods to estimate the value of the parameters in the model. However, in recent years, Bayesian inference tools have gained traction. Usually, Markov chain Monte Carlo (MCMC) methods are applied to inference problems, but they present some disadvantages, particularly when comparing different models fitted to the same data set. Other Bayesian methods can deal with this issue in a natural and effective way. We have implemented an importance sampling (IS) algorithm adapted to Bayesian inference problems in which the power of the noise in the observations is not known a priori. The main advantage of IS is that the model evidence can be derived directly from the so-called importance weights – while MCMC methods demand considerable postprocessing. The use of our adaptive target adaptive importance sampling (ATAIS) method is shown by inferring, on the one hand, the parameters of a simulated flaring event that includes a damped oscillation and, on the other hand, real data from the Kepler mission. ATAIS includes a novel automatic adaptation of the target distribution. It automatically estimates the variance of the noise in the model. ATAIS admits parallelization, which decreases the computational run-times notably. We compare our method against a nested sampling method within a model selection problem.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{9438636,
title = {Boosting Offline Handwritten Text Recognition in Historical Documents With Few Labeled Lines},
author = {Jos\'{e} Carlos Aradillas and Juan Jos\'{e} Murillo-Fuentes and Pablo M Olmos},
doi = {10.1109/ACCESS.2021.3082689},
year  = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {IEEE Access},
volume = {9},
pages = {76674-76688},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inbook{doi,
title = {Low-Density Parity-Check (LDPC) Codes for 5G Communications},
author = {Pablo M Olmos and Yanfang Liu and David G M Mitchell},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119471509.w5GRef013},
doi = {https://doi.org/10.1002/9781119471509.w5GRef013},
isbn = {9781119471509},
year  = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {Wiley 5G Ref},
pages = {1-23},
publisher = {American Cancer Society},
abstract = {Abstract In this article, we describe the fundamental advances in low-density parity-check (LDPC) codes over the last two decades with special emphasis on the class of LDPC codes selected for the 5G new radio standard. We present structured protograph and quasi-cyclic LDPC codes, which are convenient for hardware implementation. The 5G LDPC codes are then reviewed in detail. Hardware considerations regarding the implementation of the encoders and decoders of 5G LDPC codes are also discussed. We conclude the article by presenting three of the more promising extensions of LDPC codes known to date (generalized LDPC codes, nonbinary LDPC codes, and spatially coupled LDPC codes), which could potentially replace conventional LDPC codes in future communication standards.},
keywords = {5G, Channel Coding, FPGA, hardware implementation, LDPC codes, pipeline architecture, protographs, quasi-cyclic LDPC codes},
pubstate = {published},
tppubtype = {inbook}
}
@article{9398939,
title = {Spatially Coupled Generalized LDPC Codes: Asymptotic Analysis and Finite Length Scaling},
author = {David G M Mitchell and Pablo M Olmos and Michael Lentmaier and Daniel J Costello},
doi = {10.1109/TIT.2021.3071743},
year  = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {67},
number = {6},
pages = {3708-3723},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{SEVILLASALCEDO2021108141,
title = {Sparse semi-supervised heterogeneous interbattery bayesian analysis},
author = {Carlos Sevilla-Salcedo and Vanessa G\'{o}mez-Verdejo and Pablo M Olmos},
url = {https://www.sciencedirect.com/science/article/pii/S0031320321003289},
doi = {https://doi.org/10.1016/j.patcog.2021.108141},
issn = {0031-3203},
year  = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Pattern Recognition},
volume = {120},
pages = {108141},
abstract = {The Bayesian approach to feature extraction, known as factor analysis (FA), has been widely studied in machine learning to obtain a latent representation of the data. An adequate selection of the probabilities and priors of these bayesian models allows the model to better adapt to the data nature (i.e. heterogeneity, sparsity), obtaining a more representative latent space. The objective of this article is to propose a general FA framework capable of modelling any problem. To do so, we start from the Bayesian Inter-Battery Factor Analysis (BIBFA) model, enhancing it with new functionalities to be able to work with heterogeneous data, to include feature selection, and to handle missing values as well as semi-supervised problems. The performance of the proposed model, Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis (SSHIBA), has been tested on different scenarios to evaluate each one of its novelties, showing not only a great versatility and an interpretability gain, but also outperforming most of the state-of-the-art algorithms.},
keywords = {Bayesian model, Canonical correlation analysis, Factor analysis, Feature selection, Multi-task, Principal component analysis, Semi-supervised},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{9287757,
title = {On the computation of marginal likelihood via MCMC for model selection and hypothesis testing},
author = {Fernando Llorente and Luca Martino and David Delgado-G\'{o}mez and J L\'{o}pez-Santiago},
doi = {10.23919/Eusipco47968.2020.9287757},
year  = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {2020 28th European Signal Processing Conference (EUSIPCO)},
pages = {2373-2377},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{moreno2021modular,
title = {Modular Gaussian Processes for Transfer Learning},
author = {P Moreno-Mu\~{n}oz and Antonio Art\'{e}s-Rodr\'{i}guez and Mauricio Alvarez},
year  = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Advances in Neural Information Processing Systems},
volume = {34},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{9594658,
title = {Medical data wrangling with sequential variational autoencoders},
author = {Daniel Barrejon and Pablo M Olmos and Antonio Artes-Rodr\'{i}guez},
doi = {10.1109/JBHI.2021.3123839},
year  = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {1-1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{PEREZVIEITES2021108295,
title = {Nested Gaussian filters for recursive Bayesian inference and nonlinear tracking in state space models},
author = {Sara P\'{e}rez-Vieites and Joaqu\'{i}n M\'{i}guez},
url = {https://www.sciencedirect.com/science/article/pii/S0165168421003327},
doi = {https://doi.org/10.1016/j.sigpro.2021.108295},
issn = {0165-1684},
year  = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Signal Processing},
volume = {189},
pages = {108295},
abstract = {We introduce a new sequential methodology to calibrate the fixed parameters and track the stochastic dynamical variables of a state-space system. The proposed method is based on the nested hybrid filtering (NHF) framework of [1], that combines two layers of filters, one inside the other, to compute the joint posterior probability distribution of the static parameters and the state variables. In particular, we explore the use of deterministic sampling techniques for Gaussian approximation in the first layer of the algorithm, instead of the Monte Carlo methods employed in the original procedure. The resulting scheme reduces the computational cost and so makes the algorithms potentially better-suited for high-dimensional state and parameter spaces. We describe a specific instance of the new method and then study its performance and efficiency of the resulting algorithms for a stochastic Lorenz 63 model and for a stochastic volatility model with real data.},
keywords = {Bayesian inference, Filtering, Kalman, Monte Carlo, Parameter estimation},
pubstate = {published},
tppubtype = {article}
}
@article{9665783,
title = {Scaling Laws for Gaussian Random Many-Access Channels},
author = {Jithin Ravi and Tobias Koch},
doi = {10.1109/TIT.2021.3139430},
year  = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {IEEE Transactions on Information Theory},
pages = {1-1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Ravi-Asilomar2021,
title = {Scaling Laws for Many-Access Channels and Unsourced Random Access},
author = {Jithin Ravi and Tobias Koch},
doi = {10.1109/IEEECONF53345.2021.9723116 address = Pacific Grove, CA, USA},
year  = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {2021 55th Asilomar Conference on Signals, Systems, and Computers},
pages = {1482-1487},
note = {Invited},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{castellanos2021position,
title = {Position-Based Adaptive Power Back-Off for User Electromagnetic Exposure Management in 
Millimeter Wave Systems},
author = {Miguel R Castellanos and Borja Peleato and David J Love},
year  = {2021},
date = {2021-01-01},
journal = {IEEE Wireless Communications Letters},
volume = {11},
number = {1},
pages = {86--90},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{kadirvelu2021circuit,
title = {A Circuit for Simultaneous Reception of Data and Power Using a Solar Cell},
author = {Sindhubala Kadirvelu and Walter D Leon-Salas and Xiaozhe Fan and Jongseok Kim and Borja Peleato and Saeed Mohammadi and B Vijayalakshmi},
year  = {2021},
date = {2021-01-01},
journal = {IEEE Transactions on Green Communications and Networking},
volume = {5},
number = {4},
pages = {2065--2075},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{gvazquez-TIT2021,
title = {Error Probability Bounds for Gaussian Channels Under Maximal and Average Power Constraints},
author = {Gonzalo Vazquez-Vilar},
doi = {10.1109/TIT.2021.3063311},
year  = {2021},
date = {2021-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {67},
number = {6},
pages = {3965-3985},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{vazquez2021interpretable,
title = {An interpretable machine learning method for the detection of schizophrenia using EEG signals},
author = {Manuel A V\'{a}zquez and Arash Maghsoudi and In\'{e}s P Mari no},
year  = {2021},
date = {2021-01-01},
journal = {Frontiers in Systems Neuroscience},
volume = {15},
publisher = {Frontiers Media SA},
keywords = {yo},
pubstate = {published},
tppubtype = {article}
}
@article{lopez-morinigo_luisa_porra_art\'{e}s-rodr\'{i}guez_etal._2021,
title = {Pending challenges to e-mental health in the COVID-19 era: Acceptability of a smartphone-based ecological momentary assessment application among patients with schizophrenia spectrum disorders},
author = {Javier-David Lopez-Morinigo and B. -E. Maria Luisa and Alejandro Porras-Segovia and Adela S\'{a}nchez Escribano Mart\'{i}nez and P. -J. Escobedo-Aedo and Ver\'{o}nica Gonz\'{a}lez Ruiz-Ruano and L. Mata-Iturralde and L. Mu\~{n}oz-Lorenzo and S. S\'{a}nchez-Alonso and Antonio Art\'{e}s-Rodr\'{i}guez},
doi = {10.1192/j.eurpsy.2021.920},
year  = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {European Psychiatry},
volume = {64},
number = {S1},
pages = {S343\textendashS343},
publisher = {Cambridge University Press},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{virgili2021uncertainty,
title = {Uncertainty Propagation Meeting Space Debris Needs},
author = {Benjamin Bastida Virgili and Jorge Bravo Aguado and Alejandro Cano and Diego Escobar and Stijn S Lemmens and J L\'{o}pez-Santiago and Alberto L\'{o}pez Yela and Pablo M Olmos and Klaus Merz and Joaqu\'{i}n M\'{i}guez and Manuel A V\'{a}zquez},
year  = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {8th European Conference on Space Debris},
keywords = {yo},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{rust_xydis_heutschi_perraudin_casas_du_strauss_eggenschwiler_perez-cruz_gramazio_etal._2021,
title = {A data acquisition setup for data driven acoustic design},
author = {Romana Rust and Achilleas Xydis and Kurt Heutschi and Nathanael Perraudin and Gonzalo Casas and Chaoyu Du and J\"{u}rgen Strauss and Kurt Eggenschwiler and Fernando Perez-Cruz and Fabio Gramazio and Matthias Kohler},
doi = {10.1177/1351010X20986901},
year  = {2021},
date = {2021-01-01},
journal = {Building Acoustics},
volume = {28},
number = {4},
pages = {345-360},
publisher = {Sage},
note = {1. sco. 2. Yes. 3. No. 4. . 5. . 6. Yes. 7. . 8. Published. 9. . 10. .},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{cobo_porras-segovia_p\'{e}rez-rodr\'{i}guez_art\'{e}s-rodr\'{i}guez_barrig\'{o}n_courtet_baca-garc\'{i}a_2021,
title = {Patients at high risk of suicide before and during a COVID-19 lockdown: ecological momentary assessment study},
author = {Aurora Cobo and Alejandro Porras-Segovia and Mercedes M Perez-Rodriguez and Antonio Art\'{e}s-Rodr\'{i}guez and Maria Luisa Barrig\'{o}n and Philippe Courtet and Enrique Baca-Garc\'{i}a},
doi = {10.1192/bjo.2021.43},
year  = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {BJPsych Open},
volume = {7},
number = {3},
pages = {e82},
publisher = {Cambridge University Press},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{articlec,
title = {On the performance of particle filters with adaptive number of particles},
author = {Victor Elvira and Joaqu\'{i}n M\'{i}guez and Petar M Djuric},
doi = {10.1007/s11222-021-10056-0},
year  = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Statistics and Computing},
volume = {31},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{\&lt;LineBreak\&gt;	10261_258325,
title = {Exploring the coupled ocean and atmosphere system with a data science approach applied to observations from the Antarctic Circumnavigation Expedition},
author = {Sebastian Landwehr and Michele Volpi and Alexander Haumann and Charlotte Mary Robinson and Iris Thurnherr and Valerio Ferracci and Andrea Baccarini and Jenny Thomas and Irina V. Gorodetskaya and Christian Tatzelt and Silvia Henning and Robin L. Modini and Heather J. Forrer and Yajuan Lin and Nicolas Cassar and Rafel Sim\'{o} and Christel S. Hassler and Alireza Moallemi and Sarah E. Fawcett and Neil R. P. Harris and Ruth Airs and Marzieh H. Derkani and Alberto Alberello and Alessandro Toffoli and G Chen and P. Rodr\'{i}guez-Ros and Marina Zamanillo Campos and Pau Cortes and Lei Xue and Conor G. Bolas and Katherine C. Leonard and Fernando Perez-Cruz and David Walton and Julia Schmale},
doi = {10.5194/esd-12-1295-2021},
year  = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
organization = {Rafel Sim\'{o}, Marina Zamanillo, Pau Cort\'{e}s-Greus, and Pablo Rodr\'{i}guez-Ros were supported by the Spanish Ministry of Science through the BIOGAPS project (CTM2016-81008-R) organization =With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)},
abstract = {The Southern Ocean is a critical component of Earth's climate system, but its remoteness makes it challenging to develop a holistic understanding of its processes from the small scale to the large scale. As a result, our knowledge of this vast region remains largely incomplete. The Antarctic Circumnavigation Expedition (ACE, austral summer 2016/2017) surveyed a large number of variables describing the state of the ocean and the atmosphere, the freshwater cycle, atmospheric chemistry, and ocean biogeochemistry and microbiology. This circumpolar cruise included visits to 12 remote islands, the marginal ice zone, and the Antarctic coast. Here, we use 111 of the observed variables to study the latitudinal gradients, seasonality, shorter-term variations, geographic setting of environmental processes, and interactions between them over the duration of 90 d. To reduce the dimensionality and complexity of the dataset and make the relations between variables interpretable we applied an unsupervised machine learning method, the sparse principal component analysis (sPCA), which describes environmental processes through 14 latent variables. To derive a robust statistical perspective on these processes and to estimate the uncertainty in the sPCA decomposition, we have developed a bootstrap approach. Our results provide a proof of concept that sPCA with uncertainty analysis is able to identify temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and “hotspots” of interaction between environmental compartments. While confirming many well known processes, our analysis provides novel insights into the Southern Ocean water cycle (freshwater fluxes), trace gases (interplay between seasonality, sources, and sinks), and microbial communities (nutrient limitation and island mass effects at the largest scale ever reported). More specifically, we identify the important role of the oceanic circulations, frontal zones, and islands in shaping the nutrient availability that controls biological community composition and productivity; the fact that sea ice controls sea water salinity, dampens the wave field, and is associated with increased phytoplankton growth and net community productivity possibly due to iron fertilisation and reduced light limitation; and the clear regional patterns of aerosol characteristics that have emerged, stressing the role of the sea state, atmospheric chemical processing, and source processes near hotspots for the availability of cloud condensation nuclei and hence cloud formation. A set of key variables and their combinations, such as the difference between the air and sea surface temperature, atmospheric pressure, sea surface height, geostrophic currents, upper-ocean layer light intensity, surface wind speed and relative humidity played an important role in our analysis, highlighting the necessity for Earth system models to represent them adequately. In conclusion, our study highlights the use of sPCA to identify key ocean\textendashatmosphere interactions across physical, chemical, and biological processes and their associated spatio-temporal scales. It thereby fills an important gap between simple correlation analyses and complex Earth system models. The sPCA processing code is available as open-access from the following link: https://renkulab.io/gitlab/ACE-ASAID/spca-decomposition (last access: 29 March 2021). As we show here, it can be used for an exploration of environmental data that is less prone to cognitive biases (and confirmation biases in particular) compared to traditional regression analysis that might be affected by the underlying research question},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{info12030121,
title = {A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators},
author = {Sichen Li and M\'{e}lissa Zacharias and Jochem Snuverink and Jaime Coello de Portugal and Fernando Perez-Cruz and Davide Reggiani and Andreas Adelmann},
url = {https://www.mdpi.com/2078-2489/12/3/121},
doi = {10.3390/info12030121},
issn = {2078-2489},
year  = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Information},
volume = {12},
number = {3},
abstract = {The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to decrease beam time loss in the High-Intensity Proton Accelerator complex by forecasting interlock events. The forecasting is performed through binary classification of windows of multivariate time series. The time series are transformed into Recurrence Plots which are then classified by a Convolutional Neural Network, which not only captures the inner structure of the time series but also uses the advances of image classification techniques. Our best-performing interlock-to-stable classifier reaches an Area under the ROC Curve value of 0.71±0.01 compared to 0.65±0.01 of a Random Forest model, and it can potentially reduce the beam time loss by 0.5±0.2 s per interlock.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Moreno-MunozRamirezArtes-Rodriguez-2021,
title = {Change-point detection in hierarchical circadian models},
author = {P Moreno-Mu\~{n}oz and David Ram\'{i}rez and Antonio Art\'{e}s-Rodr\'{i}guez},
doi = {10.1016/j.patcog.2021.107820},
issn = {0031-3203},
year  = {2021},
date = {2021-00-01},
journal = {Pattern Recognition},
volume = {113},
pages = {107820},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Lopez_Santiago_2020,
title = {A Likely Magnetic Activity Cycle for the Exoplanet Host M Dwarf GJ 3512},
author = {J Lopez-Santiago and Luca Martino and Joaqu\'{i}n M\'{i}guez and Manuel A V\'{a}zquez},
url = {https://doi.org/10.3847/1538-3881/abc171},
doi = {10.3847/1538-3881/abc171},
year  = {2020},
date = {2020-11-01},
urldate = {2020-11-01},
journal = {The Astronomical Journal},
volume = {160},
number = {6},
pages = {273},
publisher = {American Astronomical Society},
abstract = {Current radial velocity data from specialized instruments contain a large amount of information that may pass unnoticed if their analysis is not accurate. The joint use of Bayesian inference tools and frequency analysis has been shown as effective in revealing exoplanets but they have been used less frequently to investigate stellar activity. We intend to use radial velocity data of the exoplanet host star GJ 3512 to investigate its magnetic activity. Our study includes the analysis of the photometric data available. The main objectives of our work are to constrain the orbital parameters of the exoplanets in the system, to determine the current level of activity of the star and to derive an activity cycle length for it. An adaptive importance sampling method was used to determine the parameters of the exoplanets orbit. Generalized Lomb\textendashScargle periodograms were constructed with both radial velocity curve and photometric data. A careful analysis of the harmonic frequencies was conducted in each periodogram. Our fit to multiple Keplerian orbits constrained the orbital parameters of two giant gas planets orbiting the star GJ 3512. The host star showed an increase of its magnetic activity during the last observing campaign. The accurate fit of the radial velocity curve data to the multi-Keplerian orbit permitted to reveal the star rotation in the residuals of the best fit and estimate an activity cycle length of ∼14 yr.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{AArtes20h,
title = {Actigraphic recording of motor activity in depressed inpatients: a novel computational approach to prediction of clinical course and hospital discharge},
author = {Ignacio Peis and Javier-David L\'{o}pez-Mor\'{i}\~{n}igo and Mercedes M Perez-Rodriguez and Maria Luisa Barrig\'{o}n and Marta Ruiz-G\'{o}mez and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a },
doi = {https://doi.org/10.1038/s41598-020-74425-x},
year  = {2020},
date = {2020-10-14},
journal = {Scientific Reports},
volume = {10},
number = {17286},
keywords = {Actigraphic recording},
pubstate = {published},
tppubtype = {article}
}
@article{AArtes20e,
title = {A Probabilistic Patient Scheduling Model with Time Variable Slots},
author = {Danae Carreras-Garc\'{i}a and David Delgado-G\'{o}mez and Enrique Baca-Garc\'{i}a and Antonio Art\'{e}s-Rodr\'{i}guez},
doi = {https://doi.org/10.1155/2020/9727096},
year  = {2020},
date = {2020-09-01},
journal = {Computational and Mathematical Methods in Medicine},
volume = {2020},
number = {9727096},
pages = {10},
keywords = {e-health, patient scheduling systems, prediction theory},
pubstate = {published},
tppubtype = {article}
}
@article{Tobi20b,
title = {Nearest Neighbor Decoding and Pilot-Aided Channel Estimation for Fading Channels},
author = {Taufiq A Asyhari and Tobias Koch and Albert Guill\'{e}n i F\`{a}bregas},
doi = {https://doi.org/10.3390/e22090971},
year  = {2020},
date = {2020-08-31},
journal = {Entropy},
volume = {22},
number = {9},
pages = {971},
keywords = {achievable rates, Fading, high signal-to-noise ratio (SNR), mismatched decoding, multiple antennas, multiple-access channels, nearest neighbor decoding, noncoherent, pilot-aided channel estimation},
pubstate = {published},
tppubtype = {article}
}
@article{AArtes20b,
title = {Ecological Momentary Assessment for Monitoring Risk of Suicide Behavior},
author = {Patricia Carretero and Juan Jos\'{e} Campa\~{n}a-Montes and Antonio Art\'{e}s-Rodr\'{i}guez},
doi = {https://doi.org/10.1007/7854_2020_170},
year  = {2020},
date = {2020-08-15},
journal = {Current Topics in Behavioral Neurosciences},
keywords = {Big data, Digital footprint, Digital phenotype, e-health, Ecological momentary assessment, Machine learning, Mobile health, Suicidal risk, Wearable devices},
pubstate = {published},
tppubtype = {article}
}
@article{AArtes20d,
title = {Real-Time Ventricular Cancellation in Unipolar Atrial Fibrillation Electrograms},
author = {Gonzalo R\'{i}os-Mu\~{n}oz and Antonio Art\'{e}s-Rodr\'{i}guez and Francisco Fern\'{a}ndez-Avil\'{e}s and \'{A}ngel Arenal},
doi = {https://doi.org/10.3389/fbioe.2020.00789},
year  = {2020},
date = {2020-07-30},
journal = {Frontiers in Bioengineering and Biotechnology},
volume = {8},
number = {789},
keywords = {atrial fibrillation, biomedical signal processing, multi-electrode catheter, real-time, unipolar electrograms},
pubstate = {published},
tppubtype = {article}
}
@article{JMiguez20c,
title = {Parallel sequential Monte Carlo for stochastic gradient-free nonconvex optimization},
author = {O. D. Akyildiz and Dan Crisan and Joaqu\'{i}n Miguez},
doi = {https://doi.org/10.1007/s11222-020-09964-4},
year  = {2020},
date = {2020-07-29},
journal = {Statistics and Computing},
keywords = {Gradient-free optimization, Nonconvex optimization, Sampling, Sequential Monte Carlo, Stochastic optimization},
pubstate = {published},
tppubtype = {article}
}
@article{AArtes20f,
title = {Universal mental health screening with a focus on suicidal behaviour using smartphones in a Mexican rural community: protocol for the SMART-SCREEN population-based survey},
author = {Pavel E Arenas-Casta\~{n}eda and Fuensanta Aroca and Ismael Martinez-Nicolas and Luis A Castillo Esp\'{i}ndola and Igor Barahona and Cynthya Maya-Hern\'{a}ndez and Martha Miriam Lavana Hern\'{a}ndez and Paulo C\'{e}sar Manrique Mir\'{o}n and Daniela Guadalupe Alvarado Barrera and Erik Trevi\~{n}o Aguilar and Axay\'{a}catl Barrios N\'{u}\~{n}ez and Giovanna De Jesus Carlos and Anabel Vildosola Garc\'{e}s and Josselyne Flores Mercado and Maria Luisa Barrig\'{o}n and Antonio Art\'{e}s-Rodr\'{i}guez and Santiago de Leon and Cristian Antonio Molina-Pizarro and Arsenio Rosado Franco and Mercedes M Perez-Rodriguez and Philippe Courtet and Gonzalo Mart\'{i}nez-Al\'{e}s and Enrique Baca-Garc\'{i}a},
doi = {10.1136/bmjopen-2019-035041},
year  = {2020},
date = {2020-07-19},
journal = {BMJ Open 2020},
volume = {10},
number = {e035041},
keywords = {Mental Health, Smartphone, Suicidal behavior},
pubstate = {published},
tppubtype = {article}
}
@article{2020AMT....13.3815M,
title = {Integration and calibration of non-dispersive infrared (NDIR) CO$_2$ low-cost sensors and their operation in a sensor network covering Switzerland},
author = {Michael M\"{u}ller and Peter Graf and Jonas Meyer and Anastasia Pentina and Dominik Brunner and Fernando Perez-Cruz and Christoph H\"{u}glin and Lukas Emmenegger},
doi = {10.5194/amt-13-3815-2020},
year  = {2020},
date = {2020-07-01},
urldate = {2020-07-01},
journal = {Atmospheric Measurement Techniques},
volume = {13},
number = {7},
pages = {3815-3834},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Tobi20c,
title = {Bursty Wireless Networks of Bounded Capacity},
author = {Grace Villacr\'{e}s and Tobias Koch and Gonzalo Vazquez-Vilar},
doi = {10.1109/ISIT44484.2020.9174034},
year  = {2020},
date = {2020-06-21},
booktitle = {2020 IEEE International Symposium on Information Theory (ISIT)},
pages = {2959-2964},
keywords = {Signal to noise ratio},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tobi20d,
title = {A High-SNR Normal Approximation for MIMO Rayleigh Block-Fading Channels},
author = {Chao Qi and Tobias Koch},
doi = {10.1109/ISIT44484.2020.9174409},
year  = {2020},
date = {2020-06-21},
booktitle = {2020 IEEE International Symposium on Information Theory (ISIT)},
pages = {2314-2319},
keywords = {Multiple Input Multiple Output (MIMO), Signal to noise ratio},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Tobi20e,
title = {Capacity per Unit-Energy of Gaussian Random Many-Access Channels},
author = {Jithin Ravi and Tobias Koch},
doi = {10.1109/ISIT44484.2020.9174091},
year  = {2020},
date = {2020-06-21},
booktitle = {2020 IEEE International Symposium on Information Theory (ISIT)},
pages = {3025-3030},
keywords = {Gaussian channels},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{AArtes20c,
title = {Smartphone-based ecological momentary assessment (EMA) in psychiatric patients and student controls: A real-world feasibility study},
author = {Alejandro Porras-Segovia and Rosa Mar\'{i}a Molina-Madue\~{n}o and Sofian Berrouiguet and Jorge L\'{o}pez-Castrom\'{a}n and Maria Luisa Barrig\'{o}n and Mar\'{i}a Sandra P\'{e}rez-Rodr\'{i}guez and Jos\'{e} Heliodoro Marco and Isaac D\'{i}az-Oliv\'{a}n and Santiago de Le\'{o}n and Philippe Courtet and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a},
doi = {https://doi.org/10.1016/j.jad.2020.05.067},
year  = {2020},
date = {2020-05-26},
urldate = {2020-05-26},
journal = {Journal of Affective Disorders},
volume = {274},
pages = {733-741},
keywords = {Ecological momentary assessment, Wearable devices},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{2020EGUGA..2219564S,
title = {Data assimilation in lake Geneva using the SPUX framework},
author = {Artur Safin and Damien Bouffard and James Runnalls and Fotis Georgatos and Eric Bouillet and Firat Ozdemir and Fernando Perez-Cruz and Jonas \v{S}ukys},
doi = {10.5194/egusphere-egu2020-19564},
year  = {2020},
date = {2020-05-01},
urldate = {2020-05-01},
booktitle = {EGU General Assembly Conference Abstracts},
pages = {19564},
series = {EGU General Assembly Conference Abstracts},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Tobi20,
title = {Saddlepoint Approximations for Short-Packet Wireless Communications},
author = {Alejandro Lancho and Johan \"{O}stman and Giuseppe Durisi and Tobias Koch and Gonzalo Vazquez-Vilar },
doi = {10.1109/TWC.2020.2987573},
year  = {2020},
date = {2020-04-20},
journal = {IEEE Transactions on Wireless Communications},
volume = {19},
number = {7},
pages = {4831 - 4846},
keywords = {fading channels, finite-blocklength information theory, saddlepoint approximations, short packets, Ultra-reliable low-latency communications},
pubstate = {published},
tppubtype = {article}
}
@article{JMiguez20b,
title = {Stable Approximation Schemes for Optimal Filters},
author = {Dan Crisan and Alberto L\'{o}pez-Yela and Joaqu\'{i}n Miguez},
doi = {10.1137/19M1255410},
year  = {2020},
date = {2020-03-26},
journal = {SIAM/ASA Journal on Uncertainty Quantification},
volume = {8},
number = {1},
pages = {483-509},
keywords = {optimal filters, stability analysis, State space models, truncated filters},
pubstate = {published},
tppubtype = {article}
}
@article{Ram\'{i}rez2020b,
title = {Two-Channel Passive Detection of Cyclostationary Signals},
author = {S Horstmann and David Ram\'{i}rez and Peter J Schreier},
doi = {10.1109/TSP.2020.2981767},
issn = {1053-587X},
year  = {2020},
date = {2020-03-18},
journal = {IEEE Trans. Signal Process.},
volume = {68},
pages = {2340-2355},
keywords = {Cyclostationarity, generalized likelihood ratio test (GLRT), locally most powerful invariant test (LMPIT), multiple-input multiple-output (MIMO) passive detection},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{gvazquez-IZS2020,
title = {On the Error Probability of Optimal Codes in Gaussian Channels under Average Power Constraint},
author = {Gonzalo Vazquez-Vilar},
year  = {2020},
date = {2020-02-01},
booktitle = {2020 International Zurich Seminar on Information and Communication (IZS 2020)},
address = {Zurich, Switzerland},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{AArtes20,
title = {Study protocol of a randomised clinical trial testing whether metacognitive training can improve insight and clinical outcomes in schizophrenia},
author = {Javier-David Lopez-Morinigo and Ver\'{o}nica Gonz\'{a}lez Ruiz-Ruano and Adela S\'{a}nchez Escribano Mart\'{i}nez and Mar\'{i}a Luisa Barrig\'{o}n and L. Mata-Iturralde and L. Mu\~{n}oz-Lorenzo and S. S\'{a}nchez-Alonso and Antonio Art\'{e}s-Rodr\'{i}guez and Anthony S David and Enrique Baca-Garc\'{i}a },
doi = {https://doi.org/10.1186/s12888-020-2431-x},
year  = {2020},
date = {2020-01-29},
urldate = {2020-01-29},
journal = {BMC Psychiatry},
volume = {20},
number = {30},
keywords = {Ecological momentary assessment, Insight, Metacognitive training, Schizophrenia spectrum disorders},
pubstate = {published},
tppubtype = {article}
}
@article{2020A\&amp;A...635A.206C,
title = {Is there Na I in the atmosphere of HD 209458b?. Effect of the centre-to-limb variation and Rossiter-McLaughlin effect in transmission spectroscopy studies},
author = {N Casasayas-Barris and E Pall\'{e} and F Yan and G Chen and R Luque and M Stangret and E Nagel and M Zechmeister and M Oshagh and J Sanz-Forcada and L Nortmann and F J Alonso-Floriano and P J Amado and J A Caballero and S Czesla and S Khalafinejad and M L\'{o}pez-Puertas and J L\'{o}pez-Santiago and K Molaverdikhani and D Montes and A Quirrenbach and A Reiners and I Ribas and A S\'{a}nchez-L\'{o}pez and M R Zapatero Osorio},
doi = {10.1051/0004-6361/201937221},
year  = {2020},
date = {2020-01-01},
journal = {Astronomy and Astrophysics},
volume = {635},
pages = {A206},
keywords = {Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Solar and Stellar Astrophysics, methods: observational, planetary systems, planets and satellites: atmospheres, planets and satellites: individual: HD 209458b, techniques: spectroscopic},
pubstate = {published},
tppubtype = {article}
}
@article{Ram\'{i}rez2020,
title = {Multi-channel factor analysis with common and unique factors},
author = {David Ram\'{i}rez and Ignacio Santamar\'{i}a and L L Scharf and Steven Van Vaerenbergh},
doi = {10.1109/TSP.2019.2955829},
issn = {1053-587X},
year  = {2020},
date = {2020-01-01},
journal = {IEEE Trans. Signal Process.},
volume = {68},
pages = {113-126},
keywords = {Block minorization-maximization algorithms, expectation-maximization algorithms, maximum likelihood estimation, multi-channel factor analysis, multiple-input multiple-output channels, passive radar},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{ethz-b-000403243,
title = {On the Per-User Probability of Error in Gaussian Many-Access Channels},
author = {Jithin Ravi and Tobias Koch},
year  = {2020},
date = {2020-01-01},
booktitle = {International Zurich Seminar on Information and Communication (IZS 2020)},
pages = {139-143},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{SantamariaScharfRamirez-2020,
title = {Scale-invariant subspace detectors based on first- and second-order statistical models},
author = {Ignacio Santamar\'{i}a and L L Scharf and David Ram\'{i}rez},
doi = {10.1109/TSP.2020.3036725},
issn = {1053-587X},
year  = {2020},
date = {2020-01-01},
journal = {IEEE {T}rans. Signal Process.},
volume = {68},
pages = {6432-6443},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{perez2020nested,
title = {A nested hybrid filter for parameter estimation and state tracking in homogeneous multi-scale models},
author = {Sara P\'{e}rez-Vieites and Joaqu\'{i}n M\'{i}guez},
year  = {2020},
date = {2020-01-01},
booktitle = {2020 IEEE 23rd International Conference on Information Fusion (FUSION)},
pages = {1--8},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{norbury2020social,
title = {Social media and smartphone app use predicts maintenance of physical activity during Covid-19 enforced isolation in psychiatric outpatients},
author = {Agnes Norbury and Shelley Liu and Juan Jos\'{e} Campa\~{n}a-Montes and Lorena Romero-Medrano and Mar\'{i}a Luisa Barrig\'{o}n and Emma Smith and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a and Mercedes M Perez-Rodriguez},
year  = {2020},
date = {2020-01-01},
journal = {Molecular psychiatry},
pages = {1--11},
publisher = {Nature Publishing Group},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{norbury2020use,
title = {Use of Actigraphy and Ecological Momentary Assessment to Monitor the Impact of COVID-19 on Mood and Behavior in Psychiatric Outpatients: Social Media and Smartphone App Use Predicts Maintenance of Physical Activity},
author = {Agnes Norbury and Shelley Liu and Maria Luisa Barrigon and Juan Jose Campana-Montes and Lorena Romero-Medrano and Emma Smith and Elizabeth Ramjas and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garcia and Mercedes M Perez-Rodriguez},
year  = {2020},
date = {2020-01-01},
booktitle = {NEUROPSYCHOPHARMACOLOGY},
volume = {45},
number = {SUPPL 1},
pages = {300--300},
organization = {SPRINGERNATURE CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{kim2020sleep,
title = {Sleep Dynamics, Weight, and Appetite in a Prospective Cohort of Psychiatric Patients During COVID-19},
author = {Youngjung Kim and Juan Jose Campa\~{n}a-Montes and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garcia and Mercedes M Perez-Rodriguez},
year  = {2020},
date = {2020-01-01},
booktitle = {NEUROPSYCHOPHARMACOLOGY},
volume = {45},
number = {SUPPL 1},
pages = {254--255},
organization = {SPRINGERNATURE CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{nazabal2020handling,
title = {Handling incomplete heterogeneous data using vaes},
author = {Alfredo Nazabal and Pablo M Olmos and Zoubin Ghahramani and Isabel Valera},
year  = {2020},
date = {2020-01-01},
journal = {Pattern Recognition},
volume = {107},
pages = {107501},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{aradillas2020improving,
title = {Improving offline HTR in small datasets by purging unreliable labels},
author = {Jos\'{e} Carlos Aradillas and Juan Jos\'{e} Murillo-Fuentes and Pablo M Olmos},
year  = {2020},
date = {2020-01-01},
booktitle = {2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR)},
pages = {25--30},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{rios2020hidden,
title = {Hidden Markov Models for Activity Detection in Atrial Fibrillation Electrograms},
author = {Gonzalo R\'{i}os-Mu\~{n}oz and Fernando Moreno-Pino and Nina Soto and Pablo M Olmos and Antonio Art\'{e}s-Rodr\'{i}guez and Francisco Fernandez-Aviles and Angel Arenal},
year  = {2020},
date = {2020-01-01},
booktitle = {2020 Computing in Cardiology},
pages = {1--4},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{9322143,
title = {On the Design of Generalized LDPC Codes with Component BCJR Decoding},
author = {Yanfang Liu and Pablo M Olmos and David G M Mitchell},
doi = {10.1109/GLOBECOM42002.2020.9322143},
year  = {2020},
date = {2020-01-01},
booktitle = {GLOBECOM 2020 - 2020 IEEE Global Communications Conference},
pages = {1-6},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{ASHEGHAN202016457,
title = {On the Stability of a Stochastic Nonlinear Model of the Heart Beat Rate During a Treadmill Exercise},
author = {Mohammad Mostafa Asheghan and Bahram Shafai and Joaqu\'{i}n M\'{i}guez},
url = {https://www.sciencedirect.com/science/article/pii/S2405896320310545},
doi = {https://doi.org/10.1016/j.ifacol.2020.12.735},
issn = {2405-8963},
year  = {2020},
date = {2020-01-01},
journal = {IFAC-PapersOnLine},
volume = {53},
number = {2},
pages = {16457-16461},
abstract = {We investigate the stability properties of a nonlinear stochastic dynamical model of a person’s heart beat rate (HBR) during a treadmill exercise. The analysis is based on the Lyapunov direct method and it is valid for systems with either known or unknown parameters. Specifically, we characterize an upper bound on the norm of the cumulative noise that holds in the presence of bounded errors in the model parameters and guarantees p-stability. Numerical simulations are presented that corroborate the theoretical results.},
note = {21st IFAC World Congress},
keywords = {nonlinear dynamcs, parameter perturbation, stability analysis, Stochastic processes, system biology},
pubstate = {published},
tppubtype = {article}
}
@article{fernandez2020estimacion,
title = {Estimaci\'{o}n del desplazamiento horizontal del detector en un sistema de rayos X utilizando aprendizaje por transferencia},
author = {Carlos Fern\'{a}ndez del Cerro and RC Gimenez and Pablo M Olmos and Alessandro Piol and Manuel Desco Men\'{e}ndez and M\'{o}nica Abella Garc\'{i}a},
year  = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
publisher = {Sociedad Espa~nola de Ingenier'ia Biom\'{e}dica},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{9260147,
title = {Adaptive Quadrature Schemes for Bayesian Inference via Active Learning},
author = {Fernando Llorente Fern\'{a}ndez and Luca Martino and Victor Elvira and David Delgado-G\'{o}mez and J L\'{o}pez-Santiago},
doi = {10.1109/ACCESS.2020.3038333},
year  = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {IEEE Access},
volume = {8},
pages = {208462-208483},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{article,
title = {Nudging the Particle Filter},
author = {O. D. Akyildiz and Joaqu\'{i}n M\'{i}guez},
doi = {10.1007/s11222-019-09884-y},
year  = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Statistics and Computing},
volume = {30},
pages = {305-330},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{8861117b,
title = {On Single-Antenna Rayleigh Block-Fading Channels at Finite Blocklength},
author = {Alejandro Lancho and Tobias Koch and Giuseppe Durisi},
doi = {10.1109/TIT.2019.2945782},
year  = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {66},
number = {1},
pages = {496-519},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{zhang2020increasing,
title = {Increasing Throughput in Wireless Communications by Grouping Similar Quality Bits},
author = {Mai Zhang and Jiho Song and David J Love and Dennis Ogbe and Amitava Ghosh and Borja Peleato},
year  = {2020},
date = {2020-01-01},
journal = {IEEE Communications Letters},
volume = {24},
number = {11},
pages = {2450--2453},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{zhang2020optimizing,
title = {Optimizing HARQ and Relay Strategies in Limited Feedback Communication Systems},
author = {Mai Zhang and Andres Castillo and Borja Peleato},
year  = {2020},
date = {2020-01-01},
journal = {Applied Sciences},
volume = {10},
number = {21},
pages = {7917},
publisher = {Multidisciplinary Digital Publishing Institute},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{leon2020modulation,
title = {Modulation of LED Photo-Luminescence for Underwater Optical Communications},
author = {Walter D Leon-Salas and Xiaozhe Fan and James Hidalgo and Borja Peleato and Pablo J Molina},
year  = {2020},
date = {2020-01-01},
booktitle = {2020 IEEE International Symposium on Circuits and Systems (ISCAS)},
pages = {1--5},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{zhang2020average,
title = {On the average rate for coded caching with heterogeneous user profiles},
author = {Ciyuan Zhang and Borja Peleato},
year  = {2020},
date = {2020-01-01},
booktitle = {ICC 2020-2020 IEEE International Conference on Communications (ICC)},
pages = {1--6},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{chang2020coded,
title = {On coded caching for two users with overlapping demand sets},
author = {Chih-Hua Chang and Chih-Chun Wang and Borja Peleato},
year  = {2020},
date = {2020-01-01},
booktitle = {ICC 2020-2020 IEEE International Conference on Communications (ICC)},
pages = {1--6},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{articled,
title = {Integration and calibration of non-dispersive infrared (NDIR) CO_{2} low-cost sensors and their operation in a sensor network covering Switzerland},
author = {Michael M\"{u}ller and Peter Graf and Jonas Meyer and Anastasia Pentina and Dominik Brunner and Fernando Perez-Cruz and Christoph H\"{u}glin and Lukas Emmenegger},
doi = {10.5194/amt-13-3815-2020},
year  = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Atmospheric Measurement Techniques},
volume = {13},
pages = {3815-3834},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{naviadouble,
title = {Double Confidential Federated Machine Learning Logistic Regression for Industrial Data Platforms},
author = {A Navia-V\'{a}zquez and Manuel A V\'{a}zquez and J Cid-Sueiro},
year  = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
booktitle = {International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML (FL-ICML) 2020},
keywords = {yo},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Bonilla-EscribanoRamirezArtes-Rodriguez-2020,
title = {Modeling phone call durations via switching Poisson processes with applications in mental health},
author = {P Bonilla-Escribano and David Ram\'{i}rez and Antonio Art\'{e}s-Rodr\'{i}guez},
year  = {2020},
date = {2020-00-01},
booktitle = {Proc. IEEE Int. Work. Machine Learning for Signal Process.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Moreno-MunozRamirezArtes-Rodriguez-2020,
title = {Continual learning for infinite hierarchical change-point detection},
author = {P Moreno-Mu\~{n}oz and David Ram\'{i}rez and Antonio Art\'{e}s-Rodr\'{i}guez},
doi = {10.1109/ICASSP40776.2020.9053853},
year  = {2020},
date = {2020-00-01},
booktitle = {Proc. IEEE Int. Conf. Acoustics, Speech and Signal Process.},
address = {Barcelona, Spain},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{XiaoRamirezSchreier-2020,
title = {A general test for the linear structure of covariance matrices of Gaussian populations},
author = {Y -H Xiao and David Ram\'{i}rez and Peter J Schreier},
doi = {10.1109/ICASSP40776.2020.9053718},
year  = {2020},
date = {2020-00-01},
booktitle = {Proc. IEEE Int. Conf. Acoustics, Speech and Signal Process.},
address = {Barcelona, Spain},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{JMiguez19c,
title = {Multilayer Models of Random Sequences: Representability and Inference via Nonlinear Population Monte Carlo},
author = {Joaqu\'{i}n Miguez and Lucas Lacasa and Jos\'{e} A. Mart\'{i}nez-Ord\'{o}\~{n}ez and In\'{e}s P. Mari\~{n}o},
doi = {10.1109/CAMSAP45676.2019.9022529},
year  = {2019},
date = {2019-12-15},
booktitle = {2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)},
keywords = {Markov chains, Multilayer networks, population Monte Carlo, random sequences},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{AArtes19d,
title = {Validation of Fitbit Charge 2 and Fitbit Alta HR Against Polysomnography for Assessing Sleep in Adults With Obstructive Sleep Apnea},
author = {Fernando Moreno-Pino and Alejandro Porras-Segovia and Pilar L\'{o}pez-Esteban and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a},
doi = {10.5664/jcsm.8032},
year  = {2019},
date = {2019-11-15},
journal = {Journal of Clinical Sleep Medicine},
volume = {15},
number = {11},
pages = {1645-1653},
keywords = {e-health, sleep apnea, Sleep disorders, Wearables},
pubstate = {published},
tppubtype = {article}
}
@article{Bonilla-Escribano2019,
title = {Assessment of e-social activity in psychiatric patients},
author = {P Bonilla-Escribano and David Ram\'{i}rez and Alba Sedano-Capdevila and Juan Jose Campa\~{n}a-Montes and Enrique Baca-Garc\'{i}a and Philippe Courtet and Antonio Art\'{e}s-Rodr\'{i}guez},
doi = {10.1109/JBHI.2019.2918687},
issn = {2168-2194},
year  = {2019},
date = {2019-11-01},
journal = {IEEE J. Biomedical and Health Informatics},
volume = {23},
number = {6},
pages = {2247-2256},
keywords = {E-social Activity, expectation-maximisation algorithm, maximum likelihood, mixture model, point processes},
pubstate = {published},
tppubtype = {article}
}
@article{Geiger-TIT2019a,
title = {On the Information Dimension of Stochastic Processes},
author = {Bernhard C Geiger and Tobias Koch},
issn = {0018-9448},
year  = {2019},
date = {2019-10-01},
journal = {IEEE Transactions on Information Theory},
volume = {65},
number = {10},
pages = {6496-6518},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{FPerez18,
title = {Probabilistic Time of Arrival Localization},
author = {Fernando P\'{e}rez-Cruz and Pablo M Olmos and Michael Minyi Zhang and Howard Huang},
doi = {10.1109/LSP.2019.2944005},
year  = {2019},
date = {2019-09-26},
journal = {IEEE Signal Processing Letters},
volume = {26},
number = {11},
pages = {1683 - 1687},
keywords = {Probabilistic modeling, Time of arrival localization},
pubstate = {published},
tppubtype = {article}
}
@article{AArtes19c,
title = {Combining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol},
author = {Sofian Berrouiguet and Mar\'{i}a Luisa Barrig\'{o}n and Jorge L\'{o}pez-Castrom\'{a}n and Philippe Courtet and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a },
doi = {https://doi.org/10.1186/s12888-019-2260-y},
year  = {2019},
date = {2019-09-07},
journal = {BMC Psychiatry},
volume = {19},
number = {277},
keywords = {Data Mining, sensors, Smartphone, Suicide, Wearables},
pubstate = {published},
tppubtype = {article}
}
@article{gvazquez-tit19,
title = {The Error Probability of Generalized Perfect Codes via the Meta-Converse},
author = {Gonzalo Vazquez-Vilar and Albert Guill\'{e}n i F\`{a}bregas and Sergio Verd\'{u}},
doi = {10.1109/TIT.2019.2906227},
issn = {0018-9448},
year  = {2019},
date = {2019-09-01},
journal = {IEEE Transactions on Information Theory},
volume = {65},
number = {9},
pages = {5705-5717},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{liu-TIT2019a,
title = {A Probabilistic Peeling Decoder to Efficiently Analyze Generalized LDPC Codes Over the BEC},
author = {Yanfang Liu and Pablo M Olmos and Tobias Koch},
issn = {0018-9448},
year  = {2019},
date = {2019-08-01},
journal = {IEEE Transactions on Information Theory},
volume = {65},
number = {8},
pages = {4831-4853},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{JMiguez20,
title = {Nudging the particle Filter},
author = {O. D. Akyildiz and Joaqu\'{i}n Miguez},
doi = {https://doi.org/10.1007/s11222-019-09884-y},
year  = {2019},
date = {2019-07-13},
journal = {Statistics and Computing},
volume = {30},
pages = {305-330},
keywords = {approximation errors, Data assimilation, Model errors, Nudging, Particle filtering, Robust filtering},
pubstate = {published},
tppubtype = {article}
}
@article{AArtes19b,
title = {Onset of schizophrenia diagnoses in a large clinical cohort},
author = {Jorge L\'{o}pez-Castrom\'{a}n and Jos\'{e} M Leiva-Murillo and Fanny Cegla-Schvartzman and Hilario Blasco-Fontecilla and R Garc\'{i}a-Nieto and Antonio Art\'{e}s-Rodr\'{i}guez and C Morant-Ginestar and Philippe Courtet and Carlos Blanco and Fuensanta Aroca and Enrique Baca-Garc\'{i}a},
doi = {https://doi.org/10.1038/s41598-019-46109-8},
year  = {2019},
date = {2019-07-08},
journal = {Scientific Reports},
volume = {9},
number = {9865},
keywords = {Schizoprenia diagnosis},
pubstate = {published},
tppubtype = {article}
}
@article{JMiguez19b,
title = {A probabilistic incremental proximal gradient method},
author = {O. D. Akyildiz and E. Chouzenoux and Victor Elvira and Joaqu\'{i}n Miguez},
doi = {10.1109/LSP.2019.2926926},
year  = {2019},
date = {2019-07-04},
journal = {IEEE Signal Processing Letters},
volume = {26},
number = {8},
pages = {1257-1261},
keywords = {extended Kalman filtering, Probabilistic optimization, proximal algorithms, stochastic gradient},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{gvazquez-isit2019,
title = {On the Error Probability of Optimal Codes in Gaussian Channels under Maximal Power Constraint},
author = {Gonzalo Vazquez-Vilar},
year  = {2019},
date = {2019-07-01},
booktitle = {2019 IEEE International Symposium on Information Theory (ISIT 2019)},
address = {Paris, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Garg2019,
title = {Subspace averaging and order determination for source enumeration},
author = {V. Garg and Ignacio Santamar\'{i}a and David Ram\'{i}rez and L L Scharf},
doi = {10.1109/TSP.2019.2912151},
issn = {1053-587X},
year  = {2019},
date = {2019-06-01},
urldate = {2019-06-01},
journal = {IEEE Trans. Signal Process.},
volume = {67},
number = {11},
pages = {3028-3041},
keywords = {Array processing, dimension, Grassmann manifold, order estimation, source enumeration, subspace averaging},
pubstate = {published},
tppubtype = {article}
}
@article{AArtes19,
title = {Deep Sequential Models for Suicidal Ideation From Multiple Source Data},
author = {Ignacio Peis and Pablo M Olmos and Constanza Vera-Varela and Mar\'{i}a Luisa Barrig\'{o}n and Philippe Courtet and Enrique Baca-Garc\'{i}a and Antonio Artes-Rodr\'{i}guez},
doi = {10.1109/JBHI.2019.2919270},
year  = {2019},
date = {2019-05-27},
journal = {IEEE Journal of Biomedical and Health Informatics},
volume = {23},
number = {6},
pages = {2286 - 2293},
keywords = {attention, Deep learning, EMA, RNN, Suicide},
pubstate = {published},
tppubtype = {article}
}
@article{FPerez18b,
title = {Case-control Indian buffet process identifies biomarkers of response to Codrituzumab},
author = {Melanie F. Pradier and Bernhard Reis and Lori Jukofsky and Francesca Milletti and Toshihiko Ohtomo and Fernando Perez-Cruz and Oscar Puig },
doi = {https://doi.org/10.1186/s12885-019-5472-0},
year  = {2019},
date = {2019-03-28},
journal = {BMC Cancer},
volume = {19},
number = {278},
keywords = {Codrituzumab, Indian buffet process, Natural killer cells},
pubstate = {published},
tppubtype = {article}
}
@article{2019A\&amp;amp;A...622A.210G,
title = {Simultaneous Kepler/K2 and XMM-Newton observations of superflares in the Pleiades},
author = {M ~G Guarcello and G Micela and S Sciortino and J L\'{o}pez-Santiago and C Argiroffi and F Reale and E Flaccomio and J ~D Alvarado-G\'{o}mez and V Antoniou and J ~J Drake and I. Pillitteri and L Rebull and J Stauffer},
doi = {10.1051/0004-6361/201834370},
year  = {2019},
date = {2019-02-01},
urldate = {2019-02-01},
journal = {Astronomy and Astrophysics},
volume = {622},
pages = {A210},
keywords = {Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics, stars: flare, X-rays: stars},
pubstate = {published},
tppubtype = {article}
}
@article{JMiguez19,
title = {Bayesian computation methods for inference in stochastic kinetic models},
author = {Eugenia Koblents and In\'{e}s P. Mari\~{n}o and Joaqu\'{i}n M\'{i}guez},
doi = {https://doi.org/10.1155/2019/7160934},
year  = {2019},
date = {2019-01-20},
journal = {Complexity},
volume = {2019},
number = {ID 7160934},
pages = {15},
keywords = {Monte Carlo (MC) methods, Stochastic kinetic models},
pubstate = {published},
tppubtype = {article}
}
@article{2019Sci...365.1441M,
title = {A giant exoplanet orbiting a very-low-mass star challenges planet formation models},
author = {JC Morales and et al.},
doi = {10.1126/science.aax3198},
year  = {2019},
date = {2019-01-01},
journal = {Science},
volume = {365},
number = {6460},
pages = {1441-1445},
keywords = {Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Solar and Stellar Astrophysics, PLANET SCI; ASTRONOMY},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{8849659,
title = {Saddlepoint Approximations for Noncoherent Single-Antenna Rayleigh Block-Fading Channels},
author = {Alejandro Lancho and Johan \"{O}stman and Giuseppe Durisi and Tobias Koch and Gonzalo Vazquez-Vilar},
year  = {2019},
date = {2019-01-01},
booktitle = {2019 IEEE International Symposium on Information Theory (ISIT)},
pages = {612-616},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{8849751,
title = {Capacity per Unit-Energy of Gaussian Many-Access Channels},
author = {Jithin Ravi and Tobias Koch},
year  = {2019},
date = {2019-01-01},
booktitle = {2019 IEEE International Symposium on Information Theory (ISIT)},
pages = {2763-2767},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{BonillaEscribano2019MixturesOH,
title = {Mixtures of Heterogeneous Poisson Processes for the Assessment of e-Social Activity in Mental Health},
author = {P Bonilla-Escribano and David Ram\'{i}rez and Antonio Art\'{e}s-Rodr\'{i}guez},
year  = {2019},
date = {2019-01-01},
abstract = {This work introduces a novel method to assess the social activity maintained by psychiatric patients using information and communication technologies. In particular, we jointly model using point processes the e-social activity patterns from two heterogeneous sources: the usage of phone calls and social and communication apps. We propose a nonhomogeneous Poisson mixture model with periodic (circadian) intensity function using a truncated Fourier series expansion, which is inferred using a trust-region algorithm, and it is able to cope with the different daily patterns of a person. The analysis of the usage of phone calls and social and communication apps of a cohort of 164 patients reveals that 25 patterns suffice to characterize their daily behavior.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{perez2019second,
title = {Second Order Subspace Statistics for Adaptive State-Space Partitioning in Multiple Particle Filtering},
author = {Sara P\'{e}rez-Vieites and Jordi Vil\'{a}-Valls and M\'{o}nica F Bugallo and Joaqu\'{i}n M\'{i}guez and Pau Closas},
doi = {10.1109/CAMSAP45676.2019.9022449},
year  = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
booktitle = {2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)},
pages = {609-613},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{martinez2019portable,
title = {Portable Multispectral System Based on Color Detector for the Analysis of Homogeneous Surfaces},
author = {Antonio Mart\'{i}nez-Olmos and Pablo M Olmos and Miguel M Erenas and Pablo Escobedo},
year  = {2019},
date = {2019-01-01},
journal = {Journal of Sensors},
volume = {2019},
publisher = {Hindawi},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{9049072,
title = {Finite-Blocklength Approximations for Noncoherent Rayleigh Block-Fading Channels},
author = {Alejandro Lancho and Johan \"{O}stman and Tobias Koch and Gonzalo Vazquez-Vilar},
doi = {10.1109/IEEECONF44664.2019.9049072},
year  = {2019},
date = {2019-01-01},
booktitle = {2019 53rd Asilomar Conference on Signals, Systems, and Computers},
pages = {815-819},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{deniz2019dictionary,
title = {Dictionary filtering: a probabilistic approach to online matrix factorisation},
author = {O. D. Akyildiz and Joaqu\'{i}n M\'{i}guez},
doi = {10.1007/s11760-018-1403-9},
year  = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
journal = {SIGNAL IMAGE AND VIDEO PROCESSING},
volume = {13},
number = {4},
pages = {737--744},
publisher = {SPRINGER LONDON LTD 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{10.1007/978-3-030-21568-2_11,
title = {PassGAN: A Deep Learning Approach for Password Guessing},
author = {Briland Hitaj and Paolo Gasti and Giuseppe Ateniese and Fernando Perez-Cruz},
editor = {Robert H. Deng and Val\'{e}rie Gauthier-Uma\~{n}a and Mart\'{i}n Ochoa and Moti Yung},
isbn = {978-3-030-21568-2},
year  = {2019},
date = {2019-01-01},
booktitle = {\"{A}pplied Cryptography and Network Security"},
pages = {217--237},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {State-of-the-art password guessing tools, such as HashCat and John the Ripper, enable users to check billions of passwords per second against password hashes. In addition to performing straightforward dictionary attacks, these tools can expand password dictionaries using password generation rules, such as concatenation of words (e.g., ``password123456'') and leet speak (e.g., ``password'' becomes ``p4s5w0rd''). Although these rules work well in practice, creating and expanding them to model further passwords is a labor-intensive task that requires specialized expertise.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{10.1007/978-3-030-21949-9_21,
title = {Synchrotron X-Ray Phase Contrast Imaging and Deep Neural Networks for Cardiac Collagen Quantification in Hypertensive Rat Model},
author = {Hector Dejea and Christine Tanner and R. Achanta and Marco Stampanoni and Fernando Perez-Cruz and Ender Konukoglu and Anne Bonnin},
editor = {Yves Coudi`ere and Val\'{e}ry Ozenne and Edward Vigmond and Nejib Zemzemi},
isbn = {978-3-030-21949-9},
year  = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
booktitle = {Functional Imaging and Modeling of the Heart},
pages = {187--195},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {\"{A}n excessive deposition of collagen matrix in the myocardium has been clearly identified as an indication of the progression towards heart failure. Nevertheless, few studies have been performed for its quantification and most of them use 2D histological images, thus losing valuable encoded 3D information. In this study, several biopsies of areas of the left ventricle from age-matched spontaneously hypertensive rats and Wistar Kyoto rats were imaged using synchrotron radiation-based X-ray phase contrast imaging. Then, an optimized deep neural network was used for automatic image segmentation in order to assess collagen fraction differences between models as well as its age dependency. The results show a general increase in the collagen percentage in the hypertensive model and for older rats. Such tendency is comparable with the reports found in the literature. Therefore, this proof of concept shows that synchrotron imaging in combination with deep neural networks is a powerful tool for the investigation and quantification of cardiac microstructures."},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{b_109,
title = {Source enumeration in the presence of colored noise},
author = {Alma Eguizabal and C Lameiro and David Ramirez and Peter J Schreier},
doi = {10.1109/LSP.2019.2895548},
issn = {1070-9908},
year  = {2019},
date = {2019-00-01},
journal = {IEEE Signal Process. Lett.},
volume = {26},
number = {3},
pages = {475--479},
abstract = {In array signal processing the detection of the number of sources is an important step. Most approaches assume the signals to be embedded in white noise. However, this assumption is unrealistic in many scenarios. In this paper, we propose a strategy that can handle colored noise. We model the source detection as a regression problem and apply information-theoretic criteria to determine the model order of the regression. We show simulations of different scenarios, where our approach outperforms traditional techniques.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{HorstmannRamirezSchreier-2019,
title = {Two-channel passive detection of cyclostationary signals in noise with spatio-temporal structure},
author = {S Horstmann and David Ram\'{i}rez and Peter J Schreier and Aaron Pries},
doi = {10.1109/IEEECONF44664.2019.9048746},
year  = {2019},
date = {2019-00-01},
booktitle = {Asilomar Conf. Signals, Syst. and Computers},
address = {Pacific Grove, USA},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{HorstmannRamirezSchreier-2019b,
title = {Two-channel passive detection exploiting cyclostationarity},
author = {S Horstmann and David Ram\'{i}rez and Peter J Schreier},
doi = {10.23919/EUSIPCO.2019.8902989},
year  = {2019},
date = {2019-00-01},
booktitle = {Proc. Eur. Signal Process. Conf.},
address = {A Coru~na, Spain},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{JMiguez18b,
title = {Probabilistic scheme for joint parameter estimation and state prediction in complex dynamical systems},
author = {Sara P\'{e}rez-Vieites and In\'{e}s P. Mari\~{n}o and Joaqu\'{i}n M\'{i}guez},
doi = {10.1103/PhysRevE.98.063305},
year  = {2018},
date = {2018-12-05},
journal = {Physical Review E},
volume = {98},
number = {063305},
pages = {1-19},
keywords = {Dynamical models},
pubstate = {published},
tppubtype = {article}
}
@article{JMiguez18c,
title = {Multiplex Decomposition of Non-Markovian Dynamics and the Hidden Layer Reconstruction Problem},
author = {Lucas Lacasa and In\'{e}s P. Mari\~{n}o and Joaquin Miguez and Vincenzo Nicosia and \'{E}dgar Rold\'{a}n and Ana Lisica and Stephan W. Grill and Jes\'{u}s G\'{o}mez-Garde\~{n}es},
doi = {10.1103/PhysRevX.8.031038},
year  = {2018},
date = {2018-08-07},
journal = {Physical Review X},
volume = {8},
number = {031038},
pages = {1-36},
keywords = {Biological Physics, Complex Systems, Interdisciplinary Physics},
pubstate = {published},
tppubtype = {article}
}
@article{FPerez18d,
title = {Economic complexity unfolded: Interpretable model for the productive structure of economies},
author = {Zoran Utkovski and Melanie F. Pradier and Viktor Stojkoski and Fernando Perez-Cruz and Ljupco Kocarev},
doi = {https://doi.org/10.1371/journal.pone.0200822},
year  = {2018},
date = {2018-08-07},
journal = {PLoS ONE},
volume = {13(8)},
number = {e0200822},
keywords = {Indian buffet process},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{JMiguez18d,
title = {A Comparison Of Clipping Strategies For Importance Sampling},
author = {Luca Martino and Victor Elvira and Joaqu\'{i}n Miguez and Antonio Art\'{e}s-Rodr\'{i}guez and Petar M Djuric},
doi = {10.1109/SSP.2018.8450722},
year  = {2018},
date = {2018-06-10},
booktitle = {2018 IEEE Statistical Signal Processing Workshop (SSP)},
keywords = {Bayesian inference, Importance sampling, Monte Carlo methods, Parameter estimation, Variance Reduction methods},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{JMiguez18e,
title = {A Probabilistic Approach for Adaptive State-Space Partitioning},
author = {Jordi Vil\`{a}-Valls and Pau Closas and M\'{o}nica F Bugallo and Joaqu\'{i}n Miguez},
doi = {10.1109/SSP.2018.8450821},
year  = {2018},
date = {2018-06-10},
journal = {2018 IEEE Statistical Signal Processing Workshop (SSP)},
keywords = {Adaptive state partitioning, correlated subspaces, multiple Gaussian filtering, uncertainty exchange},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{gvazquez-isit2018b,
title = {Saddlepoint approximation of the error probability of binary hypothesis testing},
author = {Gonzalo Vazquez-Vilar and Albert Guillen i Fabregas and Tobias Koch and Alejandro Lancho},
year  = {2018},
date = {2018-06-01},
booktitle = {2018 IEEE International Symposium on Information Theory (ISIT 2018)},
address = {Vail, USA},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{gvazquez-isit2018a,
title = {The error probability of generalized perfect codes},
author = {Gonzalo Vazquez-Vilar and Albert Guillen i Fabregas and Sergio Verdu},
year  = {2018},
date = {2018-06-01},
booktitle = {2018 IEEE International Symposium on Information Theory (ISIT 2018)},
address = {Vail, USA},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{8437597,
title = {Design of Discrete Constellations for Peak-Power-Limited complex Gaussian Channels},
author = {Wasim Huleihel and Ziv Goldfeld and Tobias Koch and Mokshay Madiman and Muriel M\'{e}dard},
year  = {2018},
date = {2018-06-01},
booktitle = {2018 IEEE International Symposium on Information Theory (ISIT)},
pages = {556-560},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Crisan2018,
title = {On the performance of parallelisation schemes for particle filtering},
author = {Dan Crisan and Joaqu\'{i}n M\'{i}guez and Gonzalo R\'{i}os-Mu\~{n}oz},
url = {https://doi.org/10.1186/s13634-018-0552-x},
doi = {10.1186/s13634-018-0552-x},
issn = {1687-6180},
year  = {2018},
date = {2018-05-25},
journal = {EURASIP Journal on Advances in Signal Processing},
volume = {2018},
number = {1},
pages = {31},
abstract = {Considerable effort has been recently devoted to the design of schemes for the parallel implementation of sequential Monte Carlo (SMC) methods for dynamical systems, also widely known as particle filters (PFs). In this paper, we present a brief survey of recent techniques, with an emphasis on the availability of analytical results regarding their performance. Most parallelisation methods can be interpreted as running an ensemble of lower-cost PFs, and the differences between schemes depend on the degree of interaction among the members of the ensemble. We also provide some insights on the use of the simplest scheme for the parallelisation of SMC methods, which consists in splitting the computational budget into M non-interacting PFs with N particles each and then obtaining the desired estimators by averaging over the M independent outcomes of the filters. This approach minimises the parallelisation overhead yet still displays desirable theoretical properties. We analyse the mean square error (MSE) of estimators of moments of the optimal filtering distribution and show the effect of the parallelisation scheme on the approximation error rates. Following these results, we propose a time--error index to compare schemes with different degrees of parallelisation. Finally, we provide two numerical examples involving stochastic versions of the Lorenz 63 and Lorenz 96 systems. In both cases, we show that the ensemble of non-interacting PFs can attain the approximation accuracy of a centralised PF (with the same total number of particles) in just a fraction of its running time using a standard multicore computer.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{JMiguez18f,
title = {The Incremental Proximal Method: A Probabilistic Perspective},
author = {O. D. Akyildiz and V\'{i}ctor Elvira and Joaqu\'{i}n M\'{i}guez},
doi = {10.1109/ICASSP.2018.8462131},
issn = {2379-190X},
year  = {2018},
date = {2018-04-15},
booktitle = {2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
keywords = {Incremental proximal methods, Kalman filtering, Stochastic optimization},
pubstate = {published},
tppubtype = {inproceedings}
}
@inbook{nokey,
title = {Ratio of Uniforms},
author = {Luca Martino and David Luengo and Joaqu\'{i}n M\'{i}guez},
doi = {10.1007/978-3-319-72634-2_5},
isbn = {978-3-319-72634-2},
year  = {2018},
date = {2018-04-01},
urldate = {2018-04-01},
booktitle = {Independent Random Sampling Methods},
pages = {159-196},
publisher = {Springer, Cham},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inbook{nokey,
title = {Independent Sampling for Multivariate Densities},
author = {Luca Martino and David Luengo and Joaqu\'{i}n M\'{i}guez},
doi = {10.1007/978-3-319-72634-2_6},
isbn = {978-3-319-72634-2},
year  = {2018},
date = {2018-04-01},
booktitle = {Independent Random Sampling Methods},
pages = {197-247},
publisher = {Springer, Cham},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inbook{nokey,
title = {Adaptive Rejection Sampling Methods},
author = {Luca Martino and David Luengo and Joaqu\'{i}n M\'{i}guez},
doi = {10.1007/978-3-319-72634-2_4},
isbn = {978-3-319-72634-2},
year  = {2018},
date = {2018-04-01},
booktitle = {Independent Random Sampling Methods},
pages = {115-157},
publisher = {Springer, Cham},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inbook{nokey,
title = {Direct Methods},
author = {Luca Martino and David Luengo and Joaqu\'{i}n M\'{i}guez},
doi = {10.1007/978-3-319-72634-2_2},
isbn = {978-3-319-72634-2},
year  = {2018},
date = {2018-04-01},
booktitle = {Independent Random Sampling Methods},
pages = {27-63},
publisher = {Springer, Cham},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inbook{nokey,
title = {Asymptotically Independent Samplers},
author = {Luca Martino and David Luengo and Joaqu\'{i}n M\'{i}guez},
doi = {10.1007/978-3-319-72634-2_7},
isbn = {978-3-319-72634-2},
year  = {2018},
date = {2018-04-01},
booktitle = {Independent Random Sampling Methods},
pages = {249-266},
publisher = {Springer, Cham},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
@inproceedings{IglesiasSegarraRey-Escudero:2018,
title = {Demixing and blind deconvolution of graph-diffused sparse signals},
author = {Fernando J Iglesias and Santiago Segarra and Samuel Rey-Escudero and Antonio G Marques and David Ram\'{i}rez},
year  = {2018},
date = {2018-04-01},
booktitle = {Proc. IEEE Int. Conf. Acoustics, Speech and Signal Process.},
address = {Calgary, Canada},
abstract = {This paper generalizes the classical joint problem of signal demixing and blind deconvolution to the realm of graphs. We investigate a setup where a single observation formed by the sum of multiple textitgraph signals is available. The main assumption is that each individual signal is generated by an originally textitsparse input diffused through the graph via the application of a textitgraph filter. In this context, we address the related problems of: 1) separating the individual graph signals, 2) identifying the unknown input supports, and 3) estimating the coefficients of the diffusing graph filters. We first consider the case where each signal -- prior to mixing -- is diffused in a different graph. We then particularize the results for the more challenging case where all the signals are diffused in the same graph. The corresponding demixing and blind graph-signal deconvolution problems are formulated, convex relaxations are presented, and recovery conditions are discussed. Numerical experiments in both the single and multiple graph cases show the capabilities of demixing in synthetic and biology-inspired graphs.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{RamirezRomeroVia:2018,
title = {Locally optimal invariant detector for testing equality of two power spectral densities},
author = {David Ram\'{i}rez and D Romero and Javier V\'{i}a and Roberto L\'{o}pez-Valcarce and Ignacio Santamar\'{i}a},
year  = {2018},
date = {2018-04-01},
booktitle = {Proc. IEEE Int. Conf. Acoustics, Speech and Signal Process.},
address = {Calgary, Canada},
abstract = {This work addresses the problem of determining whether two multivariate random time series have the same power spectral density (PSD), which has applications, for instance, in physical-layer security and cognitive radio. Remarkably, existing detectors for this problem do not usually provide any kind of optimality. Thus, we study here the existence under the Gaussian assumption of optimal invariant detectors for this problem, proving that the uniformly most powerful invariant test (UMPIT) does not exist. Thus, focusing on close hypotheses, we show that the locally most powerful invariant test (LMPIT) only exists for univariate time series. In the multivariate case, we prove that the LMPIT does not exist. However, this proof suggests two LMPIT-inspired detectors, one of which outperforms previously proposed approaches, as computer simulations show.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{koch-TIT2018a,
title = {A Rigorous Approach to High-Resolution Entropy-Constrained Vector Quantization},
author = {Tobias Koch and Gonzalo Vazquez-Vilar},
doi = {10.1109/TIT.2018.2803064},
issn = {0018-9448},
year  = {2018},
date = {2018-04-01},
journal = {IEEE Transactions on Information Theory},
volume = {64},
number = {4},
pages = {2609-2625},
keywords = {Distortion, Distortion measurement, Entropy, Entropy constrained, high resolution, Probability density function, quantization, Rate-distortion, Rate-distortion theory, Vector quantization},
pubstate = {published},
tppubtype = {article}
}
@article{FPerez18,
title = {Complex Gaussian Processes for Regression},
author = {Rafael Boloix-Tortosa and Juan Jos\'{e} Murillo-Fuentes and Francisco Javier Pay\'{a}n-Somet and Fernando P\'{e}rez-Cruz},
doi = {10.1109/TNNLS.2018.2805019},
year  = {2018},
date = {2018-03-06},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
volume = {29},
number = {11},
pages = {5499-5511},
keywords = {Complex-valued processes, Gaussian processes (GPs), kernel methods, regression},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{gvazquez-ciss2018,
title = {Saddlepoint Approximations of Lower and Upper Bounds to the Error Probability in Channel Coding},
author = {Josep Font-Segura and Gonzalo Vazquez-Vilar and Alfonso Martinez and Albert Guill\'{e}n i F\'{a}bregas and Alejandro Lancho},
year  = {2018},
date = {2018-03-01},
booktitle = {52th Annual Conference on Information Sciences and Systems (CISS)},
address = {Princeton, USA},
note = {Invited},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{8362297,
title = {Normal approximations for fading channels},
author = {Alejandro Lancho and Tobias Koch and Giuseppe Durisi},
year  = {2018},
date = {2018-03-01},
booktitle = {2018 52nd Annual Conference on Information Sciences and Systems (CISS)},
pages = {1-6},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{koch_izs2018,
title = {On the Information Dimension Rate of Multivariate Gaussian Processes},
author = {Bernhard C Geiger and Tobias Koch},
year  = {2018},
date = {2018-02-01},
booktitle = {2018 International Zurich Seminar on Information and Communication (IZS)},
pages = {56 - 60},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{FPerez18,
title = {Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation},
author = {Francisco J R Ruiz and Isabel Valera and Lennart Svensson and Fernando Perez-Cruz},
doi = {10.1109/TCCN.2018.2790976},
year  = {2018},
date = {2018-01-08},
journal = {IEEE Transactions on Cognitive Communications and Networking},
volume = {4},
number = {2},
pages = {177-191},
keywords = {Bayesian nonparametrics, machine-to-machine, multiuser communications, stochastic finite state machine},
pubstate = {published},
tppubtype = {article}
}
@article{santos2018turbo,
title = {Turbo EP-based Equalization: a Filter-Type Implementation},
author = {Irene Santos and Juan Jos\'{e} Murillo-Fuentes and Eva Arias-de-Reyna and Pablo M Olmos},
year  = {2018},
date = {2018-01-01},
journal = {IEEE Transactions on Communications, 66 (9), 4259-4270.},
publisher = {IEEE Communications Society},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{jaramillo2018boosting,
title = {Boosting handwriting text recognition in small databases with transfer learning},
author = {Jos\'{e} Carlos Aradillas and Juan Jos\'{e} Murillo-Fuentes and Pablo M Olmos},
year  = {2018},
date = {2018-01-01},
booktitle = {2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR)},
pages = {429--434},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{liu2018generalized,
title = {Generalized LDPC codes for ultra reliable low latency communication in 5G and beyond},
author = {Yanfang Liu and Pablo M Olmos and David G M Mitchell},
year  = {2018},
date = {2018-01-01},
journal = {IEEE Access},
volume = {6},
pages = {72002--72014},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{liu2018generalizedb,
title = {On Generalized LDPC Codes for 5G Ultra Reliable Communication},
author = {Yanfang Liu and Pablo M Olmos and David G M Mitchell},
year  = {2018},
date = {2018-01-01},
booktitle = {2018 IEEE Information Theory Workshop (ITW)},
pages = {1--5},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{costello2018spatially,
title = {Spatially coupled generalized LDPC codes: Introduction and overview},
author = {Daniel J Costello and David G M Mitchell and Pablo M Olmos and Michael Lentmaier},
year  = {2018},
date = {2018-01-01},
booktitle = {2018 IEEE 10th International Symposium on Turbo Codes \& Iterative Information Processing (ISTC)},
pages = {1--6},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{10.3150/17-BEJ954,
title = {Nested particle filters for online parameter estimation in discrete-time state-space Markov models},
author = {Dan Crisan and Joaqu\'{i}n M\'{i}guez},
url = {https://doi.org/10.3150/17-BEJ954},
doi = {10.3150/17-BEJ954},
year  = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Bernoulli},
volume = {24},
number = {4A},
pages = {3039 -- 3086},
publisher = {Bernoulli Society for Mathematical Statistics and Probability},
keywords = {error bounds, model inference, Monte Carlo, Parameter estimation, Particle filtering, recursive algorithms, State space models},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{8645554,
title = {Distributed Multiple Gaussian Filtering for Multiple Target Localization in Wireless Sensor Networks},
author = {Jordi Vil\`{a}-Valls and Pau Closas and Monica F Bugallo and Joaqu\'{i}n M\'{i}guez},
doi = {10.1109/ACSSC.2018.8645554},
year  = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {2018 52nd Asilomar Conference on Signals, Systems and Computers},
pages = {1439-1443},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{8743775,
title = {Particle Filter Tracking of Complex Stochastic Systems Applied to In Silico Wavefront Propagation},
author = {Gonzalo R\'{i}os-Mu\~{n}oz and Antonio Art\'{e}s-Rodr\'{i}guez and Joaqu\'{i}n M\'{i}guez},
doi = {10.22489/CinC.2018.233},
year  = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {2018 Computing in Cardiology Conference (CinC)},
volume = {45},
pages = {1-4},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{MIGUEZ2018281,
title = {Analysis of a nonlinear importance sampling scheme for Bayesian parameter estimation in state-space models},
author = {Joaqu\'{i}n M\'{i}guez and In\'{e}s P. Mari\~{n}o and Manuel A V\'{a}zquez},
url = {https://www.sciencedirect.com/science/article/pii/S0165168417302761},
doi = {https://doi.org/10.1016/j.sigpro.2017.07.030},
issn = {0165-1684},
year  = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Signal Processing},
volume = {142},
pages = {281-291},
abstract = {The Bayesian estimation of the unknown parameters of state-space (dynamical) systems has received considerable attention over the past decade, with a handful of powerful algorithms being introduced. In this paper we tackle the theoretical analysis of the recently proposed nonlinear population Monte Carlo (NPMC). This is an iterative importance sampling scheme whose key features, compared to conventional importance samplers, are (i) the approximate computation of the importance weights (IWs) assigned to the Monte Carlo samples and (ii) the nonlinear transformation of these IWs in order to prevent the degeneracy problem that flaws the performance of conventional importance samplers. The contribution of the present paper is a rigorous proof of convergence of the nonlinear IS (NIS) scheme as the number of Monte Carlo samples, M, increases. Our analysis reveals that the NIS approximation errors converge to 0 almost surely and with the optimal Monte Carlo rate of M−12. Moreover, we prove that this is achieved even when the mean estimation error of the IWs remains constant, a property that has been termed exact approximation in the Markov chain Monte Carlo literature. We illustrate these theoretical results by means of a computer simulation example involving the estimation of the parameters of a state-space model typically used for target tracking.},
keywords = {Adaptive importance sampling, Bayesian inference, Importance sampling, Parameter estimation, population Monte Carlo, State space models},
pubstate = {published},
tppubtype = {article}
}
@article{MIGUEZ2018281b,
title = {Analysis of a nonlinear importance sampling scheme for Bayesian parameter estimation in state-space models},
author = {Joaqu\'{i}n M\'{i}guez and In\'{e}s P. Mari\~{n}o and Manuel A V\'{a}zquez},
url = {https://www.sciencedirect.com/science/article/pii/S0165168417302761},
doi = {https://doi.org/10.1016/j.sigpro.2017.07.030},
issn = {0165-1684},
year  = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Signal Processing},
volume = {142},
pages = {281-291},
abstract = {The Bayesian estimation of the unknown parameters of state-space (dynamical) systems has received considerable attention over the past decade, with a handful of powerful algorithms being introduced. In this paper we tackle the theoretical analysis of the recently proposed nonlinear population Monte Carlo (NPMC). This is an iterative importance sampling scheme whose key features, compared to conventional importance samplers, are (i) the approximate computation of the importance weights (IWs) assigned to the Monte Carlo samples and (ii) the nonlinear transformation of these IWs in order to prevent the degeneracy problem that flaws the performance of conventional importance samplers. The contribution of the present paper is a rigorous proof of convergence of the nonlinear IS (NIS) scheme as the number of Monte Carlo samples, M, increases. Our analysis reveals that the NIS approximation errors converge to 0 almost surely and with the optimal Monte Carlo rate of M−12. Moreover, we prove that this is achieved even when the mean estimation error of the IWs remains constant, a property that has been termed exact approximation in the Markov chain Monte Carlo literature. We illustrate these theoretical results by means of a computer simulation example involving the estimation of the parameters of a state-space model typically used for target tracking.},
keywords = {Adaptive importance sampling, Bayesian inference, Importance sampling, Parameter estimation, population Monte Carlo, State space models},
pubstate = {published},
tppubtype = {article}
}
@article{vazquez2018quantitative,
title = {A quantitative performance study of two automatic methods for the diagnosis of ovarian cancer},
author = {Manuel A V\'{a}zquez and In\'{e}s P Mari no and Oleg Blyuss and Andy Ryan and Aleksandra Gentry-Maharaj and Jatinderpal Kalsi and Ranjit Manchanda and Ian Jacobs and Usha Menon and Alexey Zaikin},
year  = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Biomedical signal processing and control},
volume = {46},
pages = {86--93},
publisher = {Elsevier},
keywords = {yo},
pubstate = {published},
tppubtype = {article}
}
@article{Luengo2018,
title = {Hierarchical Algorithms for Causality Retrieval in Atrial Fibrillation Intracavitary Electrograms},
author = {David Luengo and Gonzalo R\'{i}os-Mu\~{n}oz and Victor Elvira and Carlos S\'{a}nchez and Antonio Art\'{e}s-Rodr\'{i}guez},
doi = {10.1109/JBHI.2018.2805773},
year  = {2018},
date = {2018-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
volume = {PP},
abstract = {Multi-channel intracavitary electrograms (EGMs) are acquired at the electrophysiology laboratory to guide radio frequency catheter ablation of patients suffering from atrial fibrillation (AF). These EGMs are used by cardiologists to determine candidate areas for ablation (e.g., areas corresponding to high dominant frequencies or complex fractionated electrograms). In this paper, we introduce two hierarchical algorithms to retrieve the causal interactions among these multiple EGMs. Both algorithms are based on Granger causality, but other causality measures can be easily incorporated. In both cases, they start by selecting a root node, but they differ on the way in which they explore the set of signals to determine their causeeffect relationships: either testing the full set of unexplored signals (GS-CaRe) or performing a local search only among the set of neighbor EGMs (LS-CaRe). The ensuing causal model provides important information about the propagation of the electrical signals inside the atria, uncovering wavefronts and activation patterns that can guide cardiologists towards candidate areas for catheter ablation. Numerical experiments, on both synthetic signals and annotated real-world signals, show the good performance of the two proposed approaches},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Ruiz2017a,
title = {Caracterizaci\'{o}n del sustrato de los sitios de activaci\'{o}n rotacional en Fibrilaci\'{o}n Auricular Persistente: An\'{a}lisis en funci\'{o}n del ritmo},
author = {Pablo Ruiz M Hernandez and Gonzalo R\'{i}os-Mu\~{n}oz and Evaristo Castellanos and Pablo \'{A}vila and Felipe Atienza and Antonio Art\'{e}s-Rodr\'{i}guez and Francisco Fernandez-Aviles and \'{A}ngel Arenal},
year  = {2018},
date = {2018-01-01},
booktitle = {RITMO18},
address = {Sevilla},
abstract = {La Fibrilaci\'{o}n Auricular (FA) persistente ha mostrado tasas de recurrencia sub\'{o}ptima tras aislamiento de las venas pulmonares. Los sitios de activaci\'{o}n rotacional (rotores), podr\'{i}an estar relacionados con la fibrosis. El mapeo electroanat\'{o}mico de alta densidad con microelectrodos (MADM) asociado a un sistema de localizaci\'{o}n de rotores podr\'{i}a mejorar estos resultados. Objetivo: Caracterizar el voltaje del tejido donde se asientan los rotores, en FA y en ritmo sinusal (RS), de pacientes con FA persistente. El an\'{a}lisis muestra que los rotores se localizan en zonas de voltaje que no corresponden con los umbrales cl\'{a}sicos que identifica la cicatriz, de entre 0.1 y 0.5 mV. El an\'{a}lisis en FA presenta una menor dispersi\'{o}n, por lo que ser\'{i}a preferible para identificar los umbrales de voltaje y reducir en lo posible las zonas donde buscar rotores. El an\'{a}lisis de la actividad rotacional podr\'{i}a mejorar los resultados del tratamiento invasivo de la FA.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{MARTINO_MT_MCMC,
title = {A review of multiple try MCMC algorithms for signal processing},
author = {Luca Martino},
year  = {2018},
date = {2018-01-01},
journal = {Digital Signal Processing},
volume = {75},
pages = {134 - 152},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Martino_Nak18,
title = {Extremely efficient acceptance-rejection method for simulating uncorrelated Nakagami fading channels},
author = {Luca Martino and David Luengo},
year  = {2018},
date = {2018-01-01},
journal = {Communications in Statistics - Simulation and Computation},
volume = {0},
number = {0},
pages = {1-17},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@book{MartinoBook,
title = {Independent Random Sampling Methods},
author = {Luca Martino and David Luengo and Joaqu\'{i}n M\'{i}guez},
year  = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
publisher = {Springer International Publishing},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
@article{e20110870,
title = {Robust Signaling for Bursty Interference},
author = {Grace Villacr\'{e}s and Tobias Koch and Aydin Sezgin and Gonzalo Vazquez-Vilar},
url = {http://www.mdpi.com/1099-4300/20/11/870},
doi = {10.3390/e20110870},
issn = {1099-4300},
year  = {2018},
date = {2018-01-01},
journal = {Entropy},
volume = {20},
number = {11},
abstract = {This paper studies a bursty interference channel, where the presence/absence of interference is modeled by a block-i.i.d. Bernoulli process that stays constant for a duration of T symbols (referred to as coherence block) and then changes independently to a new state. We consider both a quasi-static setup, where the interference state remains constant during the whole transmission of the codeword, and an ergodic setup, where a codeword spans several coherence blocks. For the quasi-static setup, we study the largest rate of a coding strategy that provides reliable communication at a basic rate and allows an increased (opportunistic) rate when there is no interference. For the ergodic setup, we study the largest achievable rate. We study how non-causal knowledge of the interference state, referred to as channel-state information (CSI), affects the achievable rates. We derive converse and achievability bounds for (i) local CSI at the receiver side only; (ii) local CSI at the transmitter and receiver side; and (iii) global CSI at all nodes. Our bounds allow us to identify when interference burstiness is beneficial and in which scenarios global CSI outperforms local CSI. The joint treatment of the quasi-static and ergodic setup further allows for a thorough comparison of these two setups.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{trifonov2018carmenes,
title = {The CARMENES search for exoplanets around M dwarfs-First visual-channel radial-velocity measurements and orbital parameter updates of seven M-dwarf planetary systems},
author = {T Trifonov and M K\"{u}rster and M Zechmeister and L Tal-Or and J A Caballero and A Quirrenbach and P J Amado and I Ribas and A Reiners and S Reffert},
year  = {2018},
date = {2018-01-01},
journal = {Astronomy \&amp;amp; Astrophysics},
volume = {609},
pages = {A117},
publisher = {EDP Sciences},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{reiners2018carmenes,
title = {The CARMENES search for exoplanets around M dwarfs-High-resolution optical and near-infrared spectroscopy of 324 survey stars},
author = {A Reiners and I Ribas and M Zechmeister and J A Caballero and T Trifonov and S Dreizler and JC Morales and L Tal-Or and M Lafarga and A Quirrenbach},
year  = {2018},
date = {2018-01-01},
journal = {Astronomy \&amp;amp; Astrophysics},
volume = {612},
pages = {A49},
publisher = {EDP Sciences},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{reale2018x,
title = {X-Ray Flare Oscillations Track Plasma Sloshing along Star-disk Magnetic Tubes in the Orion Star-forming Region},
author = {F Reale and J L\'{o}pez-Santiago and E Flaccomio and Antonino Petralia and S Sciortino},
year  = {2018},
date = {2018-01-01},
journal = {The Astrophysical Journal},
volume = {856},
number = {1},
pages = {51},
publisher = {IOP Publishing},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{reiners2018carmenesb,
title = {The CARMENES search for exoplanets around M dwarfs-HD147379 b: A nearby Neptune in the temperate zone of an early-M dwarf},
author = {A Reiners and I Ribas and M Zechmeister and J A Caballero and T Trifonov and S Dreizler and JC Morales and L Tal-Or and M Lafarga and A Quirrenbach},
year  = {2018},
date = {2018-01-01},
journal = {Astronomy \&amp;amp; Astrophysics},
volume = {609},
pages = {L5},
publisher = {EDP Sciences},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{lopez2018use,
title = {On the use of wavelets to reveal oscillatory patterns in stellar flare emission},
author = {J L\'{o}pez-Santiago},
year  = {2018},
date = {2018-01-01},
journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},
volume = {376},
number = {2126},
pages = {20170253},
publisher = {The Royal Society Publishing},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{reiners2018vizier,
title = {VizieR Online Data Catalog: 324 CARMENES M dwarfs velocities (Reiners+, 2018)},
author = {A Reiners and M Zechmeister and J A Caballero and I Ribas and JC Morales and S V Jeffers and A Kaminski},
year  = {2018},
date = {2018-01-01},
journal = {VizieR Online Data Catalog},
volume = {361},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{bravi2018gaia,
title = {The Gaia-ESO Survey: a kinematical and dynamical study of four young open clusters},
author = {L Bravi and E Zari and GG Sacco and S Randich and RD Jeffries and RJ Jackson and E Franciosini and E Moraux and J L\'{o}pez-Santiago and E Pancino},
year  = {2018},
date = {2018-01-01},
journal = {Astronomy \&amp;amp; Astrophysics},
volume = {615},
pages = {A37},
publisher = {EDP Sciences},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{RamirezSantamariaVaerenbergh-2018,
title = {An alternating optimization algorithm for two-channel factor analysis with common and uncommon factors},
author = {David Ram\'{i}rez and Ignacio Santamar\'{i}a and Steven Van Vaerenbergh and L L Scharf},
doi = {10.1109/ACSSC.2018.8645457},
year  = {2018},
date = {2018-00-01},
booktitle = {Proc. Asilomar Conf. Signals, Syst. and Computers},
address = {Pacific Grove, USA},
abstract = {An alternating optimization algorithm is presented and analyzed for identifying low-rank signal components, known in factor analysis terminology as common factors, that are correlated across two multiple-input multiple-output (MIMO) channels. The additive noise model at each of the MIMO channels consists of white uncorrelated noises of unequal variances plus a low-rank structured interference that is not correlated across the two channels. The low-rank components at each channel represent uncommon or channel-specific factors.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{EguizabalSchreierRamirez-2018,
title = {Model-order selection in statistical shape models},
author = {Alma Eguizabal and Peter J Schreier and David Ram\'{i}rez},
doi = {10.1109/MLSP.2018.8516941},
year  = {2018},
date = {2018-00-01},
booktitle = {Proc. IEEE Int. Work. Machine Learning for Signal Process.},
address = {Aalborg, Denmark},
abstract = {Statistical shape models enhance machine learning algorithms providing prior information about deformation. A Point Distribution Model (PDM) is a popular landmark-based statistical shape model for segmentation. It requires choosing a model order, which determines how much of the variation seen in the training data is accounted for by the PDM. A good choice of the model order depends on the number of training samples and the noise level in the training data set. Yet the most common approach for choosing the model order simply keeps a predetermined percentage of the total shape variation. In this paper, we present a technique for choosing the model order based on information-theoretic criteria, and we show empirical evidence that the model order chosen by this technique provides a good trade-off between over- and underfitting.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{SantamariaRamirezScharf-2018,
title = {Subspace averaging for source enumeration in large arrays},
author = {Ignacio Santamar\'{i}a and David Ram\'{i}rez and L L Scharf},
doi = {10.1109/SSP.2018.8450837},
year  = {2018},
date = {2018-00-01},
booktitle = {Proc. IEEE Work. Stat. Signal Process.},
address = {Freiburg, Germany},
abstract = {Subspace averaging is proposed and examined as a method of enumerating sources in large linear arrays, under conditions of low sample support. The key idea is to exploit shift invariance as a way of extracting many subspaces, which may then be approximated by a single extrinsic average. An automatic order determination rule for this extrinsic average is then the rule for determining the number of sources. Experimental results are presented for cases where the number of array snapshots is roughly half the number of array elements, and sources are well separated with respect to the Rayleigh limit.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{,
title = {LMPIT-inspired tests for detecting a cyclostationary signal in noise with spatio-temporal structure},
author = {Aaron Pries and David Ram\'{i}rez and Peter J Schreier},
doi = {10.1109/TWC.2018.2859314},
issn = {1536-1276},
year  = {2018},
date = {2018-00-01},
journal = {IEEE {T}rans. Wireless Comm.},
volume = {17},
number = {9},
pages = {6321--6334},
abstract = {In spectrum sensing for cognitive radio, the presence of a primary user can be detected by making use of the cyclostationarity property of digital communication signals. For the general scenario of a cyclostationary signal in temporally colored and spatially correlated noise, it has previously been shown that an asymptotic generalized likelihood ratio test (GLRT) and locally most powerful invariant test (LMPIT) exist. In this paper, we derive detectors for the presence of a cyclostationary signal in various scenarios with structured noise. In particular, we consider noise that is temporally white and/or spatially uncorrelated. Detectors that make use of this additional information about the noise process have enhanced performance. We have previously derived GLRTs for these specific scenarios; here, we examine the existence of LMPITs. We show that these exist only for detecting the presence of a cyclostationary signal in spatially uncorrelated noise. For white noise, an LMPIT does not exist. Instead, we propose tests that approximate the LMPIT, and they are shown to perform well in simulations. Finally, if the noise structure is not known in advance, we also present hypothesis tests using our framework.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{bi,
title = {Combining continuous smartphone native sensors data capture and unsupervised data mining techniques for behavioral changes detection: The feasibility study of the Evidence Based Behavior (eB2) platform},
author = {Sofian Berrouiguet and David Ram\'{i}rez and Mar\'{i}a Luisa Barrig\'{o}n and P Moreno-Mu\~{n}oz and R. Carmona and Enrique Baca-Garc\'{i}a and Antonio Art\'{e}s-Rodr\'{i}guez},
doi = {10.2196/mhealth.9472},
year  = {2018},
date = {2018-00-01},
journal = {JMIR mHealth and uHealth (Special issue on Computing and Mental {H}ealth)},
volume = {6},
number = {12},
pages = {e197},
abstract = {Background: The emergence of smartphones, wearable sensor technologies, and smart homes allows the non-intrusive collection of activity data. Thus, health-related events such as Activities of Daily Living (ADLs, e.g., mobility patterns, feeding, sleeping, ...) can be captured without the patient’s active participation. We designed a system able to detect changes in the mobility patterns based on the smartphone’s native sensors and advanced machine learning and signal processing techniques. Objective: The principal objective of this work was to assess the feasibility of detecting mobility patterns changes in a sample of outpatients suffering from depression using the smartphone’s sensors. The proposed method processed the data acquired by the smartphone using an unsupervised detection technique. Method: Thirty-eight outpatients from the Hospital Fundaci\'{o}n Jim\'{e}nez D\'{i}az Psychiatry Department (Madrid, Spain) participated in the study. The eB2 app was downloaded by patients on the day of recruitment and configured with the assistance of the physician. The app captured the following data: inertial sensors, physical activity, phone calls and message logs, app usage, nearby Bluetooth and Wi-Fi connections, and location. We applied a change-point detection technique to location data on a sample of 9 outpatients recruited between April 6th, 2017 and December 14th, 2017. The change-point detection was based only on location information, but the eB2 platform allowed for an easy integration of additional data. The app remained running in the background on the patient’s smartphone during the study participation. Results: The principal outcome measure was the identification of mobility pattern changes based on an unsupervised detection technique applied to the smartphone’s native sensors data. Results from five patients’ records are presented as a case series . The eB2 system detected specific mobility pattern changes according to the patient’s activity, which may be used as indicators of behavioral and clinical state changes. Discussion: The proposed technique was able to automatically detect changes in the mobility patterns of the outpatients that took part in this study. Assuming these mobility pattern changes correlated with behavioral changes, we have developed a technique that may identify possible relapses or clinical changes. Nevertheless, it is important to point out that the detected changes are not always related to relapses and that some clinical changes cannot be detected by the proposed method.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{b_109b,
title = {Testing equality of multiple power spectral density matrices},
author = {David Ramirez and D Romero and Javier Via and Roberto L\'{o}pez-Valcarce and Ignacio Santamaria},
doi = {10.1109/TSP.2018.2875884},
issn = {1053-587X},
year  = {2018},
date = {2018-00-01},
journal = {IEEE {T}rans. Signal Process.},
volume = {66},
number = {23},
pages = {6268--6280},
abstract = {This paper studies the existence of optimal invariant detectors for determining whether P multivariate processes have the same power spectral density. This problem finds application in multiple fields, including physical layer security and cognitive radio. For Gaussian observations, we prove that the optimal invariant detector, i.e., the uniformly most powerful invariant test, does not exist. Additionally, we consider the challenging case of close hypotheses, where we study the existence of the locally most powerful invariant test (LMPIT). The LMPIT is obtained in the closed form only for univariate signals. In the multivariate case, it is shown that the LMPIT does not exist. However, the corresponding proof naturally suggests an LMPIT-inspired detector that outperforms previously proposed detectors.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{b_110,
title = {Joint detection of almost-cyclostationary signals and estimation of their cycle period},
author = {S Horstmann and David Ram\'{i}rez and Peter J Schreier},
doi = {10.1109/LSP.2018.2871961},
issn = {1070-9908},
year  = {2018},
date = {2018-00-01},
journal = {IEEE Signal Process. Lett.},
volume = {25},
number = {11},
pages = {1695--1699},
abstract = {We propose a technique that jointly detects the presence of almost-cyclostationary (ACS) signals in wide-sense stationary (WSS) noise and provides an estimate of their cycle period. Since the cycle period of an ACS process is not an integer, the approach is based on a combination of a resampling stage and a multiple hypothesis test, which deal separately with the fractional part and the integer part of the cycle period. The approach requires resampling the signal at many different rates, which is computationally expensive. For this reason we propose a filter bank structure that allows us to efficiently resample a signal at many different rates by identifying common interpolation stages among the set of resampling rates.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{JMiguez17c,
title = {Adaptive noisy importance sampling for stochastic optimization},
author = {O. D. Akyildiz and In\'{e}s P. Mari\~{n}o and Joaqu\'{i}n M\'{i}guez},
doi = {10.1109/CAMSAP.2017.8313215},
year  = {2017},
date = {2017-12-10},
booktitle = {2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)},
keywords = {adaptive noisy importance sampler, Optimization},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{8003438,
title = {Continuous Transmission of Spatially Coupled LDPC Code Chains},
author = {Pablo M Olmos and David G M Mitchell and Dimitri Truhachev and Daniel J Costello},
issn = {0090-6778},
year  = {2017},
date = {2017-12-01},
journal = {IEEE Transactions on Communications},
volume = {65},
number = {12},
pages = {5097-5109},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{HorstmannRamirezSchreier:2017,
title = {Detection of almost-cyclostationarity: An approach based on a multiple hypothesis test},
author = {S Horstmann and David Ram\'{i}rez and Peter J Schreier},
year  = {2017},
date = {2017-10-01},
booktitle = {Proc. Asilomar Conf. Signals Syst. Computers},
address = {Pacific Grove, USA},
abstract = {This work presents a technique to detect whether a signal is almost cyclostationary (ACS) or wide-sense stationary (WSS). Commonly, ACS (and also CS) detectors require a priori knowledge of the cycle period, which in the ACS case is not an integer. To tackle the case of unknown cycle period, we propose an approach that combines a resampling technique, which handles the fractional part of the cycle period and allows the use of the generalized likelihood ratio test (GLRT), with a multiple hypothesis test, which handles the integer part of the cycle period. We control the probability of false alarm based on the known distribution of the individual GLRT statistic, results from order statistics, and the Holm multiple test procedure. To evaluate the performance of the proposed detector we consider a communications example, where simulation results show that the proposed technique outperforms state-of-the-art competitors.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{JMiguez17b,
title = {On the performance of nonlinear importance samplers and population Monte Carlo schemes},
author = {Joaqu\'{i}n M\'{i}guez},
doi = {10.1109/ICDSP.2017.8096057},
issn = {2165-3577},
year  = {2017},
date = {2017-08-23},
booktitle = {2017 22nd International Conference on Digital Signal Processing (DSP)},
keywords = {Monte Carlo algorithm},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{JMiguez17,
title = {A comparison of Monte Carlo-based Bayesian parameter estimation methods for stochastic models of genetic networks},
author = {In\'{e}s P. Mari\~{n}o and Alexey Zaikin and Joaqu\'{i}n M\'{i}guez},
doi = {https://doi.org/10.1371/journal.pone.0182015},
year  = {2017},
date = {2017-08-10},
urldate = {2017-08-10},
journal = {PLoS ONE},
volume = {12(8)},
number = {e0182015},
keywords = {Bayesian estimation},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{8006552,
title = {On LDPC code ensembles with generalized constraints},
author = {Yanfang Liu and Pablo M Olmos and Tobias Koch},
doi = {10.1109/ISIT.2017.8006552},
year  = {2017},
date = {2017-06-01},
booktitle = {2017 IEEE International Symposium on Information Theory (ISIT)},
pages = {371-375},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{8006656,
title = {On the information dimension rate of stochastic processes},
author = {Bernhard C Geiger and Tobias Koch},
doi = {10.1109/ISIT.2017.8006656},
year  = {2017},
date = {2017-06-01},
booktitle = {2017 IEEE International Symposium on Information Theory (ISIT)},
pages = {888-892},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{8006834,
title = {A high-SNR normal approximation for single-antenna Rayleigh block-fading channels},
author = {Alejandro Lancho and Tobias Koch and Giuseppe Durisi},
doi = {10.1109/ISIT.2017.8006834},
year  = {2017},
date = {2017-06-01},
booktitle = {2017 IEEE International Symposium on Information Theory (ISIT)},
pages = {1773-1777},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{7880668,
title = {Probabilistic Equalization With a Smoothing Expectation Propagation Approach},
author = {Irene Santos and Juan Jos\'{e} Murillo-Fuentes and Eva Arias-de-Reyna and Pablo M Olmos},
doi = {10.1109/TWC.2017.2672746},
issn = {1536-1276},
year  = {2017},
date = {2017-05-01},
journal = {IEEE Transactions on Wireless Communications},
volume = {16},
number = {5},
pages = {2950-2962},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{VPGil_2017,
title = {Learning Mobile Communications Standards through Flexible Software Defined Radio Base Stations},
author = {V\'{i}ctor Gil P Jimenez and Alejandro Lancho and Borja Genoves Guzman and Ana Garcia Armada},
doi = {10.1109/MCOM.2017.1601219},
issn = {0163-6804},
year  = {2017},
date = {2017-05-01},
journal = {IEEE Communications Magazine},
volume = {55},
number = {5},
pages = {116-123},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{RamirezMarquesSegarra:2017,
title = {Graph-signal reconstruction and blind deconvolution for diffused sparse inputs},
author = {David Ram\'{i}rez and Antonio G Marques and Santiago Segarra},
year  = {2017},
date = {2017-03-01},
booktitle = {Proc. IEEE Int. Conf. Acoustics, Speech and Signal Process.},
address = {New Orleans, USA},
abstract = {This paper investigates the problems of signal reconstruction and blind deconvolution for graph signals that have been generated by an originally sparse input diffused through the network via the application of a graph filter operator. Assuming that the support of the sparse input signal is unknown, and that the diffused signal is observed only at a subset of nodes, we address the related problems of: 1) identifying the input and 2) interpolating the values of the diffused signal at the non-sampled nodes. We first consider the more tractable case where the coefficients of the diffusing graph filter are known and then address the problem of joint input and filter identification. The corresponding blind identification problems are formulated, novel convex relaxations are discussed, and modifications to incorporate a priori information on the sparse inputs are provided.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Elvira2017,
title = {Improving Population Monte Carlo: Alternative Weighting and Resampling Schemes},
author = {Victor Elvira and Luca Martino and David Luengo and Monica F Bugallo},
url = {http://www.sciencedirect.com/science/article/pii/S0165168416301633},
doi = {10.1016/j.sigpro.2016.07.012},
issn = {01651684},
year  = {2017},
date = {2017-02-01},
journal = {Signal Processing},
volume = {131},
pages = {77--91},
abstract = {Population Monte Carlo (PMC) sampling methods are powerful tools for approximating distributions of static unknowns given a set of observations. These methods are iterative in nature: at each step they generate samples from a proposal distribution and assign them weights according to the importance sampling principle. Critical issues in applying PMC methods are the choice of the generating functions for the samples and the avoidance of the sample degeneracy. In this paper, we propose three new schemes that considerably improve the performance of the original PMC formulation by allowing for better exploration of the space of unknowns and by selecting more adequately the surviving samples. A theoretical analysis is performed, proving the superiority of the novel schemes in terms of variance of the associated estimators and preservation of the sample diversity. Furthermore, we show that they outperform other state of the art algorithms (both in terms of mean square error and robustness w.r.t. initialization) through extensive numerical simulations.},
keywords = {Adaptive importance sampling, Journal, population Monte Carlo, Proposal distribution, Resampling},
pubstate = {published},
tppubtype = {article}
}
@article{Vazquez2017,
title = {A Robust Scheme for Distributed Particle Filtering in Wireless Sensors Networks},
author = {Manuel A V\'{a}zquez and Joaqu\'{i}n M\'{i}guez},
url = {http://www.sciencedirect.com/science/article/pii/S016516841630189X},
doi = {10.1016/j.sigpro.2016.08.003},
issn = {01651684},
year  = {2017},
date = {2017-02-01},
journal = {Signal Processing},
volume = {131},
pages = {190--201},
abstract = {Wireless sensor networks (WSNs) have become a popular technology for a broad range of applications where the goal is to track and forecast the evolution of time-varying physical magnitudes. Several authors have investigated the use of particle filters (PFs) in this scenario. PFs are very flexible, Monte Carlo based algorithms for tracking and prediction in state-space dynamical models. However, to implement a PF in a WSN, the algorithm should run over different nodes in the network to produce estimators based on locally collected data. These local estimators then need to be combined so as to produce a global estimator. Existing approaches to the problem are either heuristic or well-principled but impractical (as they impose stringent conditions on the WSN communication capacity). Here, we introduce a novel distributed PF that relies on the computation of median posterior probability distributions in order to combine local Bayesian estimators (obtained at different nodes) in a way that is efficient, both computation and communication-wise. An extensive simulation study for a target tracking problem shows that the proposed scheme is competitive with existing consensus-based distributed PFs in terms of estimation accuracy, while it clearly outperforms these methods in terms of robustness and communication requirements.},
keywords = {Distributed particle filtering (DPF), Journal, Median posterior, Robust statistics, Sequential Monte Carlo Methods (SMC), Wireless sensors networks (WSNs)},
pubstate = {published},
tppubtype = {article}
}
@article{Yiu2017,
title = {Wireless RSSI Fingerprinting Localization},
author = {Simon Yiu and Marzieh Dashti and Holger Claussen and Fernando Perez-Cruz},
doi = {10.1016/j.sigpro.2016.07.005},
issn = {01651684},
year  = {2017},
date = {2017-02-01},
journal = {Signal Processing},
volume = {131},
pages = {235--244},
abstract = {Localization has attracted a lot of research effort in the last decade due to the explosion of location based service (LBS). In particular, wireless fingerprinting localization has received much attention due to its simplicity and compatibility with existing hardware. In this work, we take a closer look at the underlying aspects of wireless fingerprinting localization. First, we review the various methods to create a radiomap. In particular, we look at the traditional fingerprinting method which is based purely on measurements, the parametric pathloss regression model and the non-parametric Gaussian Process (GP) regression model. Then, based on these three methods and measurements from a real world deployment, the various aspects such as the density of access points (APs) and impact of an outdated signature map which affect the performance of fingerprinting localization are examined. At the end of the paper, the audiences should have a better understanding of what to expect from fingerprinting localization in a real world deployment.},
keywords = {Fingerprinting localization, Gaussian Process, Journal, Location-based service (LBS), Machine learning, Non-parametric model, Pathloss model, Received signal strength indicator (RSSI)},
pubstate = {published},
tppubtype = {article}
}
@article{Santos2017,
title = {Expectation Propagation as Turbo Equalizer in ISI Channels},
author = {Irene Santos and Juan Jose Murillo-Fuentes and Rafael Boloix-Tortosa and Eva Arias-de-Reyna and Pablo M Olmos},
url = {http://ieeexplore.ieee.org/document/7587428/},
doi = {10.1109/TCOMM.2016.2616141},
issn = {0090-6778},
year  = {2017},
date = {2017-01-01},
journal = {IEEE Transactions on Communications},
volume = {65},
number = {1},
pages = {360--370},
abstract = {In probabilistic equalization of channels with inter-symbol interference, the BCJR algorithm and its approximations become intractable for high-order modulations, even for moderate channel dispersions. In this paper, we introduce a novel soft equalizer to approximate the symbol a posteriori probabilities (APP), where the expectation propagation (EP) algorithm is used to provide an accurate estimation. This new soft equalizer is presented as a block solution, denoted as block-EP (BEP), where the structure of the matrices involved is exploited to reduce the complexity order to O(LN2) , i.e., linear in the length of the channel, L , and quadratic in the frame length, N . The solution is presented in complex-valued formulation within a turbo equalization scheme. This algorithm can be cast as a linear minimum-mean-squared-error (LMMSE) turbo equalization with double feedback architecture, where constellations being discrete is a restriction exploited by the EP that provides a first refinement of the APP. In the experiments included, the BEP exhibits a robust performance, regardless of the channel response, with gains in the range 1.5\textendash5 dB compared with the LMMSE equalization.},
keywords = {BCJR, complex-valued, Expectation propagation (EP), ISI, Journal, turbo equalization},
pubstate = {published},
tppubtype = {article}
}
@article{Martino2017,
title = {Cooperative Parallel Particle Filters for Online Model Selection and Applications to Urban Mobility},
author = {Luca Martino and Jesse Read and Victor Elvira and Francisco Louzada},
url = {http://www.sciencedirect.com/science/article/pii/S1051200416301610},
doi = {10.1016/j.dsp.2016.09.011},
issn = {10512004},
year  = {2017},
date = {2017-01-01},
journal = {Digital Signal Processing},
volume = {60},
pages = {172--185},
abstract = {We design a sequential Monte Carlo scheme for the dual purpose of Bayesian inference and model selection. We consider the application context of urban mobility, where several modalities of transport and different measurement devices can be employed. Therefore, we address the joint problem of online tracking and detection of the current modality. For this purpose, we use interacting parallel particle filters, each one addressing a different model. They cooperate for providing a global estimator of the variable of interest and, at the same time, an approximation of the posterior density of each model given the data. The interaction occurs by a parsimonious distribution of the computational effort, with online adaptation for the number of particles of each filter according to the posterior probability of the corresponding model. The resulting scheme is simple and flexible. We have tested the novel technique in different numerical experiments with artificial and real data, which confirm the robustness of the proposed scheme.},
keywords = {Distributed inference, Journal, Marginal likelihood estimation, Modality detection, Parallel particle filters, Sequential model selection, Urban mobility},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{RiosMunoz2017a,
title = {Substrate Characterization of Rotational Activity Sites in Persistent Atrial Fibrillation Patients},
author = {Gonzalo R\'{i}os-Mu\~{n}oz and Pablo Ruiz M Hernandez and Evaristo Castellanos and Pablo \'{A}vila and Gerard Loughlin and Francisco Fernandez-Aviles and Antonio Art\'{e}s-Rodr\'{i}guez and \'{A}ngel Arenal},
year  = {2017},
date = {2017-01-01},
booktitle = {CNIC Conference Atrial Fibrillation: From Mechanisms to Population Science},
address = {Madrid},
abstract = {The underlying mechanisms initiating and sustaining atrial fibrillation (AF) are still under debate, and an optimal treatment for AF is not been well established. Spatiotemporal stable sources (rotors) have been proposed as maintenance mechanism of AF. The use of high density electroanatomical mapping with microelectrodes (HDEMM) and a novel rotational activity detection system we can detect rotors and characterize the tissue where rotors are located. The analysis shows evidence of voltage values related to rotational activity beyond bipolar voltage range 0.1-0.5 mV, classically considered for scar definitions. Functional assessment may add incremental value to invasive treatment of AF.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{RiosMunoz2017b,
title = {Presence and Voltage Characterization of Rotational Activity in Atrial Fibrillation Patients},
author = {Gonzalo R\'{i}os-Mu\~{n}oz and Pablo Ruiz M Hernandez and Evaristo Castellanos and Pablo \'{A}vila and Gerard Loughlin and Francisco Fernandez-Aviles and Antonio Art\'{e}s-Rodr\'{i}guez and \'{A}ngel Arenal},
year  = {2017},
date = {2017-01-01},
booktitle = {Atrial Signals 2017},
address = {Valencia},
abstract = {The underlying mechanisms initiating and sustaining atrial fibrillation (AF) are still under debate, and an optimal treatment for AF is not yet established. Spatiotemporal stable sources (rotors) have been proposed as maintenance mechanism of AF. Using high density electroanatomical mapping with microelectrodes (HDEMM) and a novel rotational activity detection system we are able to detect rotors and characterize the tissue where rotors are located. The analysis shows evidence of voltage values related to rotational activity beyond the bipolar voltage range 0.1-0.5 mV, classically considered for scar definitions. Functional assessment may add incremental value to invasive treatment of AF.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{MartinoRG2017,
title = {The Recycling Gibbs sampler for efficient learning},
author = {Luca Martino and Victor Elvira and Gustavo Camps-Valls},
year  = {2017},
date = {2017-01-01},
journal = {Digital Signal Processing},
volume = {74},
pages = {1 -13},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{7974876,
title = {Adaptive Importance Sampling: The past, the present, and the future},
author = {Monica F Bugallo and Victor Elvira and Luca Martino and David Luengo and Joaquin Miguez and Petar M Djuric},
year  = {2017},
date = {2017-01-01},
journal = {IEEE Signal Processing Magazine},
volume = {34},
number = {4},
pages = {60-79},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Ruiz2017b,
title = {Presence and Distribution of Rotational Conduction Points and Its Association With Scar in Patients With Persistent Atrial Fibrillation},
author = {Pablo Ruiz M Hernandez and Gonzalo R\'{i}os-Mu\~{n}oz and Evaristo Castellanos and Pablo \'{A}vila and Esteban G Torrecilla and Gerard Loughlin and Tomas Datino and Felipe Atienza and Francisco Fernandez-Aviles and Antonio Art\'{e}s-Rodr\'{i}guez and \'{A}ngel Arenal},
year  = {2017},
date = {2017-01-01},
booktitle = {Hear. Rhythm},
volume = {14},
number = {5},
pages = {S235----S236},
organization = {Elsevier},
abstract = {Background: Persistent AF has shown high post-ablation recurrence rates, and left atrial (LA) fibrosis is a possible cause. Extrapulmonary rotational activation (rotors), could be related to fibrosis. High density electroanatomical mapping with microelectrodes (HDEMM) can allow better LA tissue characterization and rotor identification. Objetive: To assess the presence and distribution of rotors, and their relationship with areas of LA scar in patients with persistent AF. Conclusion: Rotational conduction is observed, to some degree, in the majority of sites evaluated in patients with persistent AF. Gyre complexity was greater in areas of higher voltage, and does not seem to be associated with any particular LA location.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{8234658,
title = {Probabilistic MIMO Symbol Detection with Expectation Consistency Approximate Inference},
author = {Javier C\'{e}spedes and Pablo M Olmos and Matilde Sanchez-Fernandez and Fernando Perez-Cruz},
doi = {10.1109/TVT.2017.2786638},
issn = {0018-9545},
year  = {2017},
date = {2017-01-01},
journal = {IEEE Transactions on Vehicular Technology},
volume = {PP},
number = {99},
pages = {1-1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{crisan_m\'{i}guez_2017,
title = {Uniform convergence over time of a nested particle filtering scheme for recursive parameter estimation in state-space Markov models},
author = {Dan Crisan and Joaqu\'{i}n M\'{i}guez},
doi = {10.1017/apr.2017.38},
year  = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {Advances in Applied Probability},
volume = {49},
number = {4},
pages = {1170\textendash1200},
publisher = {Cambridge University Press},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{articleb,
title = {Importance sampling with transformed weights},
author = {Manuel A V\'{a}zquez and Joaqu\'{i}n M\'{i}guez},
doi = {10.1049/el.2016.3462},
year  = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {Electronics Letters},
volume = {53},
number = {12},
pages = {783-785},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@conference{54602a23b73d4999ab3f04e075a85228,
title = {Robust Inference by Particle Filtering},
author = {Victor Elvira and Joaqu\'{i}n M\'{i}guez and Petar M Djuric},
year  = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
abstract = {Particle filters (PFs) are recursive Monte Carlo methods for online tracking and forecasting in state-space systems. They are very general and, hence, can be used with a broad class of models, including ones that are nonlinear and/or non-Gaussian. PFs suffer from a number of drawbacks including their computational complexity and sensitivity to the choice of the state space model (i.e., its compatibility with the observed data). Indeed, modelling errors and sharp changes in the dynamics of the state or the observation processesthat are not accounted for usually lead to a degradation of the performance of the PFs. In this paper we draw from recent results on online assessment of convergence of PFs to propose a simple scheme to (a) detect changes in a sate-space model from a series of observations that are described by the model and (b) re-estimate the model to make it compatible with the observed data. The detection stage is fully general, as it relies on a model-invariant statistic, while re-estimation can be done in several manners. Here, we discuss possible schemes and illustrate the theory with a simple example for a conditionally-linear Gaussian model.},
note = {61st ISI World Statistics Congress, ISI 2017 ; Conference date: 17-07-2017 Through 21-07-2017},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{e317f81741974bd1a806dad8bb4604e0,
title = {Recent advances in adaptive sequential Monte Carlo methods},
author = {Victor Elvira and Joaqu\'{i}n M\'{i}guez and Petar M Djuric},
year  = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
note = {Sixteenth International Conference on Computer Aided Systems Theory , Eurocast 2017 ; Conference date: 19-02-2017 Through 24-02-2017},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@inproceedings{8006834b,
title = {A high-SNR normal approximation for single-antenna Rayleigh block-fading channels},
author = {Alejandro Lancho and Tobias Koch and Giuseppe Durisi},
doi = {10.1109/ISIT.2017.8006834},
year  = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
booktitle = {2017 IEEE International Symposium on Information Theory (ISIT)},
pages = {1773-1777},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Pradier2016,
title = {Entropy-Constrained Scalar Quantization with a Lossy-Compressed Bit},
author = {Melanie F. Pradier and Pablo M Olmos and Fernando Perez-Cruz},
url = {http://www.mdpi.com/1099-4300/18/12/449},
doi = {10.3390/e18120449},
issn = {1099-4300},
year  = {2016},
date = {2016-12-01},
journal = {Entropy},
volume = {18},
number = {12},
pages = {449},
publisher = {Multidisciplinary Digital Publishing Institute},
abstract = {We consider the compression of a continuous real-valued source X using scalar quantizers and average squared error distortion D. Using lossless compression of the quantizer's output, Gish and Pierce showed that uniform quantizing yields the smallest output entropy in the limit D → 0 , resulting in a rate penalty of 0.255 bits/sample above the Shannon Lower Bound (SLB). We present a scalar quantization scheme named lossy-bit entropy-constrained scalar quantization (Lb-ECSQ) that is able to reduce the D → 0 gap to SLB to 0.251 bits/sample by combining both lossless and binary lossy compression of the quantizer's output. We also study the low-resolution regime and show that Lb-ECSQ significantly outperforms ECSQ in the case of 1-bit quantization.},
keywords = {Journal, scalar quantization, source coding},
pubstate = {published},
tppubtype = {article}
}
@article{Vazquez2016,
title = {On the Use of the Channel Second-Order Statistics in MMSE Receivers for Time- and Frequency-Selective MIMO Transmission Systems},
author = {Manuel A V\'{a}zquez and Joaqu\'{i}n M\'{i}guez},
url = {http://jwcn.eurasipjournals.springeropen.com/articles/10.1186/s13638-016-0768-0},
doi = {10.1186/s13638-016-0768-0},
year  = {2016},
date = {2016-12-01},
journal = {EURASIP Journal on Wireless Communications and Networking},
volume = {2016},
number = {1},
publisher = {Springer International Publishing},
abstract = {Equalization of unknown frequency- and time-selective multiple input multiple output (MIMO) channels is often carried out by means of decision feedback receivers. These consist of a channel estimator and a linear filter (for the estimation of the transmitted symbols), interconnected by a feedback loop through a symbol-wise threshold detector. The linear filter is often a minimum mean square error (MMSE) filter, and its mathematical expression involves second-order statistics (SOS) of the channel, which are usually ignored by simply assuming that the channel is a known (deterministic) parameter given by an estimate thereof. This appears to be suboptimal and in this work we investigate the kind of performance gains that can be expected when the MMSE equalizer is obtained using SOS of the channel process. As a result, we demonstrate that improvements of several dBs in the signal-to-noise ratio needed to achieve a prescribed symbol error rate are possible.},
keywords = {data estimation, Joint channel, Journal, MIMO, MMSE, Second-order statistics},
pubstate = {published},
tppubtype = {article}
}
@article{gvazquez16,
title = {Multiantenna GLR Detection of Rank-One Signals with a Known Power Spectral Shape under Spatially Uncorrelated Noise},
author = {Josep Sala and Gonzalo Vazquez-Vilar and Roberto L\'{o}pez-Valcarce and Saeid Sedighi and Abbas Taherpour},
doi = {10.1109/TSP.2016.2601290},
issn = {1053-587X},
year  = {2016},
date = {2016-12-01},
journal = {IEEE Transactions on Signal Processing},
volume = {64},
number = {23},
pages = {6269-6283},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Martino2016b,
title = {Orthogonal Parallel MCMC Methods for Sampling and Optimization},
author = {Luca Martino and Victor Elvira and David Luengo and Jukka Corander and Francisco Louzada},
url = {http://www.sciencedirect.com/science/article/pii/S1051200416300987},
doi = {10.1016/j.dsp.2016.07.013},
issn = {10512004},
year  = {2016},
date = {2016-11-01},
journal = {Digital Signal Processing},
volume = {58},
pages = {64--84},
abstract = {Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In order to foster better exploration of the state space, specially in high-dimensional applications, several schemes employing multiple parallel MCMC chains have been recently introduced. In this work, we describe a novel parallel interacting MCMC scheme, called orthogonal MCMC (O-MCMC), where a set of “vertical” parallel MCMC chains share information using some “horizontal” MCMC techniques working on the entire population of current states. More specifically, the vertical chains are led by random-walk proposals, whereas the horizontal MCMC techniques employ independent proposals, thus allowing an efficient combination of global exploration and local approximation. The interaction is contained in these horizontal iterations. Within the analysis of different implementations of O-MCMC, novel schemes in order to reduce the overall computational cost of parallel multiple try Metropolis (MTM) chains are also presented. Furthermore, a modified version of O-MCMC for optimization is provided by considering parallel simulated annealing (SA) algorithms. Numerical results show the advantages of the proposed sampling scheme in terms of efficiency in the estimation, as well as robustness in terms of independence with respect to initial values and the choice of the parameters.},
keywords = {Bayesian inference, Block Independent Metropolis, Journal, Optimization, Parallel Markov Chain Monte Carlo, Parallel Multiple Try Metropolis, Parallel Simulated Annealing, Recycling samples},
pubstate = {published},
tppubtype = {article}
}
@article{Koch2016b,
title = {The Shannon Lower Bound Is Asymptotically Tight},
author = {Tobias Koch},
url = {http://ieeexplore.ieee.org/document/7556344/},
doi = {10.1109/TIT.2016.2604254},
issn = {0018-9448},
year  = {2016},
date = {2016-11-01},
journal = {IEEE Transactions on Information Theory},
volume = {62},
number = {11},
pages = {6155--6161},
abstract = {The Shannon lower bound is one of the few lower bounds on the rate-distortion function that holds for a large class of sources. In this paper, which considers exclusively norm-based difference distortion measures, it is demonstrated that its gap to the rate-distortion function vanishes as the allowed distortion tends to zero for all sources having finite differential entropy and whose integer part has finite entropy. Conversely, it is demonstrated that if the integer part of the source has infinite entropy, then its rate-distortion function is infinite for every finite distortion level. Thus, the Shannon lower bound provides an asymptotically tight bound on the rate-distortion function if, and only if, the integer part of the source has finite entropy.},
keywords = {Journal, R{\'{e}}nyi information dimension, Rate-distortion theory, Shannon lower bound},
pubstate = {published},
tppubtype = {article}
}
@article{Song2016,
title = {Canonical Correlation Analysis of High-Dimensional Data With Very Small Sample Support},
author = {Yang Song and Peter J Schreier and David Ram\'{i}rez and Tanuj Hasija},
url = {http://www.sciencedirect.com/science/article/pii/S0165168416300834},
doi = {10.1016/j.sigpro.2016.05.020},
issn = {01651684},
year  = {2016},
date = {2016-11-01},
journal = {Signal Processing},
volume = {128},
pages = {449--458},
abstract = {This paper is concerned with the analysis of correlation between two high-dimensional data sets when there are only few correlated signal components but the number of samples is very small, possibly much smaller than the dimensions of the data. In such a scenario, a principal component analysis (PCA) rank-reduction preprocessing step is commonly performed before applying canonical correlation analysis (CCA). We present simple, yet very effective, approaches to the joint model-order selection of the number of dimensions that should be retained through the PCA step and the number of correlated signals. These approaches are based on reduced-rank versions of the Bartlett\textendashLawley hypothesis test and the minimum description length information-theoretic criterion. Simulation results show that the techniques perform well for very small sample sizes even in colored noise.},
keywords = {Bartlett-Lawley statistic, Canonical correlation analysis, Journal, Model-order selection, Principal component analysis, Small sample support},
pubstate = {published},
tppubtype = {article}
}
@article{Bocharova2016,
title = {Multi-Class Source-Channel Coding},
author = {Irina E Bocharova and Albert Guill\'{e}n i F\`{a}bregas and Boris D Kudryashov and Alfonso Martinez and Adria Tauste Campo and Gonzalo Vazquez-Vilar},
url = {http://arxiv.org/abs/1410.8714},
year  = {2016},
date = {2016-09-01},
journal = {IEEE Transactions on Information Theory},
volume = {62},
number = {9},
pages = {5093 -- 5104},
abstract = {This paper studies an almost-lossless source-channel coding scheme in which source messages are assigned to different classes and encoded with a channel code that depends on the class index. The code performance is analyzed by means of random-coding error exponents and validated by simulation of a low-complexity implementation using existing source and channel codes. While each class code can be seen as a concatenation of a source code and a channel code, the overall performance improves on that of separate source-channel coding and approaches that of joint source-channel coding when the number of classes increases.},
keywords = {Channel Coding, Complexity theory, error probability, Indexes, Journal, Maximum likelihood decoding},
pubstate = {published},
tppubtype = {article}
}
@article{Durisi2016a,
title = {Towards Massive, Ultra-Reliable, and Low-Latency Wireless Communication with Short Packets},
author = {Giuseppe Durisi and Tobias Koch and Petar Popovski},
url = {http://arxiv.org/abs/1504.06526},
year  = {2016},
date = {2016-09-01},
journal = {Proceedings of the IEEE},
volume = {104},
number = {9},
pages = {1711 -- 1726},
abstract = {Most of the recent advances in the design of high-speed wireless systems are based on information-theoretic principles that demonstrate how to efficiently transmit long data packets. However, the upcoming wireless systems, notably the 5G system, will need to support novel traffic types that use short packets. For example, short packets represent the most common form of traffic generated by sensors and other devices involved in Machine-to-Machine (M2M) communications. Furthermore, there are emerging applications in which small packets are expected to carry critical information that should be received with low latency and ultra-high reliability. Current wireless systems are not designed to support short-packet transmissions. For example, the design of current systems relies on the assumption that the metadata (control information) is of negligible size compared to the actual information payload. Hence, transmitting metadata using heuristic methods does not affect the overall system performance. However, when the packets are short, metadata may be of the same size as the payload, and the conventional methods to transmit it may be highly suboptimal. In this article, we review recent advances in information theory, which provide the theoretical principles that govern the transmission of short packets. We then apply these principles to three exemplary scenarios (the two-way channel, the downlink broadcast channel, and the uplink random access channel), thereby illustrating how the transmission of control information can be optimized when the packets are short. The insights brought by these examples suggest that new principles are needed for the design of wireless protocols supporting short packets. These principles will have a direct impact on the system design.},
keywords = {finite blocklength, Journal, massive M2M communication, short packets, ultrareliable communication (URC), Wireless 5G systems},
pubstate = {published},
tppubtype = {article}
}
@article{Nazabal2016b,
title = {Human Activity Recognition by Combining a Small Number of Classifiers.},
author = {Alfredo Naz\'{a}bal and Pablo Garcia-Moreno and Antonio Art\'{e}s-Rodr\'{i}guez and Zoubin Ghahramani},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7161292},
doi = {10.1109/JBHI.2015.2458274},
issn = {2168-2208},
year  = {2016},
date = {2016-09-01},
journal = {IEEE journal of biomedical and health informatics},
volume = {20},
number = {5},
pages = {1342 -- 1351},
publisher = {IEEE},
abstract = {We consider the problem of daily Human Activity Recognition (HAR) using multiple wireless inertial sensors and, specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semi-supervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and a Markovian structure of the human activities.},
keywords = {Bayes methods, Bayesian inference, Biological system modeling, Classifier combination, Databases, Estimation, Hidden Markov models, Journal, Sensor systems},
pubstate = {published},
tppubtype = {article}
}
@article{Valera2016b,
title = {Infinite Factorial Unbounded-State Hidden Markov Model},
author = {Isabel Valera and Francisco J R Ruiz and Fernando Perez-Cruz},
url = {http://www.ncbi.nlm.nih.gov/pubmed/26571511 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true\&amp;amp;arnumber=7322279},
doi = {10.1109/TPAMI.2015.2498931},
issn = {1939-3539},
year  = {2016},
date = {2016-09-01},
journal = {IEEE transactions on pattern analysis and machine intelligence},
volume = {38},
number = {9},
pages = {1816 -- 1828},
abstract = {There are many scenarios in artificial intelligence, signal processing or medicine, in which a temporal sequence consists of several unknown overlapping independent causes, and we are interested in accurately recovering those canonical causes. Factorial hidden Markov models (FHMMs) present the versatility to provide a good fit to these scenarios. However, in some scenarios, the number of causes or the number of states of the FHMM cannot be known or limited a priori. In this paper, we propose an infinite factorial unbounded-state hidden Markov model (IFUHMM), in which the number of parallel hidden Markov models (HMMs) and states in each HMM are potentially unbounded. We rely on a Bayesian nonparametric (BNP) prior over integer-valued matrices, in which the columns represent the Markov chains, the rows the time indexes, and the integers the state for each chain and time instant. First, we extend the existent infinite factorial binary-state HMM to allow for any number of states. Then, we modify this model to allow for an unbounded number of states and derive an MCMC-based inference algorithm that properly deals with the trade-off between the unbounded number of states and chains. We illustrate the performance of our proposed models in the power disaggregation problem.},
keywords = {Bayes methods, Bayesian nonparametrics, CASI CAM CM, Computational modeling, GAMMA-L+ UC3M, Gibbs sampling, Hidden Markov models, Inference algorithms, Journal, Markov processes, Probability distribution, reversible jump Markov chain Monte Carlo, slice sampling, Time series, variational inference, Yttrium},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{7541766,
title = {Wireless networks of bounded capacity},
author = {Grace Villacr\'{e}s and Tobias Koch},
doi = {10.1109/ISIT.2016.7541766},
year  = {2016},
date = {2016-07-01},
booktitle = {2016 IEEE International Symposium on Information Theory (ISIT)},
pages = {2584-2588},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{7541396,
title = {A general rate-distortion converse bound for entropy-constrained scalar quantization},
author = {Tobias Koch and Gonzalo Vazquez-Vilar},
doi = {10.1109/ISIT.2016.7541396},
year  = {2016},
date = {2016-07-01},
booktitle = {2016 IEEE International Symposium on Information Theory (ISIT)},
pages = {735-739},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{gvazquez-isit2016a,
title = {Multiple quantum hypothesis testing expressions and classical-quantum channel converse bounds},
author = {Gonzalo Vazquez-Vilar},
year  = {2016},
date = {2016-07-01},
booktitle = {2016 IEEE International Symposium on Information Theory (ISIT 2016)},
address = {Barcelona, Spain},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Vazquez-Vilar2016,
title = {Bayesian M-Ary Hypothesis Testing: The Meta-Converse and Verd\'{u}-Han Bounds Are Tight},
author = {Gonzalo Vazquez-Vilar and Adria Tauste Campo and Albert Guillen i Fabregas and Alfonso Martinez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7434042},
doi = {10.1109/TIT.2016.2542080},
issn = {0018-9448},
year  = {2016},
date = {2016-05-01},
journal = {IEEE Transactions on Information Theory},
volume = {62},
number = {5},
pages = {2324--2333},
abstract = {Two alternative exact characterizations of the minimum error probability of Bayesian M-ary hypothesis testing are derived. The first expression corresponds to the error probability of an induced binary hypothesis test and implies the tightness of the meta-converse bound by Polyanskiy et al.; the second expression is a function of an information-spectrum measure and implies the tightness of a generalized Verd\'{u}-Han lower bound. The formulas characterize the minimum error probability of several problems in information theory and help to identify the steps where existing converse bounds are loose.},
keywords = {Bayes methods, Channel Coding, Electronic mail, error probability, Journal, Random variables, Testing},
pubstate = {published},
tppubtype = {article}
}
@article{Miguez2016,
title = {A Proof of Uniform Convergence Over Time for a Distributed Particle Filter},
author = {Joaqu\'{i}n M\'{i}guez and Manuel A V\'{a}zquez},
url = {http://www.sciencedirect.com/science/article/pii/S0165168415004077},
doi = {10.1016/j.sigpro.2015.11.015},
issn = {01651684},
year  = {2016},
date = {2016-05-01},
journal = {Signal Processing},
volume = {122},
pages = {152--163},
abstract = {Distributed signal processing algorithms have become a hot topic during the past years. One class of algorithms that have received special attention are particles filters (PFs). However, most distributed PFs involve various heuristic or simplifying approximations and, as a consequence, classical convergence theorems for standard PFs do not hold for their distributed counterparts. In this paper, we analyze a distributed PF based on the non-proportional weight-allocation scheme of Bolic et al (2005) and prove rigorously that, under certain stability assumptions, its asymptotic convergence is guaranteed uniformly over time, in such a way that approximation errors can be kept bounded with a fixed computational budget. To illustrate the theoretical findings, we carry out computer simulations for a target tracking problem. The numerical results show that the distributed PF has a negligible performance loss (compared to a centralized filter) for this problem and enable us to empirically validate the key assumptions of the analysis.},
keywords = {Convergence analysis, Distributed algorithms, Journal, Parallelization, Particle filtering, Wireless Sensor Networks},
pubstate = {published},
tppubtype = {article}
}
@article{Koblents2016,
title = {A Nonlinear Population Monte Carlo Scheme for the Bayesian Estimation of Parameters of α-stable Distributions},
author = {Eugenia Koblents and Joaqu\'{i}n M\'{i}guez and Marco A Rodr\'{i}guez and Alexandra M Schmidt},
url = {http://www.sciencedirect.com/science/article/pii/S0167947315002340},
doi = {10.1016/j.csda.2015.09.007},
issn = {01679473},
year  = {2016},
date = {2016-03-01},
journal = {Computational Statistics \&amp;amp; Data Analysis},
volume = {95},
pages = {57--74},
abstract = {The class of $alpha$-stable distributions enjoys multiple practical applications in signal processing, finance, biology and other areas because it allows to describe interesting and complex data patterns, such as asymmetry or heavy tails, in contrast with the simpler and widely used Gaussian distribution. The density associated with a general $alpha$-stable distribution cannot be obtained in closed form, which hinders the process of estimating its parameters. A nonlinear population Monte Carlo (NPMC) scheme is applied in order to approximate the posterior probability distribution of the parameters of an $alpha$-stable random variable given a set of random realizations of the latter. The approximate posterior distribution is computed by way of an iterative algorithm and it consists of a collection of samples in the parameter space with associated nonlinearly-transformed importance weights. A numerical comparison of the main existing methods to estimate the $alpha$-stable parameters is provided, including the traditional frequentist techniques as well as a Markov chain Monte Carlo (MCMC) and a likelihood-free Bayesian approach. It is shown by means of computer simulations that the NPMC method outperforms the existing techniques in terms of parameter estimation error and failure rate for the whole range of values of $alpha$, including the smaller values for which most existing methods fail to work properly. Furthermore, it is shown that accurate parameter estimates can often be computed based on a low number of observations. Additionally, numerical results based on a set of real fish displacement data are provided.},
keywords = {Animal movement, Bayesian inference, Importance sampling, L{\'{e}}vy process, α-stable distributions},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Pries2016,
title = {Detection of Cyclostationarity in the Presence of Temporal or Spatial Structure with Applications to Cognitive Radio},
author = {Aaron Pries and David Ram\'{i}rez and Peter J Schreier},
url = {http://www.icassp2016.org/Papers/ViewPapers.asp?PaperNum=1789},
year  = {2016},
date = {2016-03-01},
booktitle = {Proc. IEEE Int. Conf. Acoustics, Speech and Signal Process., (ICASSP 2016)},
address = {Shanghai},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Koch2016bb,
title = {A Necessary and Sufficient Condition for the Asymptotic Tightness of the Shannon Lower Bound},
author = {Tobias Koch},
year  = {2016},
date = {2016-03-01},
booktitle = {International Zurich Seminar on Communications},
address = {Zurich},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Elvira2016,
title = {Online Adaptation of the Number of Particles of Sequential Monte Carlo Methods},
author = {Victor Elvira and Joaquin Miguez and Petar M Djuric},
year  = {2016},
date = {2016-03-01},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2016)},
address = {Shanghai},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Martino2016bb,
title = {Parallel Metropolis Chains with Cooperative Adaptation},
author = {Luca Martino and Victor Elvira and David Luengo and Francisco Louzada},
url = {http://www.icassp2016.org/Papers/ViewPapers.asp?PaperNum=3747},
year  = {2016},
date = {2016-03-01},
booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2016)},
address = {Shanghai},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Martin-Fernandez2015b,
title = {A Bayesian Method for Model Selection in Environmental Noise Prediction},
author = {L Mart\'{i}n-Fern\'{a}ndez and D P Ruiz and A J Torija and Joaquin Miguez},
url = {http://www.researchgate.net/publication/268213140_A_Bayesian_method_for_model_selection_in_environmental_noise_prediction},
issn = {1726-2135},
year  = {2016},
date = {2016-03-01},
journal = {Journal of Environmental Informatics},
volume = {27},
number = {1},
pages = {31--42},
abstract = {Environmental noise prediction and modeling are key factors for addressing a proper planning and management of urban sound environments. In this paper we propose a maximum a posteriori (MAP) method to compare nonlinear state-space models that describe the problem of predicting environmental sound levels. The numerical implementation of this method is based on particle filtering and we use a Markov chain Monte Carlo technique to improve the resampling step. In order to demonstrate the validity of the proposed approach for this particular problem, we have conducted a set of experiments where two prediction models are quantitatively compared using real noise measurement data collected in different urban areas.},
keywords = {Journal},
pubstate = {published},
tppubtype = {article}
}
@article{Martino2016a,
title = {Layered adaptive importance sampling},
author = {Luca Martino and Victor Elvira and David Luengo and Jukka Corander},
url = {http://link.springer.com/10.1007/s11222-016-9642-5},
doi = {10.1007/s11222-016-9642-5},
issn = {0960-3174},
year  = {2016},
date = {2016-03-01},
journal = {Statistics and Computing},
pages = {1--25},
publisher = {Springer US},
abstract = {Monte Carlo methods represent the de facto standard for approximating complicated integrals involving multidimensional target distributions. In order to generate random realizations from the target distribution, Monte Carlo techniques use simpler proposal probability densities to draw candidate samples. The performance of any such method is strictly related to the specification of the proposal distribution, such that unfortunate choices easily wreak havoc on the resulting estimators. In this work, we introduce a layered (i.e., hierarchical) procedure to generate samples employed within a Monte Carlo scheme. This approach ensures that an appropriate equivalent proposal density is always obtained automatically (thus eliminating the risk of a catastrophic performance), although at the expense of a moderate increase in the complexity. Furthermore, we provide a general unified importance sampling (IS) framework, where multiple proposal densities are employed and several IS schemes are introduced by applying the so-called deterministic mixture approach. Finally, given these schemes, we also propose a novel class of adaptive importance samplers using a population of proposals, where the adaptation is driven by independent parallel or interacting Markov chain Monte Carlo (MCMC) chains. The resulting algorithms efficiently combine the benefits of both IS and MCMC methods.},
keywords = {Bayesian inference Adaptive importance sampling Po, Journal},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{gvazquez-izs2016,
title = {Hypothesis testing and quasi-perfect codes},
author = {Gonzalo Vazquez-Vilar and Albert Guill\'{e}n i F\`{a}bregas and Sergio Verd\'{u}},
year  = {2016},
date = {2016-03-01},
booktitle = {2016 International Zurich Seminar on Communications (IZS 2016)},
address = {Zurich, Switzerland},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Luengo2016b,
title = {A hierarchical algorithm for causality discovery among atrial fibrillation electrograms},
author = {David Luengo and Gonzalo R\'{i}os-Mu\~{n}oz and Victor Elvira and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/document/7471780/},
doi = {10.1109/ICASSP.2016.7471780},
isbn = {978-1-4799-9988-0},
year  = {2016},
date = {2016-03-01},
booktitle = {2016 IEEE Int. Conf. Acoust. Speech Signal Process.},
pages = {774--778},
publisher = {IEEE},
abstract = {Multi-channel intracardiac electrocardiograms (electrograms) are sequentially acquired, at the electrophysiology laboratory, in order to guide radio frequency catheter ablation during heart surgery performed on patients with sustained atrial fibrillation (AF). These electrograms are used by cardiologists to determine candidate areas for ablation (e.g., areas corresponding to high dominant frequencies or complex fractionated electrograms). In this paper, we introduce a novel hierarchical algorithm for causality discovery among these multi-output sequentially acquired electrograms. The causal model obtained provides important information about the propagation of the electrical signals inside the heart, uncovering wavefronts and activation patterns that will serve to increase our knowledge about AF and guide cardiologists towards candidate areas for catheter ablation. Numerical results on synthetic signals, generated using the FitzHugh-Nagumo model, show the good performance of the proposed approach.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Stinner2016,
title = {On the Waterfall Performance of Finite-Length SC-LDPC Codes Constructed From Protographs},
author = {Markus Stinner and Pablo M Olmos},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7339427},
doi = {10.1109/JSAC.2015.2504279},
issn = {0733-8716},
year  = {2016},
date = {2016-02-01},
journal = {IEEE Journal on Selected Areas in Communications},
volume = {34},
number = {2},
pages = {345--361},
abstract = {An analysis of spatially coupled low-density parity-check (SC-LDPC) codes constructed from protographs is proposed. Given the protograph used to generate the SC-LDPC code ensemble, a set of scaling parameters to characterize the average finite-length performance in the waterfall region is computed. The error performance of structured SC-LDPC code ensembles is shown to follow a scaling law similar to that of unstructured randomly constructed SC-LDPC codes. Under a finite-length perspective, some of the most relevant SC-LDPC protograph structures proposed to date are compared. The analysis reveals significant differences in their finite-length scaling behavior, which is corroborated by simulation. Spatially coupled repeat-accumulate codes present excellent finite-length performance, as they outperform in the waterfall region SC-LDPC codes of the same rate and better asymptotic thresholds.},
keywords = {Analytical models, capacity-achieving codes, Complexity theory, Couplings, Decoding, Encoding, finite-length analysis, Iterative decoding, Low-density parity-check (LDPC) codes, spatially coupled LDPC codes constructed from prot},
pubstate = {published},
tppubtype = {article}
}
@article{Valera2016ab,
title = {Infinite Continuous Feature Model for Psychiatric Comorbidity Analysis.},
author = {Isabel Valera and Francisco J R Ruiz and Pablo M Olmos and Carlos Blanco and Fernando Perez-Cruz},
url = {http://www.ncbi.nlm.nih.gov/pubmed/26654208},
doi = {10.1162/NECO_a_00805},
issn = {1530-888X},
year  = {2016},
date = {2016-02-01},
journal = {Neural computation},
volume = {28},
number = {2},
pages = {354--381},
abstract = {We aim at finding the comorbidity patterns of substance abuse, mood and personality disorders using the diagnoses from the National Epidemiologic Survey on Alcohol and Related Conditions database. To this end, we propose a novel Bayesian nonparametric latent feature model for categorical observations, based on the Indian buffet process, in which the latent variables can take values between 0 and 1. The proposed model has several interesting features for modeling psychiatric disorders. First, the latent features might be off, which allows distinguishing between the subjects who suffer a condition and those who do not. Second, the active latent features take positive values, which allows modeling the extent to which the patient has that condition. We also develop a new Markov chain Monte Carlo inference algorithm for our model that makes use of a nested expectation propagation procedure.},
keywords = {Journal},
pubstate = {published},
tppubtype = {article}
}
@article{Durisi2016b,
title = {Short-Packet Communications Over Multiple-Antenna Rayleigh-Fading Channels},
author = {Giuseppe Durisi and Tobias Koch and Johan Ostman and Yury Polyanskiy and Wei Yang},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7362178},
doi = {10.1109/TCOMM.2015.2511087},
issn = {0090-6778},
year  = {2016},
date = {2016-02-01},
journal = {IEEE Transactions on Communications},
volume = {64},
number = {2},
pages = {618--629},
publisher = {IEEE},
abstract = {Motivated by the current interest in ultra-reliable, low-latency, machine-type communication systems, we investigate the tradeoff between reliability, throughput, and latency in the transmission of information over multiple-antenna Rayleigh block-fading channels. Specifically, we obtain finite-blocklength, finite-SNR upper and lower bounds on the maximum coding rate achievable over such channels for a given constraint on the packet error probability. Numerical evidence suggests that our bounds delimit tightly the maximum coding rate already for short blocklengths (packets of about 100 symbols). Furthermore, our bounds reveal the existence of a tradeoff between the rate gain obtainable by spreading each codeword over all available time-frequency-spatial degrees of freedom, and the rate loss caused by the need of estimating the fading coefficients over these degrees of freedom. In particular, our bounds allow us to determine the optimal number of transmit antennas and the optimal number of time-frequency diversity branches that maximize the rate. Finally, we show that infinite-blocklength performance metrics such as the ergodic capacity and the outage capacity yield inaccurate throughput estimates},
keywords = {diversity branches, Encoding, ergodic capacity, Fading, fading channels, finite-blocklength information theory, finiteblocklength information theory, infinite-blocklength performance metrics, Journal, machine-type communication systems, maximum coding rate, Mission critical systems, mission-critical machine-type communications, multiple antennas, multiple-antenna Rayleigh block-fading channels, Multiplexing, optimal number, outage capacity, rate gain, Rayleigh channels, Receivers, Reliability, short-packet communications, spatial multiplexing, Throughput, Time-frequency analysis, time-frequency-spatial degrees of freedom, transmit antennas, transmit diversity, Transmitting antennas, Ultra-reliable low-latency communications},
pubstate = {published},
tppubtype = {article}
}
@article{Valera2016c,
title = {Infinite Factorial Unbounded-State Hidden Markov Model},
author = {Isabel Valera and Francisco J R Ruiz and Fernando Perez-Cruz},
url = {http://www.ncbi.nlm.nih.gov/pubmed/26571511 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true\&amp;amp;arnumber=7322279},
doi = {10.1109/TPAMI.2015.2498931},
issn = {1939-3539},
year  = {2016},
date = {2016-01-01},
journal = {IEEE transactions on pattern analysis and machine intelligence},
volume = {To appear},
number = {99},
pages = {1},
abstract = {There are many scenarios in artificial intelligence, signal processing or medicine, in which a temporal sequence consists of several unknown overlapping independent causes, and we are interested in accurately recovering those canonical causes. Factorial hidden Markov models (FHMMs) present the versatility to provide a good fit to these scenarios. However, in some scenarios, the number of causes or the number of states of the FHMM cannot be known or limited a priori. In this paper, we propose an infinite factorial unbounded-state hidden Markov model (IFUHMM), in which the number of parallel hidden Markov models (HMMs) and states in each HMM are potentially unbounded. We rely on a Bayesian nonparametric (BNP) prior over integer-valued matrices, in which the columns represent the Markov chains, the rows the time indexes, and the integers the state for each chain and time instant. First, we extend the existent infinite factorial binary-state HMM to allow for any number of states. Then, we modify this model to allow for an unbounded number of states and derive an MCMC-based inference algorithm that properly deals with the trade-off between the unbounded number of states and chains. We illustrate the performance of our proposed models in the power disaggregation problem.},
keywords = {Bayes methods, Bayesian nonparametrics, CASI CAM CM, Computational modeling, GAMMA-L+ UC3M, Gibbs sampling, Hidden Markov models, Inference algorithms, Markov processes, Probability distribution, reversible jump Markov chain Monte Carlo, slice sampling, Time series, variational inference, Yttrium},
pubstate = {published},
tppubtype = {article}
}
@article{Nazabal2016bb,
title = {Human Activity Recognition by Combining a Small Number of Classifiers},
author = {Alfredo Nazabal and Pablo Garcia-Moreno and Antonio Artes-Rodriguez and Zoubin Ghahramani},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7161292},
doi = {10.1109/JBHI.2015.2458274},
issn = {2168-2208},
year  = {2016},
date = {2016-01-01},
journal = {IEEE journal of biomedical and health informatics},
volume = {To appear},
publisher = {IEEE},
abstract = {We consider the problem of daily Human Activity Recognition (HAR) using multiple wireless inertial sensors and, specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semi-supervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and a Markovian structure of the human activities.},
keywords = {Bayes methods, Bayesian inference, Biological system modeling, Classifier combination, Databases, Estimation, Hidden Markov models, Sensor systems},
pubstate = {published},
tppubtype = {article}
}
@article{Borchani2016,
title = {Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers},
author = {Hanen Borchani and Pedro Larra\~{n}aga and J Gama and Concha Bielza},
url = {http://cig.fi.upm.es/node/879},
year  = {2016},
date = {2016-01-01},
journal = {Intelligent Data Analysis},
volume = {20},
keywords = {CASI CAM CM, CIG UPM, Journal},
pubstate = {published},
tppubtype = {article}
}
@article{Asheghan2016,
title = {Stability Analysis and Robust Control of Heart Beat Rate During Treadmill Exercise},
author = {Mohammad Mostafa Asheghan and Joaqu\'{i}n M\'{i}guez},
doi = {10.1016/j.automatica.2015.10.027},
issn = {00051098},
year  = {2016},
date = {2016-01-01},
journal = {Automatica},
volume = {63},
pages = {311--320},
abstract = {We investigate a nonlinear dynamical model of a human's heart beat rate (HBR) during a treadmill exercise. We begin with a rigorous analysis of the stability of the model that extends significantly the results available in the literature. In particular, we first identify a simple set of necessary and sufficient conditions for both input-state stability and Lyapunov stability of the system, and then prove that the same conditions also hold when the model parameters are subject to unknown but bounded perturbations. The second part of the paper is devoted to the design and analysis of a control structure for this model, where the treadmill speed plays the role of the control input and the output is the subject's HBR, which is intended to follow a prescribed pattern. We propose a simple control scheme, suitable for a practical implementation, and then analyze its performance. Specifically, we prove (i) that the same conditions that guarantee the stability of the system also ensure that the controller attains a desired level of performance (quantified in terms of the admissible deviation of the HBR from the prescribed profile) and (ii) that the controller is robust to bounded perturbations both in the system parameters and the control input. Numerical simulations are also presented in order to illustrate some of the theoretical results.},
keywords = {Cardiovascular system, Journal, Nonlinear systems, Robust control},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Castellanos2016,
title = {Influencia del Ritmo en la Identificaci\'{o}n de Islotes de Escara Auricular en Pacientes con FA Persistente sin Disfunci\'{o}n Ventricular Izquierda Detectada con Cat\'{e}ter de Mapeo Multielectrodo de 1mm},
author = {Evaristo Castellanos and Pablo Ruiz M Hernandez and Gonzalo R\'{i}os-Mu\~{n}oz and Pablo \'{A}vila and Tom\'{a}s Datino and Felipe Atienza and Francisco Fern\'{a}ndez-Avil\'{e}s and \'{A}ngel Arenal},
year  = {2016},
date = {2016-01-01},
booktitle = {SEC 2016 - El Congr. las Enfermedades Cardiovasc.},
number = {6002-38},
abstract = {En la fibrilaci\'{o}n auricular persistente (FA-Per), el aislamiento de las venas pulmonares (VVPP) presenta una mayor tasa de recidiva que en FA parox\'{i}stica. La FA-Per induce una remodelaci\'{o}n estructural caracterizada por fibrosis y formaci\'{o}n de tejido cicatricial en la aur\'{i}cula. La remodelaci\'{o}n estructural se asocia con una mayor tasa de recurrencia tras la ablaci\'{o}n. El mapeo electroanat\'{o}mico del tejido cicatricial no asociado a las VVPP podr\'{i}a facilitar la identificaci\'{o}n del sustrato espec\'{i}fico de la FA-Per. Los cat\'{e}teres de mapeo multielectrodo proporcionan una alta definici\'{o}n de tejido fibr\'{o}tico en pacientes con taquicardias auriculares. El objetivo fue evaluar y cuantificar la presencia de islotes de tejido cicatricial (durante el ritmo sinusal (RS) y FA) en los pacientes con FA-Per sin disfunci\'{o}n ventricular izquierda},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{F.Pradier2016,
title = {Prior Design for Dependent Dirichlet Processes: An Application to Marathon Modeling},
author = {Melanie F. Pradier and Francisco J R Ruiz and Fernando Perez-Cruz},
editor = {Guy Brock},
url = {http://dx.plos.org/10.1371/journal.pone.0147402},
doi = {10.1371/journal.pone.0147402},
issn = {1932-6203},
year  = {2016},
date = {2016-01-01},
journal = {PLOS ONE},
volume = {11},
number = {1},
pages = {e0147402},
publisher = {Public Library of Science},
abstract = {This paper presents a novel application of Bayesian nonparametrics (BNP) for marathon data modeling. We make use of two well-known BNP priors, the single-p dependent Dirichlet process and the hierarchical Dirichlet process, in order to address two different problems. First, we study the impact of age, gender and environment on the runners' performance. We derive a fair grading method that allows direct comparison of runners regardless of their age and gender. Unlike current grading systems, our approach is based not only on top world records, but on the performances of all runners. The presented methodology for comparison of densities can be adopted in many other applications straightforwardly, providing an interesting perspective to build dependent Dirichlet processes. Second, we analyze the running patterns of the marathoners in time, obtaining information that can be valuable for training purposes. We also show that these running patterns can be used to predict finishing time given intermediate interval measurements. We apply our models to New York City, Boston and London marathons.},
keywords = {Journal},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Valera2015a,
title = {Infinite Factorial Dynamical Model},
author = {Isabel Valera and Francisco J R Ruiz and Lennart Svensson and Fernando Perez-Cruz},
url = {http://papers.nips.cc/paper/5667-infinite-factorial-dynamical-model},
year  = {2015},
date = {2015-12-01},
booktitle = {Advances in Neural Information Processing Systems},
pages = {1657--1665},
address = {Montreal},
abstract = {We propose the infinite factorial dynamic model (iFDM), a general Bayesian nonparametric model for source separation. Our model builds on the Markov Indian buffet process to consider a potentially unbounded number of hidden Markov chains (sources) that evolve independently according to some dynamics, in which the state space can be either discrete or continuous. For posterior inference, we develop an algorithm based on particle Gibbs with ancestor sampling that can be efficiently applied to a wide range of source separation problems. We evaluate the performance of our iFDM on four well-known applications: multitarget tracking, cocktail party, power disaggregation, and multiuser detection. Our experimental results show that our approach for source separation does not only outperform previous approaches, but it can also handle problems that were computationally intractable for existing approaches.},
keywords = {CASI CAM CM, GAMMA-L+ UC3M},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Luengo2015a,
title = {Bias correction for distributed Bayesian estimators},
author = {David Luengo and Luca Martino and Victor Elvira and Monica F Bugallo},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7383784},
doi = {10.1109/CAMSAP.2015.7383784},
isbn = {978-1-4799-1963-5},
year  = {2015},
date = {2015-12-01},
booktitle = {2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)},
pages = {253--256},
publisher = {IEEE},
address = {Cancun},
abstract = {Dealing with the whole dataset in big data estimation problems is usually unfeasible. A common solution then consists of dividing the data into several smaller sets, performing distributed Bayesian estimation and combining these partial estimates to obtain a global estimate. A major problem of this approach is the presence of a non-negligible bias in the partial estimators, due to the mismatch between the unknown true prior and the prior assumed in the estimation. A simple method to mitigate the effect of this bias is proposed in this paper. Essentially, the approach is based on using a reference data set to obtain a rough estimation of the parameter of interest, i.e., a reference parameter. This information is then communicated to the partial filters that handle the smaller data sets, which can thus use a refined prior centered around this parameter. Simulation results confirm the good performance of this scheme.},
keywords = {Bayes methods, Big data, Distributed databases, Estimation, Probability density function, Wireless Sensor Networks},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Dashi2015,
title = {RSSI Localization with Gaussian Processes and Tracking},
author = {Marzieh Dashti and Simon Yiu and Siamak Yousefi and Fernando Perez-Cruz and Holger Claussen},
url = {http://globecom2015.ieee-globecom.org/},
year  = {2015},
date = {2015-12-01},
booktitle = {IEEE Globecom},
address = {San Diego},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Guzman2015,
title = {Cooperative Optical Wireless Transmission for Improving Performance in Indoor Scenarios for Visible Light Communications},
author = {Borja Genoves Guzman and Alejandro Lancho Serrano and V\'{i}ctor Gil P Jimenez},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7389772},
doi = {10.1109/TCE.2015.7389772},
issn = {0098-3063},
year  = {2015},
date = {2015-11-01},
journal = {IEEE Transactions on Consumer Electronics},
volume = {61},
number = {4},
pages = {393--401},
publisher = {IEEE},
abstract = {In this paper, a novel cooperative transmission and reception scheme in Visible Light Communications (VLC) is proposed and evaluated. This new scheme provides improvements and reliability in large indoor scenarios, such as corridors, laboratories, shops or conference rooms, where the coverage needs to be obtained by using different access points when VLC is used. The main idea behind the proposal is a simple cooperative transmission scheme where the receiver terminal will obtain the signal from different access points at the same time. This proposal outperforms traditional VLC schemes, especially in Non-Line-of-Sight reception where around 3 dB of gain, with respect to traditional schemes, can be obtained for unoptimized parameters, and larger than 3 dB could easily be achieved. The cooperation is studied in terms of the percentage of light coming from the main access point and a parameter called sidelobes??? amplitude level. The performance is evaluated according to the location within the atto-cell.},
keywords = {CoMP, Cooperative transmission andreception, Interference, Journal, Nonlinear optics, Optical receivers, Proposals, Pulse Position Division Multiplexing, Radio frequency, VLC, Wireless communication},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Elvira2015b,
title = {On Sample Generation and Weight Calculation in Multiple Importance Sampling},
author = {Victor Elvira and Luca Martino and David Luengo and Monica F Bugallo},
url = {http://www.asilomarsscconf.org/webpage/asil15/Asilomar 2015 Book of Abstracts v005.pdf},
year  = {2015},
date = {2015-11-01},
booktitle = {IEEE Conference on Signals, Systems, and Computers (ASILOMAR 2015)},
address = {Pacific Groove},
abstract = {We investigate various sampling and weight updating techniques, which are the two crucial steps of importance sampling. We discuss the standard mixture sampling that randomly draws samples from a set of proposals and the deterministic mixture sampling, where exactly one sample is drawn from each proposal. For weight calculation, we either compute the weights by considering the particular proposal used for each sample or by interpreting the proposal as a mixture formed by all available proposals. All combinations of sampling and weight calculation and some modifications that improve the performance and/or reduce the computational complexity are examined through computer simulations},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Acer2015,
title = {Sensing WiFi Network for Personal IoT Analytics},
author = {Utku Gunay Acer and Aidan Boran and Claudio Forlivesi and Werner Liekens and Fernando Perez-cruz and Fahim Kawsar},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7356554},
doi = {10.1109/IOT.2015.7356554},
isbn = {978-1-4673-8056-0},
year  = {2015},
date = {2015-10-01},
booktitle = {2015 5th International Conference on the Internet of Things (IOT)},
pages = {104--111},
publisher = {IEEE},
address = {Seoul},
abstract = {We present the design, implementation and evaluation of an enabling platform for locating and querying physical objects using existing WiFi network. We propose the use of WiFi management probes as a data transport mechanism for physical objects that are tagged with WiFi-enabled accelerometers and are capable of determining their state-of-use based on motion signatures. A local WiFi gateway captures these probes emitted from the connected objects and stores them locally after annotating them with a coarse grained location estimate using a proximity ranging algorithm. External applications can query the aggregated views of state-of-use and location traces of connected objects through a cloud-based query server. We present the technical architecture and algorithms of the proposed platform together with a prototype personal object analytics application and assess the feasibility of our different design decisions. This work makes important contributions by demonstrating that it is possible to build a pure network-based IoT analytics platform with only location and motion signatures of connected objects, and that the WiFi network is the key enabler for the future IoT applications.},
keywords = {Accelerometers, cloud based query server, cloud computing, data transport mechanism, digital signatures, Distance measurement, Internet of Things, internetworking, IoT analytic, Logic gates, Mobile communication, motion signatures, network servers, Probes, proximity ranging algorithm, Search problems, telecommunication network management, WiFi gateway captures, WiFi management probes, WiFi network, wireless LAN},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Ramirez2015,
title = {Detection of Multivariate Cyclostationarity},
author = {David Ram\'{i}rez and Peter J Schreier and Javier Via and Ignacio Santamaria and L L Scharf},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7134806},
doi = {10.1109/TSP.2015.2450201},
issn = {1053-587X},
year  = {2015},
date = {2015-10-01},
journal = {IEEE Transactions on Signal Processing},
volume = {63},
number = {20},
pages = {5395--5408},
publisher = {IEEE},
abstract = {This paper derives an asymptotic generalized likelihood ratio test (GLRT) and an asymptotic locally most powerful invariant test (LMPIT) for two hypothesis testing problems: 1) Is a vector-valued random process cyclostationary (CS) or is it wide-sense stationary (WSS)? 2) Is a vector-valued random process CS or is it nonstationary? Our approach uses the relationship between a scalar-valued CS time series and a vector-valued WSS time series for which the knowledge of the cycle period is required. This relationship allows us to formulate the problem as a test for the covariance structure of the observations. The covariance matrix of the observations has a block-Toeplitz structure for CS and WSS processes. By considering the asymptotic case where the covariance matrix becomes block-circulant we are able to derive its maximum likelihood (ML) estimate and thus an asymptotic GLRT. Moreover, using Wijsman's theorem, we also obtain an asymptotic LMPIT. These detectors may be expressed in terms of the Loève spectrum, the cyclic spectrum, and the power spectral density, establishing how to fuse the information in these spectra for an asymptotic GLRT and LMPIT. This goes beyond the state-of-the-art, where it is common practice to build detectors of cyclostationarity from ad-hoc functions of these spectra.},
keywords = {ad hoc function, asymptotic GLRT, asymptotic LMPIT, block circulant, block-Toeplitz structure, Correlation, covariance matrices, Covariance matrix, covariance structure, cycle period, cyclic spectrum, Cyclostationarity, Detectors, Frequency-domain analysis, generalized likelihood ratio test, generalized likelihood ratio test (GLRT), hypothesis testing problem, locally most powerful invariant test, locally most powerful invariant test (LMPIT), Loe{\&amp;amp;amp;amp;}{#}x0300, maximum likelihood estimation, multivariate cyclostationarity detection, power spectral density, random processes, s theorem, scalar valued CS time series, signal detection, spectral analysis, statistical testing, Testing, Time series, Time series analysis, Toeplitz matrices, Toeplitz matrix, ve spectrum, vector valued random process cyclostationary, vector valued WSS time series, wide sense stationary, Wijsman theorem, Wijsman{\&amp;amp;amp;amp;}{#}x2019},
pubstate = {published},
tppubtype = {article}
}
@article{Elvira2015bb,
title = {Efficient Multiple Importance Sampling Estimators},
author = {Victor Elvira and Luca Martino and David Luengo and Monica F Bugallo},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7105865},
doi = {10.1109/LSP.2015.2432078},
issn = {1070-9908},
year  = {2015},
date = {2015-10-01},
journal = {IEEE Signal Processing Letters},
volume = {22},
number = {10},
pages = {1757--1761},
publisher = {IEEE},
abstract = {Multiple importance sampling (MIS) methods use a set of proposal distributions from which samples are drawn. Each sample is then assigned an importance weight that can be obtained according to different strategies. This work is motivated by the trade-off between variance reduction and computational complexity of the different approaches (classical vs. deterministic mixture) available for the weight calculation. A new method that achieves an efficient compromise between both factors is introduced in this letter. It is based on forming a partition of the set of proposal distributions and computing the weights accordingly. Computer simulations show the excellent performance of the associated partial deterministic mixture MIS estimator.},
keywords = {Adaptive importance sampling, classical mixture approach, computational complexity, Computational efficiency, Computer Simulation, deterministic mixture, estimation theory, Journal, Monte Carlo methods, multiple importance sampling, multiple importance sampling estimator, partial deterministic mixture MIS estimator, Proposals, signal sampling, Sociology, Standards, variance reduction, weight calculation},
pubstate = {published},
tppubtype = {article}
}
@article{Mihaljevic2015,
title = {Classifying GABAergic interneurons with semi-supervised projected model-based clustering.},
author = {Bojan Mihaljevi\'{c} and Ruth Benavides-Piccione and Luis Guerra and Javier DeFelipe and Pedro Larra\~{n}aga and Concha Bielza},
url = {http://www.aiimjournal.com/article/S0933365714001481/fulltext http://cig.fi.upm.es/articles/2015/Mihaljevic-2015-AIIM.pdf},
doi = {10.1016/j.artmed.2014.12.010},
issn = {1873-2860},
year  = {2015},
date = {2015-09-01},
journal = {Artificial intelligence in medicine},
volume = {65},
number = {1},
pages = {49--59},
publisher = {Elsevier},
abstract = {OBJECTIVES: A recently introduced pragmatic scheme promises to be a useful catalog of interneuron names. We sought to automatically classify digitally reconstructed interneuronal morphologies according to this scheme. Simultaneously, we sought to discover possible subtypes of these types that might emerge during automatic classification (clustering). We also investigated which morphometric properties were most relevant for this classification. MATERIALS AND METHODS: A set of 118 digitally reconstructed interneuronal morphologies classified into the common basket (CB), horse-tail (HT), large basket (LB), and Martinotti (MA) interneuron types by 42 of the world's leading neuroscientists, quantified by five simple morphometric properties of the axon and four of the dendrites. We labeled each neuron with the type most commonly assigned to it by the experts. We then removed this class information for each type separately, and applied semi-supervised clustering to those cells (keeping the others' cluster membership fixed), to assess separation from other types and look for the formation of new groups (subtypes). We performed this same experiment unlabeling the cells of two types at a time, and of half the cells of a single type at a time. The clustering model is a finite mixture of Gaussians which we adapted for the estimation of local (per-cluster) feature relevance. We performed the described experiments on three different subsets of the data, formed according to how many experts agreed on type membership: at least 18 experts (the full data set), at least 21 (73 neurons), and at least 26 (47 neurons). RESULTS: Interneurons with more reliable type labels were classified more accurately. We classified HT cells with 100% accuracy, MA cells with 73% accuracy, and CB and LB cells with 56% and 58% accuracy, respectively. We identified three subtypes of the MA type, one subtype of CB and LB types each, and no subtypes of HT (it was a single, homogeneous type). We got maximum (adapted) Silhouette width and ARI values of 1, 0.83, 0.79, and 0.42, when unlabeling the HT, CB, LB, and MA types, respectively, confirming the quality of the formed cluster solutions. The subtypes identified when unlabeling a single type also emerged when unlabeling two types at a time, confirming their validity. Axonal morphometric properties were more relevant that dendritic ones, with the axonal polar histogram length in the [$pi$, 2$pi$) angle interval being particularly useful. CONCLUSIONS: The applied semi-supervised clustering method can accurately discriminate among CB, HT, LB, and MA interneuron types while discovering potential subtypes, and is therefore useful for neuronal classification. The discovery of potential subtypes suggests that some of these types are more heterogeneous that previously thought. Finally, axonal variables seem to be more relevant than dendritic ones for distinguishing among the CB, HT, LB, and MA interneuron types.},
keywords = {Automatic neuron classification, CASI CAM CM, Cerebral cortex, CIG UPM, Gaussian mixture models, Journal, Semi-supervised projected clustering},
pubstate = {published},
tppubtype = {article}
}
@article{Borchani2015,
title = {A survey on multi-output regression},
author = {Hanen Borchani and Gherardo Varando and Concha Bielza and Pedro Larra\~{n}aga},
url = {http://doi.wiley.com/10.1002/widm.1157 http://cig.fi.upm.es/articles/2015/Borchani-2015-WDMKD.pdf},
doi = {10.1002/widm.1157},
issn = {19424787},
year  = {2015},
date = {2015-09-01},
journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
volume = {5},
number = {5},
pages = {216--233},
abstract = {In recent years, a plethora of approaches have been proposed to deal with the increasingly challenging task of multi-output regression. This study provides a survey on state-of-the-art multi-output regression methods, that are categorized as problem transformation and algorithm adaptation methods. In addition, we present the mostly used performance evaluation measures, publicly available data sets for multi-output regression real-world problems, as well as open-source software frameworks.},
keywords = {algorithm adaptation methods, CASI CAM CM, CIG UPM, Journal, Multi-output regression, multi-target regression, performance evaluation measure, problem transformation methods},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Luengo2015b,
title = {Causality analysis of atrial fibrillation electrograms},
author = {David Luengo and Gonzalo R\'{i}os-Mu\~{n}oz and Victor Elvira},
url = {http://ieeexplore.ieee.org/document/7410978/},
doi = {10.1109/CIC.2015.7410978},
isbn = {978-1-5090-0685-4},
year  = {2015},
date = {2015-09-01},
booktitle = {2015 Comput. Cardiol. Conf.},
pages = {585--588},
publisher = {IEEE},
abstract = {Multi-channel intracavitary electrograms (EGMs) are acquired at the electrophysiology laboratory to guide radio frequency catheter ablation of patients suffering from atrial fibrillation (AF). These EGMs are used by cardiologists to determine candidate areas for ablation (e.g., areas corresponding to high dominant frequencies or complex fractionated electrograms). In this paper, we introduce two hierarchical algorithms to retrieve the causal interactions among these multiple EGMs. Both algorithms are based on Granger causality, but other causality measures can be easily incorporated. In both cases, they start by selecting a root node, but they differ on the way in which they explore the set of signals to determine their causeeffect relationships: either testing the full set of unexplored signals (GS-CaRe) or performing a local search only among the set of neighbor EGMs (LS-CaRe). The ensuing causal model provides important information about the propagation of the electrical signals inside the atria, uncovering wavefronts and activation patterns that can guide cardiologists towards candidate areas for catheter ablation. Numerical experiments, on both synthetic signals and annotated real-world signals, show the good performance of the two proposed approaches.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Luengo2015c,
title = {Causality Analysis of Atrial Fibrillation Electrograms},
author = {David Luengo and Gonzalo R\'{i}os-Mu\~{n}oz and Victor Elvira},
url = {http://www.cinc2015.org/},
year  = {2015},
date = {2015-09-01},
booktitle = {Computing in Cardiology},
address = {Nice},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Valera2015b,
title = {A Bayesian Nonparametric Approach for Blind Multiuser Channel Estimation},
author = {Isabel Valera and Francisco J R Ruiz and Lennart Svensson and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7362888 http://www.eurasip.org/Proceedings/Eusipco/Eusipco2015/papers/1570096659.pdf},
doi = {10.1109/EUSIPCO.2015.7362888},
isbn = {978-0-9928-6263-3},
year  = {2015},
date = {2015-08-01},
booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
pages = {2766--2770},
publisher = {IEEE},
address = {Nice},
abstract = {In many modern multiuser communication systems, users are allowed to enter and leave the system at any given time. Thus, the number of active users is an unknown and time-varying parameter, and the performance of the system depends on how accurately this parameter is estimated over time. We address the problem of blind joint channel parameter and data estimation in a multiuser communication channel in which the number of transmitters is not known. For that purpose, we develop a Bayesian nonparametric model based on the Markov Indian buffet process and an inference algorithm that makes use of slice sampling and particle Gibbs with ancestor sampling. Our experimental results show that the proposed approach can effectively recover the data-generating process for a wide range of scenarios.},
keywords = {Bayes methods, Bayesian nonparametric, communication systems, factorial HMM, Hidden Markov models, machine-to-machine, multiuser communication, Receiving antennas, Signal to noise ratio, Transmitters},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Santos2015,
title = {Block Expectation Propagation Equalization for ISI Channels},
author = {Irene Santos and Juan Jose Murillo-Fuentes and Pablo M Olmos},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7362409},
doi = {10.1109/EUSIPCO.2015.7362409},
isbn = {978-0-9928-6263-3},
year  = {2015},
date = {2015-08-01},
booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
pages = {379--383},
publisher = {IEEE},
address = {Nice},
abstract = {Actual communications systems use high-order modulations and channels with memory. However, as the memory of the channels and the order of the constellations grow, optimal equalization such as BCJR algorithm is computationally intractable, as their complexity increases exponentially with the number of taps and size of modulation. In this paper, we propose a novel low-complexity hard and soft output equalizer based on the Expectation Propagation (EP) algorithm that provides high-accuracy posterior probability estimations at the input of the channel decoder with similar computational complexity than the linear MMSE. We experimentally show that this quasi-optimal solution outperforms classical solutions reducing the bit error probability with low complexity when LDPC channel decoding is used, avoiding the curse of dimensionality with channel memory and constellation size.},
keywords = {Approximation algorithms, Approximation methods, BCJR algorithm, channel equalization, Complexity theory, Decoding, Equalizers, expectation propagation, ISI, low complexity, Signal processing algorithms},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Moreno2015b,
title = {Bayesian Nonparametric Crowdsourcing},
author = {Pablo Garcia-Moreno and Yee Whye Teh and Fernando Perez-Cruz and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.jmlr.org/papers/volume16/moreno15a/moreno15a.pdf},
year  = {2015},
date = {2015-08-01},
journal = {Journal of Machine Learning Research},
volume = {16},
number = {August},
pages = {1607--1627},
abstract = {Crowdsourcing has been proven to be an effective and efficient tool to annotate large datasets. User annotations are often noisy, so methods to combine the annotations to produce reliable estimates of the ground truth are necessary. We claim that considering the existence of clusters of users in this combination step can improve the performance. This is especially important in early stages of crowdsourcing implementations, where the number of annotations is low. At this stage there is not enough information to accurately estimate the bias introduced by each annotator separately, so we have to resort to models that consider the statistical links among them. In addition, finding these clusters is interesting in itself as knowing the behavior of the pool of annotators allows implementing efficient active learning strategies. Based on this, we propose in this paper two new fully unsupervised models based on a Chinese Restaurant Process (CRP) prior and a hierarchical structure that allows inferring these groups jointly with the ground truth and the properties of the users. Efficient inference algorithms based on Gibbs sampling with auxiliary variables are proposed. Finally, we perform experiments, both on synthetic and real databases, to show the advantages of our models over state-of-the-art algorithms.},
keywords = {Bayesian nonparametrics, Dirichlet process, Gibbs sampling, Hierarchical clustering, Journal, Multiple annotators},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Martino2015bb,
title = {Parallel interacting Markov adaptive importance sampling},
author = {Luca Martino and Victor Elvira and David Luengo and Jukka Corander},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7362433 http://www.eurasip.org/Proceedings/Eusipco/Eusipco2015/papers/1570111267.pdf},
doi = {10.1109/EUSIPCO.2015.7362433},
isbn = {978-0-9928-6263-3},
year  = {2015},
date = {2015-08-01},
booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
pages = {499--503},
publisher = {IEEE},
address = {Nice},
abstract = {Monte Carlo (MC) methods are widely used for statistical inference in signal processing applications. A well-known class of MC methods is importance sampling (IS) and its adaptive extensions. In this work, we introduce an iterated importance sampler using a population of proposal densities, which are adapted according to an MCMC technique over the population of location parameters. The novel algorithm provides a global estimation of the variables of interest iteratively, using all the samples weighted according to the deterministic mixture scheme. Numerical results, on a multi-modal example and a localization problem in wireless sensor networks, show the advantages of the proposed schemes.},
keywords = {Adaptive importance sampling, Bayesian inference, MCMC methods, Monte Carlo methods, Parallel Chains, Probability density function, Proposals, Signal processing, Signal processing algorithms, Sociology},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Martino2015bbb,
title = {An Adaptive Population Importance Sampler: Learning From Uncertainty},
author = {Luca Martino and Victor Elvira and David Luengo and Jukka Corander},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7117437},
doi = {10.1109/TSP.2015.2440215},
issn = {1053-587X},
year  = {2015},
date = {2015-08-01},
journal = {IEEE Transactions on Signal Processing},
volume = {63},
number = {16},
pages = {4422--4437},
publisher = {IEEE},
abstract = {Monte Carlo (MC) methods are well-known computational techniques, widely used in different fields such as signal processing, communications and machine learning. An important class of MC methods is composed of importance sampling (IS) and its adaptive extensions, such as population Monte Carlo (PMC) and adaptive multiple IS (AMIS). In this paper, we introduce a novel adaptive and iterated importance sampler using a population of proposal densities. The proposed algorithm, named adaptive population importance sampling (APIS), provides a global estimation of the variables of interest iteratively, making use of all the samples previously generated. APIS combines a sophisticated scheme to build the IS estimators (based on the deterministic mixture approach) with a simple temporal adaptation (based on epochs). In this way, APIS is able to keep all the advantages of both AMIS and PMC, while minimizing their drawbacks. Furthermore, APIS is easily parallelizable. The cloud of proposals is adapted in such a way that local features of the target density can be better taken into account compared to single global adaptation procedures. The result is a fast, simple, robust, and high-performance algorithm applicable to a wide range of problems. Numerical results show the advantages of the proposed sampling scheme in four synthetic examples and a localization problem in a wireless sensor network.},
keywords = {Adaptive importance sampling, adaptive multiple IS, adaptive population importance sampler, AMIS, APIS, Estimation, Importance sampling, IS estimators, iterative estimation, iterative methods, Journal, MC methods, Monte Carlo (MC) methods, Monte Carlo methods, population Monte Carlo, Proposals, Signal processing algorithms, simple temporal adaptation, Sociology, Standards, Wireless sensor network, Wireless Sensor Networks},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Olmos2015b,
title = {Analyzing the Finite-Length Performance of Generalized LDPC Codes},
author = {Pablo M Olmos and David G M Mitchell and Daniel J Costello},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7282943},
doi = {10.1109/ISIT.2015.7282943},
isbn = {978-1-4673-7704-1},
year  = {2015},
date = {2015-06-01},
booktitle = {2015 IEEE International Symposium on Information Theory (ISIT)},
pages = {2683--2687},
publisher = {IEEE},
address = {Hong Kong},
abstract = {In this paper, we analyze the performance of finite-length generalized LDPC (GLDPC) block codes constructed from protographs when transmission takes place over the binary erasure channel (BEC). A generalized peeling decoder is proposed and we derive a system of differential equations that gives the expected evolution of the graph degree distribution during decoding. We then show that the finite-length performance of a GLDPC code can be estimated by means of a simple scaling law, where a single scaling parameter represents the finite-length properties of the code. We also show that, as we consider stronger component codes, both the asymptotic threshold and the finite-length scaling parameter are improved.},
keywords = {BEC, binary codes, binary erasure channel, Block codes, Codes on graphs, Decoding, Differential equations, error probability, finite-length generalized LDPC block codes, finite-length performance analysis, generalized LDPC codes, generalized peeling decoder, GLDPC block codes, graph degree distribution, graph theory, Iterative decoding, parity check codes, protographs},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Stinner2015,
title = {Finite-Length Performance of Multi-Edge Protograph-Based Spatially Coupled LDPC Codes},
author = {Markus Stinner and Pablo M Olmos},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7282583},
doi = {10.1109/ISIT.2015.7282583},
isbn = {978-1-4673-7704-1},
year  = {2015},
date = {2015-06-01},
booktitle = {2015 IEEE International Symposium on Information Theory (ISIT)},
pages = {889--893},
publisher = {IEEE},
address = {Hong Kong},
abstract = {The finite-length performance of multi-edge spatially coupled low-density parity-check (SC-LDPC) codes over the binary erasure channel (BEC) is analyzed. Existing scaling laws are extended to arbitrary protograph base matrices that include puncturing patterns and multiple edges between nodes. A regular protograph-based SC-LDPC construction based on the (4; 8)-regular LDPC block code works well in the waterfall region compared to more involved rate-1/2 structures proposed to improve the threshold to minimum distance trade-off. Scaling laws are also used for code design and to estimate the block length of a given SC-LDPC code ensemble to match the performance of some other code. Estimates on the performance degradation are developed if the chain length varies.},
keywords = {binary erasure channel, Block codes, Couplings, Decoding, Error analysis, finite length performance, finite-length performance, graph theory, Iterative decoding, low density parity check codes, multiedge protograph, parity check codes, spatially coupled LDPC codes, spatially-coupled LDPC codes, Steady-state},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Vazquez-Vilar2015,
title = {A derivation of the Cost-Constrained Sphere-Packing Exponent},
author = {Gonzalo Vazquez-Vilar and Alfonso Martinez and Albert Guillen i Fabregas},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7282591},
doi = {10.1109/ISIT.2015.7282591},
isbn = {978-1-4673-7704-1},
year  = {2015},
date = {2015-06-01},
booktitle = {2015 IEEE International Symposium on Information Theory (ISIT)},
pages = {929--933},
publisher = {IEEE},
address = {Hong Kong},
keywords = {Channel Coding, channel-coding cost-constrained sphere-packing exp, continuous channel, continuous memoryless channel, cost constraint, error probability, hypothesis testing, Lead, Memoryless systems, Optimization, per-codeword cost constraint, reliability function, spherepacking exponent, Testing},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Read2015b,
title = {Scalable Multi-Output Label Prediction: From Classifier Chains to Classifier Trellises},
author = {Jesse Read and Luca Martino and Pablo M Olmos and David Luengo},
url = {http://www.sciencedirect.com/science/article/pii/S0031320315000084},
year  = {2015},
date = {2015-06-01},
journal = {Pattern Recognition},
volume = {48},
number = {6},
pages = {2096--2106},
abstract = {Multi-output inference tasks, such as multi-label classification, have become increasingly important in recent years. A popular method for multi-label classification is classifier chains, in which the predictions of individual classifiers are cascaded along a chain, thus taking into account inter-label dependencies and improving the overall performance. Several varieties of classifier chain methods have been introduced, and many of them perform very competitively across a wide range of benchmark datasets. However, scalability limitations become apparent on larger datasets when modeling a fully cascaded chain. In particular, the methods' strategies for discovering and modeling a good chain structure constitutes a mayor computational bottleneck. In this paper, we present the classifier trellis (CT) method for scalable multi-label classification. We compare CT with several recently proposed classifier chain methods to show that it occupies an important niche: it is highly competitive on standard multi-label problems, yet it can also scale up to thousands or even tens of thousands of labels.},
keywords = {Bayesian networks, classifer chains, Journal, Multi-label classification, multi-output prediction, structured inference},
pubstate = {published},
tppubtype = {article}
}
@article{Varando2015a,
title = {Decision functions for chain classifiers based on Bayesian networks for multi-label classification},
author = {Gherardo Varando and Concha Bielza and Pedro Larra\~{n}aga},
url = {http://www.researchgate.net/publication/279069321_Decision_functions_for_chain_classifiers_based_on_Bayesian_networks_for_multi-label_classification http://cig.fi.upm.es/node/887},
doi = {10.1016/j.ijar.2015.06.006},
issn = {0888613X},
year  = {2015},
date = {2015-06-01},
journal = {International Journal of Approximate Reasoning},
abstract = {Multi-label classification problems require each instance to be assigned a subset of a defined set of labels. This problem is equivalent to finding a multi-valued decision function that predicts a vector of binary classes. In this paper we study the decision boundaries of two widely used approaches for building multi-label classifiers, when Bayesian network-augmented naive Bayes classifiers are used as base models: Binary relevance method and chain classifiers. In particular extending previous single-label results to multi-label chain classifiers, we find polynomial expressions for the multi-valued decision functions associated with these methods. We prove upper boundings on the expressive power of both methods and we prove that chain classifiers provide a more expressive model than the binary relevance method Decision functions for chain classifiers based on Bayesian networks for multi-label classification. Available from: http://www.researchgate.net/publication/279069321_Decision_functions_for_chain_classifiers_based_on_Bayesian_networks_for_multi-label_classification [accessed Nov 15, 2015].},
keywords = {CASI CAM CM, CIG UPM, Journal},
pubstate = {published},
tppubtype = {article}
}
@article{Olmos2015bb,
title = {A Scaling Law to Predict the Finite-Length Performance of Spatially-Coupled LDPC Codes},
author = {Pablo M Olmos and Rudiger Urbanke},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7086074},
doi = {10.1109/TIT.2015.2422816},
issn = {0018-9448},
year  = {2015},
date = {2015-06-01},
journal = {IEEE Transactions on Information Theory},
volume = {61},
number = {6},
pages = {3164--3184},
abstract = {Spatially-coupled low-density parity-check (SC-LDPC) codes are known to have excellent asymptotic properties. Much less is known regarding their finite-length performance. We propose a scaling law to predict the error probability of finite-length spatially coupled code ensembles when transmission takes place over the binary erasure channel. We discuss how the parameters of the scaling law are connected to fundamental quantities appearing in the asymptotic analysis of these ensembles and we verify that the predictions of the scaling law fit well to the data derived from simulations over a wide range of parameters. The ultimate goal of this line of research is to develop analytic tools for the design of SC-LDPC codes under practical constraints.},
keywords = {asymptotic analysis, asymptotic properties, binary erasure channel, Channel Coding, Codes on graphs, Couplings, Decoding, Differential equations, error probability, finite length performance, finite length spatially coupled code, finite-length code performance, finite-length performance, Iterative decoding, iterative decoding thresholds, Journal, parity check codes, Probability, SC-LDPC codes, scaling law, Sockets, spatially coupled LDPC codes, spatially-coupled LDPC codes},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Elvira2015a,
title = {A Gradient Adaptive Population Importance Sampler},
author = {Victor Elvira and Luca Martino and David Luengo and Jukka Corander},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7178737 http://www.tsc.uc3m.es/~velvira/papers/ICASSP2015_elvira.pdf},
doi = {10.1109/ICASSP.2015.7178737},
isbn = {978-1-4673-6997-8},
year  = {2015},
date = {2015-04-01},
booktitle = {2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {4075--4079},
publisher = {IEEE},
address = {Brisbane},
abstract = {Monte Carlo (MC) methods are widely used in signal processing and machine learning. A well-known class of MC methods is composed of importance sampling and its adaptive extensions (e.g., population Monte Carlo). In this paper, we introduce an adaptive importance sampler using a population of proposal densities. The novel algorithm dynamically optimizes the cloud of proposals, adapting them using information about the gradient and Hessian matrix of the target distribution. Moreover, a new kind of interaction in the adaptation of the proposal densities is introduced, establishing a trade-off between attaining a good performance in terms of mean square error and robustness to initialization.},
keywords = {adaptive extensions, adaptive importance sampler, Adaptive importance sampling, gradient adaptive population, gradient matrix, Hamiltonian Monte Carlo, Hessian matrices, Hessian matrix, learning (artificial intelligence), Machine learning, MC methods, Monte Carlo, Monte Carlo methods, population Monte Carlo (PMC), proposal densities, Signal processing, Sociology, statistics, target distribution},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Fernandez-Bes2015,
title = {A Probabilistic Least-Mean-Squares Filter},
author = {Jesus Fernandez-Bes and Victor Elvira and Steven Van Vaerenbergh},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7178361 http://www.tsc.uc3m.es/~velvira/papers/ICASSP2015_bes.pdf},
doi = {10.1109/ICASSP.2015.7178361},
isbn = {978-1-4673-6997-8},
year  = {2015},
date = {2015-04-01},
booktitle = {2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {2199--2203},
publisher = {IEEE},
address = {Brisbane},
abstract = {We introduce a probabilistic approach to the LMS filter. By means of an efficient approximation, this approach provides an adaptable step-size LMS algorithm together with a measure of uncertainty about the estimation. In addition, the proposed approximation preserves the linear complexity of the standard LMS. Numerical results show the improved performance of the algorithm with respect to standard LMS and state-of-the-art algorithms with similar complexity. The goal of this work, therefore, is to open the door to bring somemore Bayesian machine learning techniques to adaptive filtering.},
keywords = {adaptable step size LMS algorithm, Adaptation models, adaptive filtering, Approximation algorithms, Bayesian machine learning techniques, efficient approximation algorithm, filtering theory, Least squares approximations, least-mean-squares, probabilistic least mean squares filter, Probabilistic logic, probabilisticmodels, Probability, Signal processing algorithms, Standards, state-space models},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Luengo2015bb,
title = {Efficient Linear Combination of Partial Monte Carlo Estimators},
author = {David Luengo and Luca Martino and Victor Elvira and Monica F Bugallo},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7178742 http://www.tsc.uc3m.es/~velvira/papers/ICASSP2015_luengo.pdf},
doi = {10.1109/ICASSP.2015.7178742},
isbn = {978-1-4673-6997-8},
year  = {2015},
date = {2015-04-01},
booktitle = {2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {4100--4104},
publisher = {IEEE},
address = {Brisbane},
abstract = {In many practical scenarios, including those dealing with large data sets, calculating global estimators of unknown variables of interest becomes unfeasible. A common solution is obtaining partial estimators and combining them to approximate the global one. In this paper, we focus on minimum mean squared error (MMSE) estimators, introducing two efficient linear schemes for the fusion of partial estimators. The proposed approaches are valid for any type of partial estimators, although in the simulated scenarios we concentrate on the combination of Monte Carlo estimators due to the nature of the problem addressed. Numerical results show the good performance of the novel fusion methods with only a fraction of the cost of the asymptotically optimal solution.},
keywords = {covariance matrices, efficient linear combination, Estimation, fusion, Global estimator, global estimators, least mean squares methods, linear combination, minimum mean squared error estimators, Monte Carlo estimation, Monte Carlo methods, partial estimator, partial Monte Carlo estimators, Xenon},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Nazabal2015,
title = {Discriminative spectral learning of hidden markov models for human activity recognition},
author = {Alfredo Nazabal and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7178314},
doi = {10.1109/ICASSP.2015.7178314},
isbn = {978-1-4673-6997-8},
year  = {2015},
date = {2015-04-01},
booktitle = {2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {1966--1970},
publisher = {IEEE},
address = {Brisbane},
abstract = {Hidden Markov Models (HMMs) are one of the most important techniques to model and classify sequential data. Maximum Likelihood (ML) and (parametric and non-parametric) Bayesian estimation of the HMM parameters suffers from local maxima and in massive datasets they can be specially time consuming. In this paper, we extend the spectral learning of HMMs, a moment matching learning technique free from local maxima, to discriminative HMMs. The resulting method provides the posterior probabilities of the classes without explicitly determining the HMM parameters, and is able to deal with missing labels. We apply the method to Human Activity Recognition (HAR) using two different types of sensors: portable inertial sensors, and fixed, wireless binary sensor networks. Our algorithm outperforms the standard discriminative HMM learning in both complexity and accuracy.},
keywords = {Accuracy, Bayesian estimation, classify sequential data, Data models, Databases, Discriminative learning, discriminative spectral learning, Hidden Markov models, HMM parameters, Human activity recognition, learning (artificial intelligence), maximum likelihood, maximum likelihood estimation, ML, moment matching learning technique, Observable operator models, sensors, Spectral algorithm, spectral learning, Speech recognition, Training},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Martino2015a,
title = {Smelly Parallel MCMC Chains},
author = {Luca Martino and Victor Elvira and David Luengo and Antonio Art\'{e}s-Rodr\'{i}guez and Jukka Corander},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7178736 http://www.tsc.uc3m.es/~velvira/papers/ICASSP2015_martino.pdf},
doi = {10.1109/ICASSP.2015.7178736},
isbn = {978-1-4673-6997-8},
year  = {2015},
date = {2015-04-01},
booktitle = {2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {4070--4074},
publisher = {IEEE},
address = {Brisbane},
abstract = {Monte Carlo (MC) methods are useful tools for Bayesian inference and stochastic optimization that have been widely applied in signal processing and machine learning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce a novel parallel interacting MCMC scheme, where the parallel chains share information, thus yielding a faster exploration of the state space. The interaction is carried out generating a dynamic repulsion among the “smelly” parallel chains that takes into account the entire population of current states. The ergodicity of the scheme and its relationship with other sampling methods are discussed. Numerical results show the advantages of the proposed approach in terms of mean square error, robustness w.r.t. to initial values and parameter choice.},
keywords = {Bayesian inference, learning (artificial intelligence), Machine learning, Markov chain Monte Carlo, Markov chain Monte Carlo algorithms, Markov processes, MC methods, MCMC algorithms, MCMC scheme, mean square error, mean square error methods, Monte Carlo methods, optimisation, parallel and interacting chains, Probability density function, Proposals, robustness, Sampling methods, Signal processing, Signal processing algorithms, signal sampling, smelly parallel chains, smelly parallel MCMC chains, Stochastic optimization},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Ruiz2015b,
title = {A Generative Model for Predicting Outcomes in College Basketball},
author = {Francisco J R Ruiz and Fernando Perez-Cruz},
url = {http://www.degruyter.com/view/j/jqas.2015.11.issue-1/jqas-2014-0055/jqas-2014-0055.xml},
doi = {10.1515/jqas-2014-0055},
issn = {1559-0410},
year  = {2015},
date = {2015-03-01},
journal = {Journal of Quantitative Analysis in Sports},
volume = {11},
number = {1 Special Issue},
pages = {39--52},
abstract = {We show that a classical model for soccer can also provide competitive results in predicting basketball outcomes. We modify the classical model in two ways in order to capture both the specific behavior of each National collegiate athletic association (NCAA) conference and different strategies of teams and conferences. Through simulated bets on six online betting houses, we show that this extension leads to better predictive performance in terms of profit we make. We compare our estimates with the probabilities predicted by the winner of the recent Kaggle competition on the 2014 NCAA tournament, and conclude that our model tends to provide results that differ more from the implicit probabilities of the betting houses and, therefore, has the potential to provide higher benefits.},
keywords = {CASI CAM CM, GAMMA-L+ UC3M, Journal, NCAA tournament, Poisson factorization, Probabilistic modeling, variational inference},
pubstate = {published},
tppubtype = {article}
}
@article{Koblents2014b,
title = {A Population Monte Carlo Scheme with Transformed Weights and Its Application to Stochastic Kinetic Models},
author = {Eugenia Koblents and Joaquin Miguez},
url = {http://link.springer.com/10.1007/s11222-013-9440-2 http://gts.tsc.uc3m.es/wp-content/uploads/2014/01/NPMC_A-population-Monte-Carlo-scheme-with-transformed_jma.pdf},
doi = {10.1007/s11222-013-9440-2},
issn = {0960-3174},
year  = {2015},
date = {2015-03-01},
journal = {Statistics and Computing},
volume = {25},
number = {2},
pages = {407--425},
abstract = {This paper addresses the Monte Carlo approximation of posterior probability distributions. In particular, we consider the population Monte Carlo (PMC) technique, which is based on an iterative importance sampling (IS) approach. An important drawback of this methodology is the degeneracy of the importance weights (IWs) when the dimension of either the observations or the variables of interest is high. To alleviate this difficulty, we propose a new method that performs a nonlinear transformation of the IWs. This operation reduces the weight variation, hence it avoids degeneracy and increases the efficiency of the IS scheme, specially when drawing from proposal functions which are poorly adapted to the true posterior. For the sake of illustration, we have applied the proposed algorithm to the estimation of the parameters of a Gaussian mixture model. This is a simple problem that enables us to discuss the main features of the proposed technique. As a practical application, we have also considered the challenging problem of estimating the rate parameters of a stochastic kinetic model (SKM). SKMs are multivariate systems that model molecular interactions in biological and chemical problems. We introduce a particularization of the proposed algorithm to SKMs and present numerical results.},
keywords = {COMPREHENSION, degeneracy of importance weights, Importance sampling, Journal, population Monte Carlo, Stochastic kinetic models},
pubstate = {published},
tppubtype = {article}
}
@article{Varando2015b,
title = {Conditional Density Approximations with Mixtures of Polynomials},
author = {Gherardo Varando and Pedro L L\'{o}pez-Cruz and Thomas D Nielsen and Pedro Larra\~{n}aga and Concha Bielza},
url = {http://doi.wiley.com/10.1002/int.21699 http://cig.fi.upm.es/articles/2015/Varando-2015-IJIS.pdf},
doi = {10.1002/int.21699},
issn = {08848173},
year  = {2015},
date = {2015-03-01},
journal = {International Journal of Intelligent Systems},
volume = {30},
number = {3},
pages = {236--264},
abstract = {Mixtures of polynomials (MoPs) are a nonparametric density estimation technique especially designed for hybrid Bayesian networks with continuous and discrete variables. Algorithms to learn one- and multidimensional (marginal) MoPs from data have recently been proposed. In this paper, we introduce two methods for learning MoP approximations of conditional densities from data. Both approaches are based on learning MoP approximations of the joint density and the marginal density of the conditioning variables, but they differ as to how the MoP approximation of the quotient of the two densities is found. We illustrate and study the methods using data sampled from known parametric distributions, and demonstrate their applicability by learning models based on real neuroscience data. Finally, we compare the performance of the proposed methods with an approach for learning mixtures of truncated basis functions (MoTBFs). The empirical results show that the proposed methods generally yield models that are comparable to or significantly better than those found using the MoTBF-based method.},
keywords = {CASI CAM CM, CIG UPM, Journal},
pubstate = {published},
tppubtype = {article}
}
@article{Ruiz2015bb,
title = {A Generative Model for Predicting Outcomes in College Basketball},
author = {Francisco J R Ruiz and Fernando Perez-Cruz},
url = {http://www.degruyter.com/view/j/jqas.2015.11.issue-1/jqas-2014-0055/jqas-2014-0055.xml},
doi = {10.1515/jqas-2014-0055},
issn = {1559-0410},
year  = {2015},
date = {2015-03-01},
journal = {Journal of Quantitative Analysis in Sports},
volume = {11},
number = {1 Special Issue},
pages = {39--52},
abstract = {We show that a classical model for soccer can also provide competitive results in predicting basketball outcomes. We modify the classical model in two ways in order to capture both the specific behavior of each National collegiate athletic association (NCAA) conference and different strategies of teams and conferences. Through simulated bets on six online betting houses, we show that this extension leads to better predictive performance in terms of profit we make. We compare our estimates with the probabilities predicted by the winner of the recent Kaggle competition on the 2014 NCAA tournament, and conclude that our model tends to provide results that differ more from the implicit probabilities of the betting houses and, therefore, has the potential to provide higher benefits.},
keywords = {CASI CAM CM, GAMMA-L+ UC3M, NCAA tournament, Poisson factorization, Probabilistic modeling, variational inference},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Perez-Cruz2015,
title = {A Blind Nonparametric Non-line of Sight Bias Model for Accurate Localization},
author = {Fernando Perez-Cruz and Howard Huang},
url = {http://ita.ucsd.edu/workshop/15/files/abstract/abstract_1462.txt},
year  = {2015},
date = {2015-02-01},
booktitle = {Information Theory and Applications (ITA)},
address = {San Diego},
abstract = {One of the most promising solutions for accurate localization services is estimating the Time Difference of Arrival (TDoA) with a cellular infrastructure and triangulating the position of the sought device. There are three different elements that limit the accuracy of TDoA: bandwidth/snr, clock accuracy and non-line-of-sight (NLOS) bias. The Cramer-Rao lower bound is well known and can be made sufficiently low (centimeters) with existing technologies. GPS clock accuracy is below 15ns (less than 5 meters). NLOS is difficult to characterize and depends heavily on the environment. We cannot rely on simple distributions to model it and we should not expect it to follow a few typical scenarios. In this talk, we present a nonparametric model for estimating the NLOS bias and an algorithm that learns the model on the fly without feedback on the true position. This procedure allows getting accurate localization in any environment and without needing to fine-tune a priori de NLOS for each base station. The actual accuracy depends on the number of bases that hear the device, but uncontrolled outliers no longer limit it. For a dense infrastructure, we show that the localization error can be measured in a few meters.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Salamanca2014bb,
title = {Approaching the DT Bound Using Linear Codes in the Short Blocklength Regime},
author = {Luis Salamanca and Juan Jos\'{e} Murillo-Fuentes and Pablo M Olmos and Fernando Perez-Cruz and Sergio Verdu},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6957577},
doi = {10.1109/LCOMM.2014.2371032},
issn = {1089-7798},
year  = {2015},
date = {2015-02-01},
journal = {IEEE Communications Letters},
volume = {19},
number = {2},
pages = {123--126},
abstract = {The dependence-testing (DT) bound is one of the strongest achievability bounds for the binary erasure channel (BEC) in the finite block length regime. In this paper, we show that maximum likelihood decoded regular low-density paritycheck (LDPC) codes with at least 5 ones per column almost achieve the DT bound. Specifically, using quasi-regular LDPC codes with block length of 256 bits, we achieve a rate that is less than 1% away from the rate predicted by the DT bound for a word error rate below 103. The results also indicate that the maximum-likelihood solution is computationally feasible for decoding block codes over the BEC with several hundred bits.},
keywords = {binary erasure channel, Channel Coding, Complexity theory, finite blocklength regime, LDPC codes, Maximum likelihood decoding, ML decoding, parity check codes, random coding},
pubstate = {published},
tppubtype = {article}
}
@article{Manzano2015,
title = {Cognitive Self-Scheduled Mechanism for Access Control in Noisy Vehicular Ad Hoc Networks},
author = {Mario Manzano and Felipe Espinosa and \'{A}ngel M Bravo-Santos and Alfredo Gardel-Vicente},
url = {http://www.hindawi.com/journals/mpe/2015/354292/ http://dx.doi.org/10.1155/2015/354292},
doi = {10.1155/2015/354292},
year  = {2015},
date = {2015-01-01},
journal = {Mathematical Problems in Engineering.},
volume = {2015},
pages = {1--12},
abstract = {Within the challenging environment of intelligent transportation systems (ITS), networked control systems such as platooning guidance of autonomous vehicles require innovative mechanisms to provide real-time communications. Although several proposals are currently under discussion, the design of a rapid, efficient, flexible, and reliable medium access control mechanism which meets the specific constraints of such real-time communications applications remains unsolved in this highly dynamic environment. However, cognitive radio (CR) combines the capacity to sense the radio spectrum with the flexibility to adapt to transmission parameters in order to maximize system performance and has thus become an effective approach for the design of dynamic spectrum access (DSA) mechanisms. This paper presents the enhanced noncooperative cognitive division multiple access (ENCCMA) proposal combining time division multiple access (TDMA) and frequency division multiple access (FDMA) schemes with CR techniques to obtain a mechanism fulfilling the requirements of real-time communications. The analysis presented here considers the IEEE WAVE and 802.11p as reference standards; however, the proposed medium access control (MAC) mechanism can be adapted to operate on the physical layer of different standards. The mechanism also offers the advantage of avoiding signaling, thus enhancing system autonomy as well as behavior in adverse scenarios.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Varando2015c,
title = {Decision boundary for discrete Bayesian network classifiers},
author = {Gherardo Varando and Concha Bielza and Pedro Larra\~{n}aga},
url = {http://cig.fi.upm.es/node/881 http://cig.fi.upm.es/articles/2015/Varando-2015-JMLR.pdf},
year  = {2015},
date = {2015-01-01},
journal = {Journal of Machine Learning Research},
abstract = {Bayesian network classi ers are a powerful machine learning tool. In order to evaluate the expressive power of these models, we compute families of polynomials that sign-represent decision functions induced by Bayesian network classi ers. We prove that those families are linear combinations of products of Lagrange basis polynomials. In absence of V -structures in the predictor sub-graph, we are also able to prove that this family of polynomials does indeed characterize the speci c classi er considered. We then use this representation to bound the number of decision functions representable by Bayesian network classi ers with a given structure},
keywords = {Bayesian networks, CASI CAM CM, CIG UPM, decision boundary, Journal, Lagrange basis, polynomial, supervised classication, threshold function},
pubstate = {published},
tppubtype = {article}
}
@article{Manzano2015b,
title = {Cognitive Self-Scheduled Mechanism for Access Control in Noisy Vehicular Ad Hoc Networks},
author = {Mario Manzano and Felipe Espinosa and \'{A}ngel M Bravo-Santos and Alfredo Gardel-Vicente},
url = {http://www.hindawi.com/journals/mpe/2015/354292/ http://dx.doi.org/10.1155/2015/354292},
doi = {10.1155/2015/354292},
year  = {2015},
date = {2015-01-01},
journal = {Mathematical Problems in Engineering.},
volume = {2015},
pages = {1--12},
abstract = {Within the challenging environment of intelligent transportation systems (ITS), networked control systems such as platooning guidance of autonomous vehicles require innovative mechanisms to provide real-time communications. Although several proposals are currently under discussion, the design of a rapid, efficient, flexible, and reliable medium access control mechanism which meets the specific constraints of such real-time communications applications remains unsolved in this highly dynamic environment. However, cognitive radio (CR) combines the capacity to sense the radio spectrum with the flexibility to adapt to transmission parameters in order to maximize system performance and has thus become an effective approach for the design of dynamic spectrum access (DSA) mechanisms. This paper presents the enhanced noncooperative cognitive division multiple access (ENCCMA) proposal combining time division multiple access (TDMA) and frequency division multiple access (FDMA) schemes with CR techniques to obtain a mechanism fulfilling the requirements of real-time communications. The analysis presented here considers the IEEE WAVE and 802.11p as reference standards; however, the proposed medium access control (MAC) mechanism can be adapted to operate on the physical layer of different standards. The mechanism also offers the advantage of avoiding signaling, thus enhancing system autonomy as well as behavior in adverse scenarios.},
keywords = {Journal},
pubstate = {published},
tppubtype = {article}
}
@article{Luengo2014bb,
title = {Blind Analysis of Atrial Fibrillation Electrograms: A Sparsity-Aware Formulation},
author = {David Luengo and Sandra Monzon and Tom Trigano and Javier V\'{i}a and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://content.iospress.com/articles/integrated-computer-aided-engineering/ica00471 
http://www.tsc.uc3m.es/~dluengo/sparseEGM.pdf},
year  = {2015},
date = {2015-01-01},
journal = {Integrated Computer-Aided Engineering},
volume = {22},
number = {1},
pages = {71--85},
abstract = {The problem of blind sparse analysis of electrogram (EGM) signals under atrial fibrillation (AF) conditions is considered in this paper. A mathematical model for the observed signals that takes into account the multiple foci typically appearing inside the heart during AF is firstly introduced. Then, a reconstruction model based on a fixed dictionary is developed and several alternatives for choosing the dictionary are discussed. In order to obtain a sparse solution, which takes into account the biological restrictions of the problem at the same time, the paper proposes using a Least Absolute Shrinkage and Selection Operator (LASSO) regularization followed by a post-processing stage that removes low amplitude coefficients violating the refractory period characteristic of cardiac cells. Finally, spectral analysis is performed on the clean activation sequence obtained from the sparse learning stage in order to estimate the number of latent foci and their frequencies. Simulations on synthetic signals and applications on real data are provided to validate the proposed approach.},
keywords = {atrial fibrillation, biomedical signal processing},
pubstate = {published},
tppubtype = {article}
}
@article{Martin-Fernandez2015bb,
title = {A Bayesian Method for Model Selection in Environmental Noise Prediction},
author = {L Mart\'{i}n-Fern\'{a}ndez and Diego Ruiz and Antonio Torija and Joaquin Miguez},
url = {http://www.researchgate.net/publication/268213140_A_Bayesian_method_for_model_selection_in_environmental_noise_prediction},
issn = {1726-2135},
year  = {2015},
date = {2015-01-01},
journal = {Journal of Environmental Informatics},
volume = {January 20},
abstract = {Environmental noise prediction and modeling are key factors for addressing a proper planning and management of urban sound environments. In this paper we propose a maximum a posteriori (MAP) method to compare nonlinear state-space models that describe the problem of predicting environmental sound levels. The numerical implementation of this method is based on particle filtering and we use a Markov chain Monte Carlo technique to improve the resampling step. In order to demonstrate the validity of the proposed approach for this particular problem, we have conducted a set of experiments where two prediction models are quantitatively compared using real noise measurement data collected in different urban areas.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Bravo-Santos2014b,
title = {Detectors for Cooperative Mesh Networks with Decode-and-Forward Relays},
author = {\'{A}ngel M Bravo-Santos and Petar M Djuric},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6928514},
doi = {10.1109/TSP.2014.2364016},
issn = {1053-587X},
year  = {2015},
date = {2015-01-01},
journal = {IEEE Transactions on Signal Processing},
volume = {63},
number = {1},
pages = {5--17},
publisher = {IEEE},
abstract = {We consider mesh networks composed of groups of relaying nodes which operate in decode-andforward mode. Each node from a group relays information to all the nodes in the next group. We study these networks in two setups, one where the nodes have complete state information about the channels through which they receive the signals, and another when they only have the statistics of the channels. We derive recursive expressions for the probabilities of errors of the nodes and present several implementations of detectors used in these networks. We compare the mesh networks with multi-hop networks formed by a set of parallel sections of multiple relaying nodes. We demonstrate with numerous simulations that there are significant improvements in performance of mesh over multi-hop networks in various scenarios.},
keywords = {Cooperative systems, Detectors, Mesh networks, Modulation, Relays, spread spectrum communication, Wireless communication},
pubstate = {published},
tppubtype = {article}
}
@article{Bravo-Santos2014bb,
title = {Detectors for Cooperative Mesh Networks with Decode-and-Forward Relays},
author = {\'{A}ngel M Bravo-Santos and Petar M Djuric},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6928514},
doi = {10.1109/TSP.2014.2364016},
issn = {1053-587X},
year  = {2015},
date = {2015-01-01},
journal = {IEEE Transactions on Signal Processing},
volume = {63},
number = {1},
pages = {5--17},
publisher = {IEEE},
abstract = {We consider mesh networks composed of groups of relaying nodes which operate in decode-andforward mode. Each node from a group relays information to all the nodes in the next group. We study these networks in two setups, one where the nodes have complete state information about the channels through which they receive the signals, and another when they only have the statistics of the channels. We derive recursive expressions for the probabilities of errors of the nodes and present several implementations of detectors used in these networks. We compare the mesh networks with multi-hop networks formed by a set of parallel sections of multiple relaying nodes. We demonstrate with numerous simulations that there are significant improvements in performance of mesh over multi-hop networks in various scenarios.},
keywords = {Cooperative systems, Detectors, Journal, Mesh networks, Modulation, Relays, spread spectrum communication, Wireless communication},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Farajtabar2014,
title = {Shaping Social Activity by Incentivizing Users},
author = {Mehrdad Farajtabar and Nan Du and Manuel Gomez-rodriguez and Isabel Valera and Hongyuan Zha and Le Song},
url = {http://papers.nips.cc/paper/5365-shaping-social-activity-by-incentivizing-users.pdf},
year  = {2014},
date = {2014-12-01},
booktitle = {Advances in Neural Information Processing Systems},
volume = {December},
pages = {2474--2482},
address = {Montreal},
abstract = {Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network. How much external drive should be provided to each user, such that the network activity can be steered towards a target state? In this paper, we model social events using multivariate Hawkes processes, which can capture both endogenous and exogenous event intensities, and derive a time dependent linear relation between the intensity of exogenous events and the overall network activity. Exploiting this connection, we develop a convex optimization framework for determining the required level of external drive in order for the network to reach a desired activity level. We experimented with event data gathered from Twitter, and show that our method can steer the activity of the network more accurately than alternatives.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{gvazquez-isit2014,
title = {Source-Channel Coding with Multiple Classes},
author = {Irina E Bocharova and Albert Guill\'{e}n i F\`{a}bregas and Boris D Kudryashov and Alfonso Martinez and Adria Tauste Campo and Gonzalo Vazquez-Vilar},
year  = {2014},
date = {2014-06-01},
booktitle = {2014 IEEE International Symposium on Information Theory (ISIT 2014)},
address = {Honolulu, HI, USA},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Taborda2014,
title = {New Information-Estimation Results for Poisson, Binomial and Negative Binomial Models},
author = {Camilo G Taborda and Fernando Perez-Cruz and Dongning Guo},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6875225},
doi = {10.1109/ISIT.2014.6875225},
isbn = {978-1-4799-5186-4},
year  = {2014},
date = {2014-06-01},
booktitle = {2014 IEEE International Symposium on Information Theory},
pages = {2207--2211},
publisher = {IEEE},
address = {Honolulu},
abstract = {In recent years, a number of mathematical relationships have been established between information measures and estimation measures for various models, including Gaussian, Poisson and binomial models. In this paper, it is shown that the second derivative of the input-output mutual information with respect to the input scaling can be expressed as the expectation of a certain Bregman divergence pertaining to the conditional expectations of the input and the input power. This result is similar to that found for the Gaussian model where the Bregman divergence therein is the square distance. In addition, the Poisson, binomial and negative binomial models are shown to be similar in the small scaling regime in the sense that the derivative of the mutual information and the derivative of the relative entropy converge to the same value.},
keywords = {Bregman divergence, Estimation, estimation measures, Gaussian models, Gaussian processes, information measures, information theory, information-estimation results, negative binomial models, Poisson models, Stochastic processes},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Miguez2014,
title = {On the uniform asymptotic convergence of a distributed particle filter},
author = {Joaquin Miguez},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6882385},
doi = {10.1109/SAM.2014.6882385},
isbn = {978-1-4799-1481-4},
year  = {2014},
date = {2014-06-01},
booktitle = {2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM)},
pages = {241--244},
publisher = {IEEE},
address = {A Coru\~{n}a},
abstract = {Distributed signal processing algorithms suitable for their implementation over wireless sensor networks (WSNs) and ad hoc networks with communications and computing capabilities have become a hot topic during the past years. One class of algorithms that have received special attention are particles filters. However, most distributed versions of this type of methods involve various heuristic or simplifying approximations and, as a consequence, classical convergence theorems for standard particle filters do not hold for their distributed counterparts. In this paper, we look into a distributed particle filter scheme that has been proposed for implementation in both parallel computing systems and WSNs, and prove that, under certain stability assumptions regarding the physical system of interest, its asymptotic convergence is guaranteed. Moreover, we show that convergence is attained uniformly over time. This means that approximation errors can be kept bounded for an arbitrarily long period of time without having to progressively increase the computational effort.},
keywords = {ad hoc networks, Approximation algorithms, approximation errors, Approximation methods, classical convergence theorems, Convergence, convergence of numerical methods, distributed particle filter scheme, distributed signal processing algorithms, Monte Carlo methods, parallel computing systems, particle filtering (numerical methods), Signal processing, Signal processing algorithms, stability assumptions, uniform asymptotic convergence, Wireless Sensor Networks, WSNs},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Santiago-Mozos2013,
title = {An Automated Screening System for Tuberculosis},
author = {Ricardo Santiago-Mozos and Fernando Perez-Cruz and Michael Madden and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.tsc.uc3m.es/~antonio/papers/P47_2014_An Automated Screening System for Tuberculosis.pdf http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6630069},
issn = {2168-2208},
year  = {2014},
date = {2014-05-01},
journal = {IEEE journal of biomedical and health informatics},
volume = {18},
number = {3},
pages = {855-862},
publisher = {IEEE},
abstract = {Automated screening systems are commonly used to detect some agent in a sample and take a global decision about the subject (e.g. ill/healthy) based on these detections. We propose a Bayesian methodology for taking decisions in (sequential) screening systems that considers the false alarm rate of the detector. Our approach assesses the quality of its decisions and provides lower bounds on the achievable performance of the screening system from the training data. In addition, we develop a complete screening system for sputum smears in tuberculosis diagnosis, and show, using a real-world database, the advantages of the proposed framework when compared to the commonly used count detections and threshold approach.},
keywords = {Automated screening, Bayesian, Decision making, Sequential analysis, Tuberculosis},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Crisan2014b,
title = {Nested Particle Filters for Sequential Parameter Estimation in Discrete-time State-space Models},
author = {Dan Crisan and Joaquin Miguez},
year  = {2014},
date = {2014-03-01},
booktitle = {SIAM 2014 Conference on Uncertainty Quantification},
address = {Savannah},
abstract = {The problem of estimating the parameters of nonlinear, possibly non-Gaussian discrete-time state models has drawn considerable attention during the past few years, leading to the appearance of general methodologies (SMC2, particle MCMC, recursive ML) that have improved on earlier, simpler extensions of the standard particle filter. However, there is still a lack of recursive (online) methods that can provide a theoretically-grounded approximation of the joint posterior probability distribution of the parameters and the dynamic state variables of the model. In the talk, we will describe a two-layer particle filtering scheme that addresses this problem. Both a recursive algorithm, suitable for online implementation, and some results regarding its asymptotic convergence will be presented.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Impedovo2014b,
title = {Efficient Monte Carlo Methods for Multi-Dimensional Learning with Classifier Chains},
author = {Sebastiano Impedovo and Cheng-Lin Liu and Donato Impedovo and Giuseppe Pirlo and Jesse Read and Luca Martino and David Luengo},
url = {http://www.sciencedirect.com/science/article/pii/S0031320313004160},
year  = {2014},
date = {2014-01-01},
journal = {Pattern Recognition},
volume = {47},
number = {3},
pages = {1535--1546},
abstract = {Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance \textendash at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest-performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for finding a good chain sequence and performing efficient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.},
keywords = {Bayesian inference, Classifier chains, Monte Carlo methods, Multi-dimensional classification, Multi-label classification},
pubstate = {published},
tppubtype = {article}
}
@article{Read2014b,
title = {A Distributed Particle Filter for Nonlinear Tracking in Wireless Sensor Networks},
author = {Jesse Read and Katrin Achutegui and Joaquin Miguez},
url = {http://www.tsc.uc3m.es/~jmiguez/papers/P40_2014_A Distributed Particle Filter for Nonlinear Tracking in Wireless Sensor Networks.pdf 
http://www.sciencedirect.com/science/article/pii/S0165168413004568},
year  = {2014},
date = {2014-01-01},
journal = {Signal Processing},
volume = {98},
pages = {121--134},
abstract = {The use of distributed particle filters for tracking in sensor networks has become popular in recent years. The distributed particle filters proposed in the literature up to now are only approximations of the centralized particle filter or, if they are a proper distributed version of the particle filter, their implementation in a wireless sensor network demands a prohibitive communication capability. In this work, we propose a mathematically sound distributed particle filter for tracking in a real-world indoor wireless sensor network composed of low-power nodes. We provide formal and general descriptions of our methodology and then present the results of both real-world experiments and/or computer simulations that use models fitted with real data. With the same number of particles as a centralized filter, the distributed algorithm is over four times faster, yet our simulations show that, even assuming the same processing speed, the accuracy of the centralized and distributed algorithms is practically identical. The main limitation of the proposed scheme is the need to make all the sensor observations available to every processing node. Therefore, it is better suited to broadcast networks or multihop networks where the volume of generated data is kept low, e.g., by an adequate local pre-processing of the observations.},
keywords = {Distributed filtering, Target tracking, Wireless sensor network},
pubstate = {published},
tppubtype = {article}
}
@article{Alvarado2014,
title = {High-SNR Asymptotics of Mutual Information for Discrete Constellations With Applications to BICM},
author = {Alex Alvarado and Fredrik Brannstrom and Erik Agrell and Tobias Koch},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6671479 
http://www.tsc.uc3m.es/~koch/files/IEEE_TIT_60%282%29.pdf},
issn = {0018-9448},
year  = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {60},
number = {2},
pages = {1061--1076},
abstract = {Asymptotic expressions of the mutual information between any discrete input and the corresponding output of the scalar additive white Gaussian noise channel are presented in the limit as the signal-to-noise ratio (SNR) tends to infinity. Asymptotic expressions of the symbol-error probability (SEP) and the minimum mean-square error (MMSE) achieved by estimating the channel input given the channel output are also developed. It is shown that for any input distribution, the conditional entropy of the channel input given the output, MMSE, and SEP have an asymptotic behavior proportional to the Gaussian Q-function. The argument of the Q-function depends only on the minimum Euclidean distance (MED) of the constellation and the SNR, and the proportionality constants are functions of the MED and the probabilities of the pairs of constellation points at MED. The developed expressions are then generalized to study the high-SNR behavior of the generalized mutual information (GMI) for bit-interleaved coded modulation (BICM). By means of these asymptotic expressions, the long-standing conjecture that Gray codes are the binary labelings that maximize the BICM-GMI at high SNR is proven. It is further shown that for any equally spaced constellation whose size is a power of two, there always exists an anti-Gray code giving the lowest BICM-GMI at high SNR.},
keywords = {additive white Gaussian noise channel, Anti-Gray code, bit-interleaved coded modulation, discrete constellations, Entropy, Gray code, high-SNR asymptotics, IP networks, Labeling, minimum-mean square error, Modulation, Mutual information, Signal to noise ratio, Vectors},
pubstate = {published},
tppubtype = {article}
}
@article{Martin-Fernandez2014,
title = {A Rao-Blackwellized Particle Filter for Joint Parameter Estimation and Biomass Tracking in a Stochastic Predator-Prey System},
author = {L Martin-Fernandez and G Gilioli and E Lanzarone and Joaquin Miguez and S Pasquali and F Ruggeri and D P Ruiz},
url = {http://www.tsc.uc3m.es/~jmiguez/papers/P42_2014_A Rao-Blackwellized Particle Filter for Joint Parameter Estimation and Biomass Tracking in a Stochastic Predator-Prey System.pdf https://www.aimsciences.org/journals/pdfs.jsp?paperID=9557\&amp;amp;mode=full http://gts.tsc.uc3m.es/wp-content/uploads/2014/01/LMF_et_al_MBE13_A-RAO-BLACKWELLIZED-PARTICLE-FILTER_-jma.pdf https://www.aimsciences.org/journals/displayArticlesnew.jsp?paperID=9557},
year  = {2014},
date = {2014-01-01},
journal = {Mathematical Biosciences and Engineering},
volume = {11},
number = {3},
pages = {573--597},
abstract = {Functional response estimation and population tracking in predator- prey systems are critical problems in ecology. In this paper we consider a stochastic predator-prey system with a Lotka-Volterra functional response and propose a particle ltering method for: (a) estimating the behavioral parameter representing the rate of e ective search per predator in the functional response and (b) forecasting the population biomass using eld data. In particular, the proposed technique combines a sequential Monte Carlo sampling scheme for tracking the time-varying biomass with the analytical integration of the unknown behavioral parameter. In order to assess the performance of the method, we show results for both synthetic and observed data collected in an acarine predator-prey system, namely the pest mite Tetranychus urticae and the predatory mite Phytoseiulus persimilis.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Pineiro-Ave2014,
title = {Target Detection for Low Cost Uncooled MWIR Cameras Based on Empirical Mode Decomposition},
author = {Jos\'{e} Pi\~{n}eiro-Ave and Manuel Blanco-Velasco and Fernando Cruz-Rold\'{a}n and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.tsc.uc3m.es/~antonio/papers/P49_2014_Target Detection for Low Cost Uncooled MWIR Cameras Based on Empirical Mode Decomposition.pdf 
http://www.sciencedirect.com/science/article/pii/S1350449514000085},
issn = {13504495},
year  = {2014},
date = {2014-01-01},
journal = {Infrared Physics \&amp;amp; Technology},
volume = {63},
pages = {222--231},
abstract = {In this work, a novel method for detecting low intensity fast moving objects with low cost Medium Wavelength Infrared (MWIR) cameras is proposed. The method is based on background subtraction in a video sequence obtained with a low density Focal Plane Array (FPA) of the newly available uncooled lead selenide (PbSe) detectors. Thermal instability along with the lack of specific electronics and mechanical devices for canceling the effect of distortion make background image identification very difficult. As a result, the identification of targets is performed in low signal to noise ratio (SNR) conditions, which may considerably restrict the sensitivity of the detection algorithm. These problems are addressed in this work by means of a new technique based on the empirical mode decomposition, which accomplishes drift estimation and target detection. Given that background estimation is the most important stage for detecting, a previous denoising step enabling a better drift estimation is designed. Comparisons are conducted against a denoising technique based on the wavelet transform and also with traditional drift estimation methods such as Kalman filtering and running average. The results reported by the simulations show that the proposed scheme has superior performance.},
keywords = {Background subtraction, Change detection, Denoising, Drift, Empirical Mode Decomposition (EMD), Intrinsic Mode Function (IMF)},
pubstate = {published},
tppubtype = {article}
}
@article{Koblents2014bb,
title = {A Population Monte Carlo Scheme with Transformed Weights and Its Application to Stochastic Kinetic Models},
author = {Eugenia Koblents and Joaquin Miguez},
url = {http://link.springer.com/10.1007/s11222-013-9440-2 http://gts.tsc.uc3m.es/wp-content/uploads/2014/01/NPMC_A-population-Monte-Carlo-scheme-with-transformed_jma.pdf},
issn = {0960-3174},
year  = {2014},
date = {2014-01-01},
journal = {Statistics and Computing},
number = {(to appear)},
abstract = {This paper addresses the Monte Carlo approximation of posterior probability distributions. In particular, we consider the population Monte Carlo (PMC) technique, which is based on an iterative importance sampling (IS) approach. An important drawback of this methodology is the degeneracy of the importance weights (IWs) when the dimension of either the observations or the variables of interest is high. To alleviate this difficulty, we propose a new method that performs a nonlinear transformation of the IWs. This operation reduces the weight variation, hence it avoids degeneracy and increases the efficiency of the IS scheme, specially when drawing from proposal functions which are poorly adapted to the true posterior. For the sake of illustration, we have applied the proposed algorithm to the estimation of the parameters of a Gaussian mixture model. This is a simple problem that enables us to discuss the main features of the proposed technique. As a practical application, we have also considered the challenging problem of estimating the rate parameters of a stochastic kinetic model (SKM). SKMs are multivariate systems that model molecular interactions in biological and chemical problems. We introduce a particularization of the proposed algorithm to SKMs and present numerical results.},
keywords = {degeneracy of importance weights, Importance sampling, population Monte Carlo, Stochastic kinetic models},
pubstate = {published},
tppubtype = {article}
}
@article{Crisan2014bb,
title = {Particle-Kernel Estimation of the Filter Density in State-Space Models},
author = {Dan Crisan and Joaquin Miguez},
url = {http://www.tsc.uc3m.es/~jmiguez/papers/P43_2014_Particle-Kernel Estimation of the Filter Density in State-Space Models.pdf 
http://www.bernoulli-society.org/index.php/publications/bernoulli-journal/bernoulli-journal-papers},
year  = {2014},
date = {2014-01-01},
journal = {Bernoulli},
volume = {(to appear},
abstract = {Sequential Monte Carlo (SMC) methods, also known as particle filters, are simulation-based recursive algorithms for the approximation of the a posteriori probability measures generated by state-space dynamical models. At any given time t, a SMC method produces a set of samples over the state space of the system of interest (often termed “particles”) that is used to build a discrete and random approximation of the posterior probability distribution of the state variables, conditional on a sequence of available observations. One potential application of the methodology is the estimation of the densities associated to the sequence of a posteriori distributions. While practitioners have rather freely applied such density approximations in the past, the issue has received less attention from a theoretical perspective. In this paper, we address the problem of constructing kernel-based estimates of the posterior probability density function and its derivatives, and obtain asymptotic convergence results for the estimation errors. In particular, we find convergence rates for the approximation errors that hold uniformly on the state space and guarantee that the error vanishes almost surely as the number of particles in the filter grows. Based on this uniform convergence result, we first show how to build continuous measures that converge almost surely (with known rate) toward the posterior measure and then address a few applications. The latter include maximum a posteriori estimation of the system state using the approximate derivatives of the posterior density and the approximation of functionals of it, e.g., Shannon’s entropy.},
keywords = {density estimation, Markov systems., Models, Sequential Monte Carlo, state-space, stochastic filtering},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Martino2014,
title = {An Adaptive Population Importance Sampler},
author = {Luca Martino and V\'{i}ctor Elvira and David Luengo},
url = {http://www.icassp2014.org/home.html},
year  = {2014},
date = {2014-01-01},
booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)},
address = {Florencia},
keywords = {ALCIT, COMPREHENSION},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Pastore2014,
title = {A Rate-Splitting Approach to Fading Multiple-Access Channels with Imperfect Channel-State Information},
author = {Adriano Pastore and Tobias Koch and Javier Rodriguez Fonollosa},
url = {http://www.tsc.uc3m.es/~koch/files/IZS_2014_009-012.pdf http://e-collection.library.ethz.ch/eserv/eth:8192/eth-8192-01.pdf},
year  = {2014},
date = {2014-01-01},
booktitle = {International Zurich Seminar on Communications (IZS)},
address = {Zurich},
abstract = {As shown by M´edard, the capacity of fading channels with imperfect channel-state information (CSI) can be lowerbounded by assuming a Gaussian channel input and by treating the unknown portion of the channel multiplied by the channel input as independent worst-case (Gaussian) noise. Recently, we have demonstrated that this lower bound can be sharpened by a rate-splitting approach: by expressing the channel input as the sum of two independent Gaussian random variables (referred to as layers), say X = X1+X2, and by applying M´edard’s bounding technique to first lower-bound the capacity of the virtual channel from X1 to the channel output Y (while treating X2 as noise), and then lower-bound the capacity of the virtual channel from X2 to Y (while assuming X1 to be known), one obtains a lower bound that is strictly larger than M´edard’s bound. This ratesplitting approach is reminiscent of an approach used by Rimoldi and Urbanke to achieve points on the capacity region of the Gaussian multiple-access channel (MAC). Here we blend these two rate-splitting approaches to derive a novel inner bound on the capacity region of the memoryless fading MAC with imperfect CSI. Generalizing the above rate-splitting approach to more than two layers, we show that, irrespective of how we assign powers to each layer, the supremum of all rate-splitting bounds is approached as the number of layers tends to infinity, and we derive an integral expression for this supremum. We further derive an expression for the vertices of the best inner bound, maximized over the number of layers and over all power assignments.},
keywords = {ALCIT},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Ruiz2014,
title = {Bayesian Nonparametric Comorbidity Analysis of Psychiatric Disorders},
author = {Francisco J R Ruiz and Isabel Valera and Carlos Blanco and Fernando Perez-Cruz},
url = {http://jmlr.org/papers/volume15/ruiz14a/ruiz14a.pdf 
http://arxiv.org/abs/1401.7620},
year  = {2014},
date = {2014-01-01},
journal = {Journal of Machine Learning Research},
volume = {15},
number = {1},
pages = {1215--1248},
abstract = {The analysis of comorbidity is an open and complex research field in the branch of psychiatry, where clinical experience and several studies suggest that the relation among the psychiatric disorders may have etiological and treatment implications. In this paper, we are interested in applying latent feature modeling to find the latent structure behind the psychiatric disorders that can help to examine and explain the relationships among them. To this end, we use the large amount of information collected in the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) database and propose to model these data using a nonparametric latent model based on the Indian Buffet Process (IBP). Due to the discrete nature of the data, we first need to adapt the observation model for discrete random variables. We propose a generative model in which the observations are drawn from a multinomial-logit distribution given the IBP matrix. The implementation of an efficient Gibbs sampler is accomplished using the Laplace approximation, which allows integrating out the weighting factors of the multinomial-logit likelihood model. We also provide a variational inference algorithm for this model, which provides a complementary (and less expensive in terms of computational complexity) alternative to the Gibbs sampler allowing us to deal with a larger number of data. Finally, we use the model to analyze comorbidity among the psychiatric disorders diagnosed by experts from the NESARC database.},
keywords = {ALCIT, Bayesian Non-parametrics, categorical observations, Indian Buet Process, Laplace approximation, multinomial-logit function, variational inference},
pubstate = {published},
tppubtype = {article}
}
@article{O'Mahony2014,
title = {Objective diagnosis of ADHD using IMUs},
author = {Niamh O'Mahony and Blanca Florentino-Lia\~{n}o and Juan J Carballo and Enrique Baca-Garc\'{i}a and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.tsc.uc3m.es/~antonio/papers/P50_2014_Objective Diagnosis of ADHD Using IMUs.pdf 
http://www.sciencedirect.com/science/article/pii/S1350453314000459},
issn = {1873-4030},
year  = {2014},
date = {2014-01-01},
journal = {Medical engineering \&amp;amp; physics},
volume = {36},
number = {7},
pages = {922--6},
abstract = {This work proposes the use of miniature wireless inertial sensors as an objective tool for the diagnosis of ADHD. The sensors, consisting of both accelerometers and gyroscopes to measure linear and rotational movement, respectively, are used to characterize the motion of subjects in the setting of a psychiatric consultancy. A support vector machine is used to classify a group of subjects as either ADHD or non-ADHD and a classification accuracy of greater than 95% has been achieved. Separate analyses of the motion data recorded during various activities throughout the visit to the psychiatric consultancy show that motion recorded during a continuous performance test (a forced concentration task) provides a better classification performance than that recorded during "free time".},
keywords = {Attention deficit/hyperactivity disorder, Classification, Inertial sensors, Machine learning, Objective diagnosis},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Montoya-Martinez2014,
title = {Structured Sparse-Low Rank Matrix Factorization for the EEG Inverse Problem},
author = {Jair Montoya-Martinez and Antonio Art\'{e}s-Rodr\'{i}guez and Massimiliano Pontil},
url = {http://www.conwiz.dk/cgi-all/cip2014/view_abstract.pl?idno=21},
year  = {2014},
date = {2014-01-01},
booktitle = {4th International Workshop on Cognitive Information Processing (CIP 2014)},
address = {Copenhagen},
abstract = {We consider the estimation of the Brain Electrical Sources (BES) matrix from noisy EEG measurements, commonly named as the EEG inverse problem. We propose a new method based on the factorization of the BES as a product of a sparse coding matrix and a dense latent source matrix. This structure is enforced by minimizing a regularized functional that includes the $backslash ell_21$-norm of the coding matrix and the squared Frobenius norm of the latent source matrix. We develop an alternating optimization algorithm to solve the resulting nonsmooth-nonconvex minimization problem. We have evaluated our approach under a simulated scenario consisting on estimating a synthetic BES matrix with 5124 sources. We compare the performance of our method respect to the Lasso, Group Lasso, Sparse Group Lasso and Trace norm regularizers.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Elvira2014,
title = {A Novel Feature Extraction Technique for Human Activity Recognition},
author = {V\'{i}ctor Elvira and Alfredo Nazabal and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://edas.info/p15153#S1569490857},
year  = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE Workshop on Statistical Signal Processing (SSP 2014)},
address = {Gold Coast},
abstract = {This work presents a novel feature extraction technique for human activity recognition using inertial and magnetic sensors. The proposed method estimates the orientation of the person with respect to the earth frame by using quaternion representation. This estimation is performed automatically without any extra information about where the sensor is placed on the body of the person. Furthermore, the method is also robust to displacements of the sensor with respect to the body. This novel feature extraction technique is used to feed a classification algorithm showing excellent results that outperform those obtained by an existing state-of-the-art feature extraction technique.},
keywords = {Activity Classification, Ambulatory Monitoring, Features Extraction, Inertial sensors, Magnetic, orientation estimation, Quaternions., sensors},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Martino2014b,
title = {Orthogonal MCMC Algorithms},
author = {Luca Martino and V\'{i}ctor Elvira and David Luengo and Antonio Art\'{e}s-Rodr\'{i}guez and Jukka Corander},
url = {http://edas.info/p15153#S1569490857},
year  = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE Workshop on Statistical Signal Processing (SSP 2014)},
address = {Gold Coast},
abstract = {Monte Carlo (MC) methods are widely used in signal processing, machine learning and stochastic optimization. A wellknown class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce a novel parallel interacting MCMC scheme, where the parallel chains share information using another MCMC technique working on the entire population of current states. These parallel “vertical” chains are led by random-walk proposals, whereas the “horizontal” MCMC uses a independent proposal, which can be easily adapted by making use of all the generated samples. Numerical results show the advantages of the proposed sampling scheme in terms of mean absolute error, as well as robustness w.r.t. to initial values and parameter choice.},
keywords = {Bayesian inference, Markov Chain Monte Carlo (MCMC), Parallel Chains, population Monte Carlo},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Trigano2014,
title = {Grouped Sparsity Algorithm for Multichannel Intracardiac ECG Synchronization},
author = {Tom Trigano and V Kolesnikov and David Luengo and Antonio Art\'{e}s-Rodr\'{i}guez},
year  = {2014},
date = {2014-01-01},
booktitle = {22nd European Signal Processing Conference (EUSIPCO 2014)},
address = {Lisbon},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Montoya-Martinez2014b,
title = {A Regularized Matrix Factorization Approach to Induce Structured Sparse-Low Rank Solutions in the EEG Inverse Problem},
author = {Jair Montoya-Martinez and Antonio Art\'{e}s-Rodr\'{i}guez and Massimiliano Pontil and Lars Kai Hansen},
url = {http://www.tsc.uc3m.es/~antonio/papers/P48_2014_A Regularized Matrix Factorization Approach to Induce Structured Sparse-Low Rank Solutions in the EEG Inverse Problem.pdf 
http://asp.eurasipjournals.com/content/2014/1/97/abstract},
issn = {1687-6180},
year  = {2014},
date = {2014-01-01},
journal = {EURASIP Journal on Advances in Signal Processing},
volume = {2014},
number = {1},
pages = {97},
publisher = {Springer},
abstract = {We consider the estimation of the Brain Electrical Sources (BES) matrix from noisy Electroencephalographic (EEG) measurements, commonly named as the EEG inverse problem. We propose a new method to induce neurophysiological meaningful solutions, which takes into account the smoothness, structured sparsity and low rank of the BES matrix. The method is based on the factorization of the BES matrix as a product of a sparse coding matrix and a dense latent source matrix. The structured sparse-low rank structure is enforced by minimizing a regularized functional that includes the l21-norm of the coding matrix and the squared Frobenius norm of the latent source matrix. We develop an alternating optimization algorithm to solve the resulting nonsmooth-nonconvex minimization problem. We analyze the convergence of the optimization procedure, and we compare, under different synthetic scenarios, the performance of our method respect to the Group Lasso and Trace Norm regularizers when they are applied directly to the target matrix.},
keywords = {Low rank, Matrix factorization, Nonsmooth-nonconvex optimization, Regularization, Structured sparsity},
pubstate = {published},
tppubtype = {article}
}
@article{Pastore2014a,
title = {A Rate-Splitting Approach to Fading Channels With Imperfect Channel-State Information},
author = {Pastore A and Tobias Koch and Javier Rodriguez Fonollosa},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6832779 http://www.tsc.uc3m.es/~koch/files/IEEE_TIT_60(7).pdf http://arxiv.org/pdf/1301.6120.pdf},
issn = {0018-9448},
year  = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {60},
number = {7},
pages = {4266--4285},
publisher = {IEEE},
abstract = {As shown by M\'{e}dard, the capacity of fading channels with imperfect channel-state information can be lower-bounded by assuming a Gaussian channel input (X) with power (P) and by upper-bounding the conditional entropy (h(X|Y,hat {H})) by the entropy of a Gaussian random variable with variance equal to the linear minimum mean-square error in estimating (X) from ((Y,hat {H})) . We demonstrate that, using a rate-splitting approach, this lower bound can be sharpened: by expressing the Gaussian input (X) as the sum of two independent Gaussian variables (X_1) and (X_2) and by applying M\'{e}dard's lower bound first to bound the mutual information between (X_1) and (Y) while treating (X_2) as noise, and by applying it a second time to the mutual information between (X_2) and (Y) while assuming (X_1) to be known, we obtain a capacity lower bound that is strictly larger than M\'{e}dard's lower bound. We then generalize this approach to an arbi- rary number (L) of layers, where (X) is expressed as the sum of (L) independent Gaussian random variables of respective variances (P_ell ) , (ell = 1,dotsc ,L) summing up to (P) . Among all such rate-splitting bounds, we determine the supremum over power allocations (P_ell ) and total number of layers (L) . This supremum is achieved for (L rightarrow infty ) and gives rise to an analytically expressible capacity lower bound. For Gaussian fading, this novel bound is shown to converge to the Gaussian-input mutual information as the signal-to-noise ratio (SNR) grows, provided that the variance of the channel estimation error (H-hat {H}) tends to zero as the SNR tends to infinity.},
keywords = {channel capacity, COMONSENS, DEIPRO, Entropy, Fading, fading channels, flat fading, imperfect channel-state information, MobileNET, Mutual information, OTOSiS, Random variables, Receivers, Signal to noise ratio, Upper bound},
pubstate = {published},
tppubtype = {article}
}
@article{TausteCampo2014,
title = {A Derivation of the Source-Channel Error Exponent Using Nonidentical Product Distributions},
author = {Adria Tauste Campo and Gonzalo Vazquez-Vilar and Albert Guill\'{e}n i F\`{a}bregas and Tobias Koch and Alfonso Martinez},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6803047 http://www.tsc.uc3m.es/~koch/files/IEEE_TIT_60(6).pdf},
issn = {0018-9448},
year  = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {60},
number = {6},
pages = {3209--3217},
publisher = {IEEE},
abstract = {This paper studies the random-coding exponent of joint source-channel coding for a scheme where source messages are assigned to disjoint subsets (referred to as classes), and codewords are independently generated according to a distribution that depends on the class index of the source message. For discrete memoryless systems, two optimally chosen classes and product distributions are found to be sufficient to attain the sphere-packing exponent in those cases where it is tight.},
keywords = {ALCIT, Channel Coding, COMONSENS, DEIPRO, error probability, joint source-channel coding, Joints, MobileNET, Probability distribution, product distributions, random coding, Reliability, reliability function, sphere-packing bound, Upper bound},
pubstate = {published},
tppubtype = {article}
}
@article{Cespedes2014,
title = {Expectation Propagation Detection for High-order High-dimensional MIMO Systems},
author = {Javier Cespedes and Pablo M Olmos and Matilde Sanchez-Fernandez and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6841617},
issn = {0090-6778},
year  = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Communications},
volume = {PP},
number = {99},
pages = {1--1},
abstract = {Modern communications systems use multiple-input multiple-output (MIMO) and high-order QAM constellations for maximizing spectral efficiency. However, as the number of antennas and the order of the constellation grow, the design of efficient and low-complexity MIMO receivers possesses big technical challenges. For example, symbol detection can no longer rely on maximum likelihood detection or sphere-decoding methods, as their complexity increases exponentially with the number of transmitters/receivers. In this paper, we propose a low-complexity high-accuracy MIMO symbol detector based on the Expectation Propagation (EP) algorithm. EP allows approximating iteratively at polynomial-time the posterior distribution of the transmitted symbols. We also show that our EP MIMO detector outperforms classic and state-of-the-art solutions reducing the symbol error rate at a reduced computational complexity.},
keywords = {Approximation methods, computational complexity, Detectors, MIMO, Signal to noise ratio, Vectors},
pubstate = {published},
tppubtype = {article}
}
@article{Read2014bb,
title = {Multi-Dimensional Classification with Super-Classes},
author = {Jesse Read and Concha Bielza and Pedro Larranaga},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6648319},
issn = {1041-4347},
year  = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Knowledge and Data Engineering},
volume = {26},
number = {7},
pages = {1720--1733},
publisher = {IEEE},
abstract = {The multi-dimensional classification problem is a generalisation of the recently-popularised task of multi-label classification, where each data instance is associated with multiple class variables. There has been relatively little research carried out specific to multi-dimensional classification and, although one of the core goals is similar (modelling dependencies among classes), there are important differences; namely a higher number of possible classifications. In this paper we present method for multi-dimensional classification, drawing from the most relevant multi-label research, and combining it with important novel developments. Using a fast method to model the conditional dependence between class variables, we form super-class partitions and use them to build multi-dimensional learners, learning each super-class as an ordinary class, and thus explicitly modelling class dependencies. Additionally, we present a mechanism to deal with the many class values inherent to super-classes, and thus make learning efficient. To investigate the effectiveness of this approach we carry out an empirical evaluation on a range of multi-dimensional datasets, under different evaluation metrics, and in comparison with high-performing existing multi-dimensional approaches from the literature. Analysis of results shows that our approach offers important performance gains over competing methods, while also exhibiting tractable running time.},
keywords = {Accuracy, Bayes methods, Classification, COMPRHENSION, conditional dependence, Context, core goals, data instance, evaluation metrics, Integrated circuit modeling, modeling class dependencies, multi-dimensional, Multi-dimensional classification, multidimensional classification problem, multidimensional datasets, multidimensional learners, multilabel classification, multilabel research, multiple class variables, ordinary class, pattern classification, problem transformation, recently-popularized task, super classes, super-class partitions, tractable running time, Training, Vectors},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Pradier2014,
title = {Map/Reduce Uncollapsed Gibbs Sampling for Bayesian Non Parametric Models},
author = {Melanie F. Pradier and Pablo Garcia-Moreno and Francisco J R Ruiz and Isabel Valera and Harold Molina-Bulla and Fernando Perez-Cruz},
year  = {2014},
date = {2014-01-01},
booktitle = {NIPS Workshop on Software Engineering for Machine Learning},
address = {Montreal},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ruiz2014b,
title = {True Natural Gradient of Collapsed Variational Bayes},
author = {Francisco J R Ruiz and Neil D Lawrence and James Hensman},
year  = {2014},
date = {2014-01-01},
booktitle = {NIPS Workshop on Advances in Variational Inference},
address = {Montreal},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Valera2014a,
title = {Infinite Factorial Unbounded Hidden Markov Model for Blind Multiuser Channel Estimation},
author = {Isabel Valera and Francisco J R Ruiz and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6844506},
isbn = {978-1-4799-3696-0},
year  = {2014},
date = {2014-01-01},
booktitle = {2014 4th International Workshop on Cognitive Information Processing (CIP)},
pages = {1--6},
publisher = {IEEE},
address = {Copenhagen},
abstract = {Bayesian nonparametric models allow solving estimation and detection problems with an unbounded number of degrees of freedom. In multiuser multiple-input multiple-output (MIMO) communication systems we might not know the number of active users and the channel they face, and assuming maximal scenarios (maximum number of transmitters and maximum channel length) might degrade the receiver performance. In this paper, we propose a Bayesian nonparametric prior and its associated inference algorithm, which is able to detect an unbounded number of users with an unbounded channel length. This generative model provides the dispersive channel model for each user and a probabilistic estimate for each transmitted symbol in a fully blind manner, i.e., without the need of pilot (training) symbols.},
keywords = {Bayes methods, Bayesian non parametrics, Bayesian nonparametric models, blind multiuser channel estimation, Channel estimation, degrees of freedom, detection problems, dispersive channel model, generative model, Hidden Markov models, HMM, inference algorithm, infinite factorial unbounded hidden Markov model, Markov chain Monte Carlo, Markov processes, MIMO, MIMO communication, MIMO communication systems, multiple-input multiple-output (MIMO), multiple-input multiple-output communication syste, receiver performance, Receivers, Signal to noise ratio, Transmitters, unbounded channel length, unbounded number, user detection},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Gopalan2014,
title = {Bayesian Nonparametric Poisson Factorization for Recommendation Systems},
author = {Prem Gopalan and Francisco J R Ruiz and Rajesh Ranganath and David M Blei},
year  = {2014},
date = {2014-01-01},
booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
address = {Reykjavik},
abstract = {We develop a Bayesian nonparametric Poisson factorization model for recommendation systems. Poisson factorization implicitly models each user's limited budget of attention (or money) that allows consumption of only a small subset of the available items. In our Bayesian nonparametric variant, the number of latent components is theoretically unbounded and e ectively estimated when computing a posterior with observed user behavior data. To approximate the posterior, we develop an ecient variational inference algorithm. It adapts the dimensionality of the latent components to the data, only requires iteration over the user/item pairs that have been rated, and has computational complexity on the same order as for a parametric model with xed dimensionality. We studied our model and algorithm with large realworld data sets of user-movie preferences. Our model eases the computational burden of searching for the number of latent components and gives better predictive performance than its parametric counterpart.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Yang2014b,
title = {Dispersion of Quasi-Static MIMO Fading Channels via Stokes' Theorem},
author = {Wei Yang and Giuseppe Durisi and Tobias Koch and Yury Polyanskiy},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6875198},
isbn = {978-1-4799-5186-4},
year  = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE International Symposium on Information Theory},
pages = {2072--2076},
publisher = {IEEE},
address = {Honolulu},
abstract = {This paper analyzes the channel dispersion of quasi-static multiple-input multiple-output fading channels with no channel state information at the transmitter. We show that the channel dispersion is zero under mild conditions on the fading distribution. The proof of our result is based on Stokes' theorem, which deals with the integration of differential forms on manifolds with boundary.},
keywords = {channel capacity, differential form integration, Dispersion, Fading, fading channels, fading distribution, integration, Manifolds, Measurement, MIMO, MIMO communication, quasistatic MIMO fading channels dispersion, quasistatic multiple-input multiple-output fading, radio transmitters, Random variables, Stoke Theorem, transmitter},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Koch2014,
title = {On the Dither-Quantized Gaussian Channel at Low SNR},
author = {Tobias Koch},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6874820},
isbn = {978-1-4799-5186-4},
year  = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE International Symposium on Information Theory},
pages = {186--190},
publisher = {IEEE},
address = {Honolulu},
abstract = {We study the capacity of the peak-and-average-power-limited Gaussian channel when its output is quantized using a dithered, infinite-level, uniform quantizer of step size $Delta$. We focus on the low signal-to-noise-ratio (SNR) regime, where communication at low spectral efficiencies takes place. We show that, when the peak-power constraint is absent, the low-SNR asymptotic capacity is equal to that of the unquantized channel irrespective of $Delta$. We further derive an expression for the low-SNR asymptotic capacity for finite peak-to-average-power ratios and evaluate it in the low- and high-resolution limit. We demonstrate that, in this case, the low-SNR asymptotic capacity converges to that of the unquantized channel when $Delta$ tends to zero, and it tends to zero when $Delta$ tends to infinity.},
keywords = {Additive noise, channel capacity, dither quantized Gaussian channel, Entropy, Gaussian channels, low signal-to-noise-ratio, low-SNR asymptotic capacity, peak power constraint, peak-and-average-power-limited Gaussian channel, Quantization (signal), Signal to noise ratio},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ostman2014,
title = {Diversity Versus Multiplexing at Finite Blocklength},
author = {Johan Ostman and Wei Yang and Giuseppe Durisi and Tobias Koch},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6933444},
isbn = {978-1-4799-5863-4},
year  = {2014},
date = {2014-01-01},
booktitle = {2014 11th International Symposium on Wireless Communications Systems (ISWCS)},
pages = {702--706},
publisher = {IEEE},
address = {Barcelona},
abstract = {A finite blocklenth analysis of the diversity-multiplexing tradeoff is presented, based on nonasymptotic bounds on the maximum channel coding rate of multiple-antenna block-memoryless Rayleigh-fading channels. The bounds in this paper allow one to numerically assess for which packet size, number of antennas, and degree of channel selectivity, diversity-exploiting schemes are close to optimal, and when instead the available spatial degrees of freedom should be used to provide spatial multiplexing. This finite blocklength view on the diversity-multiplexing tradeoff provides insights on the design of delay-sensitive ultra-reliable communication links.},
keywords = {Antennas, Channel Coding, channel selectivity, Coherence, delay-sensitive ultra-reliable communication links, diversity reception, diversity-exploiting schemes, diversity-multiplexing tradeoff, Fading, finite blocklength analysis, maximum channel coding rate, multiple-antenna block-memoryless Rayleigh-fading, Multiplexing, nonasymptotic bounds, packet size, radio links, Rayleigh channels, Time-frequency analysis, Transmitters, Upper bound},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Cespedes2014b,
title = {Improved Performance of LDPC-Coded MIMO Systems with EP-based Soft-Decisions},
author = {Javier Cespedes and Pablo M Olmos and Matilde Sanchez-Fernandez and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6875183},
isbn = {978-1-4799-5186-4},
year  = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE International Symposium on Information Theory},
pages = {1997--2001},
publisher = {IEEE},
address = {Honolulu},
abstract = {Modern communications systems use efficient encoding schemes, multiple-input multiple-output (MIMO) and high-order QAM constellations for maximizing spectral efficiency. However, as the dimensions of the system grow, the design of efficient and low-complexity MIMO receivers possesses technical challenges. Symbol detection can no longer rely on conventional approaches for posterior probability computation due to complexity. Marginalization of this posterior to obtain per-antenna soft-bit probabilities to be fed to a channel decoder is computationally challenging when realistic signaling is used. In this work, we propose to use Expectation Propagation (EP) algorithm to provide an accurate low-complexity Gaussian approximation to the posterior, easily solving the posterior marginalization problem. EP soft-bit probabilities are used in an LDPC-coded MIMO system, achieving outstanding performance improvement compared to similar approaches in the literature for low-complexity LDPC MIMO decoding.},
keywords = {Approximation algorithms, Approximation methods, approximation theory, Channel Coding, channel decoder, communication complexity, complexity, Complexity theory, Detectors, encoding scheme, EP soft bit probability, EP-based soft decision, error statistics, expectation propagation, expectation-maximisation algorithm, expectation-propagation algorithm, Gaussian approximation, Gaussian channels, LDPC, LDPC coded MIMO system, Low Complexity receiver, MIMO, MIMO communication, MIMO communication systems, MIMO receiver, modern communication system, multiple input multiple output, parity check codes, per-antenna soft bit probability, posterior marginalization problem, posterior probability computation, QAM constellation, Quadrature amplitude modulation, radio receivers, signaling, spectral analysis, spectral efficiency maximization, symbol detection, telecommunication signalling, Vectors},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Stinner2014,
title = {Analyzing Finite-length Protograph-Based Spatially Coupled LDPC Codes},
author = {Markus Stinner and Pablo M Olmos},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6874961},
isbn = {978-1-4799-5186-4},
year  = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE International Symposium on Information Theory},
pages = {891--895},
publisher = {IEEE},
address = {Honolulu},
abstract = {The peeling decoding for spatially coupled low-density parity-check (SC-LDPC) codes is analyzed for a binary erasure channel. An analytical calculation of the mean evolution of degree-one check nodes of protograph-based SC-LDPC codes is given and an estimate for the covariance evolution of degree-one check nodes is proposed in the stable decoding phase where the decoding wave propagates along the chain of coupled codes. Both results are verified numerically. Protograph-based SC-LDPC codes turn out to have a more robust behavior than unstructured random SC-LDPC codes. Using the analytically calculated parameters, the finite-length scaling laws for these constructions are given and verified by numerical simulations.},
keywords = {binary erasure channel, covariance analysis, covariance evolution, Decoding, degree-one check nodes, Error analysis, finite-length protograph, mean evolution, Monte Carlo methods, parity check codes, peeling decoding, protograph-based SC-LDPC codes, spatially coupled low-density parity-check codes, stable decoding phase, Steady-state, Vectors},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Olmos2014,
title = {Improving the Finite-Length Performance of Long SC-LDPC Code Chains by Connecting Consecutive Chains},
author = {Pablo M Olmos and David G M Mitchell and Dimitri Truhachev and Daniel J Costello},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6955088},
year  = {2014},
date = {2014-01-01},
booktitle = {8th IEEE International Symposium on Turbo Codes \&amp;amp; Iterative Information Processing},
pages = {72--76},
publisher = {IEEE},
address = {Bremen},
abstract = {We propose a novel encoding/transmission scheme called continuous chain (CC) transmission that is able to improve the finite-length performance of a system using long spatially coupled low-density parity-check (SC-LDPC) code chains. First, we show that the decoding of SC-LDPC code chains is more reliable for shorter chain lengths, i.e., the scaling between block error rate and gap to threshold is more favorable for shorter chains. This motivates the use of CC transmission in which, instead of transmitting a sequence of independent codewords from a long SC-LDPC chain, we connect multiple chains in a layered format, where encoding, transmission, and decoding are now performed in a continuous fashion. Finally, we show that CC transmission can be implemented with only a small increase in decoding complexity or delay with respect to a system employing a single SC-LDPC code chain for transmission},
keywords = {Decoding, Error analysis, error probability, Information processing, parity check codes, Turbo codes},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Djuric2014,
title = {Cooperative Mesh Networks with EGC Detectors},
author = {Petar M Djuric and \'{A}ngel M Bravo-Santos},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6882381},
isbn = {978-1-4799-1481-4},
year  = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM)},
pages = {225--228},
publisher = {IEEE},
address = {A Coru\~{n}a},
abstract = {We address mesh networks with decode and forward relays that use binary modulations. For detection, the nodes employ equal gain combining, which is appealing because it is very easy to implement. We study the performance of these networks and compare it to that of multihop multi-branch networks. We also examine the performance of the networks when both the number of groups and total number of nodes are fixed but the topology of the network varies. We demonstrate the performance of these networks where the channels are modeled with Nakagami distributions and the noise is zero mean Gaussian},
keywords = {binary modulations, cooperative communications, cooperative mesh networks, decode and forward communication, decode and forward relays, Detectors, EGC detectors, Gaussian processes, Joints, Manganese, Mesh networks, multihop multibranch networks, Nakagami channels, Nakagami distribution, Nakagami distributions, relay networks (telecommunication), Signal to noise ratio, zero mean Gaussian},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Valera2014b,
title = {General Table Completion using a Bayesian Nonparametric Model},
author = {Isabel Valera and Zoubin Ghahramani},
year  = {2014},
date = {2014-01-01},
booktitle = {Neural Information Processing Systems Conference 2014 (NIPS 2014)},
address = {Montreal},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Salamanca2014bb,
title = {Near DT Bound Achieving Linear Codes in the Short Blocklength Regime},
author = {Luis Salamanca and Juan Jos\'{e} Murillo-Fuentes and Pablo M Olmos and Fernando Perez-Cruz and Sergio Verdu},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6957577},
issn = {1089-7798},
year  = {2014},
date = {2014-01-01},
journal = {IEEE Communications Letters},
volume = {PP},
number = {99},
pages = {1--1},
abstract = {The dependence-testing (DT) bound is one of the strongest achievability bounds for the binary erasure channel (BEC) in the finite block length regime. In this paper, we show that maximum likelihood decoded regular low-density paritycheck (LDPC) codes with at least 5 ones per column almost achieve the DT bound. Specifically, using quasi-regular LDPC codes with block length of 256 bits, we achieve a rate that is less than 1% away from the rate predicted by the DT bound for a word error rate below 103. The results also indicate that the maximum-likelihood solution is computationally feasible for decoding block codes over the BEC with several hundred bits.},
keywords = {binary erasure channel, Channel Coding, Complexity theory, finite blocklength regime, LDPC codes, Maximum likelihood decoding, ML decoding, parity check codes, random coding},
pubstate = {published},
tppubtype = {article}
}
@article{GilTaborda2014,
title = {Information--Estimation Relationships over Binomial and Negative Binomial Models},
author = {Camilo G Taborda and Dongning Guo and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6746122},
issn = {0018-9448},
year  = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {to appear},
pages = {1--1},
publisher = {IEEE},
abstract = {In recent years, a number of new connections between information measures and estimation have been found under various models, including, predominantly, Gaussian and Poisson models. This paper develops similar results for the binomial and negative binomial models. In particular, it is shown that the derivative of the relative entropy and the derivative of the mutual information for the binomial and negative binomial models can be expressed through the expectation of closed-form expressions that have conditional estimates as the main argument. Under mild conditions, those derivatives take the form of an expected Bregman divergence},
keywords = {ALCIT},
pubstate = {published},
tppubtype = {article}
}
@article{Yang2014bb,
title = {Quasi-Static Multiple-Antenna Fading Channels at Finite Blocklength},
author = {Wei Yang and Giuseppe Durisi and Tobias Koch and Yury Polyanskiy},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6802432 http://arxiv.org/abs/1311.2012},
issn = {0018-9448},
year  = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {60},
number = {7},
pages = {4232--4265},
publisher = {IEEE},
abstract = {This paper investigates the maximal achievable rate for a given blocklength and error probability over quasi-static multiple-input multiple-output fading channels, with and without channel state information at the transmitter and/or the receiver. The principal finding is that outage capacity, despite being an asymptotic quantity, is a sharp proxy for the finite-blocklength fundamental limits of slow-fading channels. Specifically, the channel dispersion is shown to be zero regardless of whether the fading realizations are available at both transmitter and receiver, at only one of them, or at neither of them. These results follow from analytically tractable converse and achievability bounds. Numerical evaluation of these bounds verifies that zero dispersion may indeed imply fast convergence to the outage capacity as the blocklength increases. In the example of a particular 1 $,times,$ 2 single-input multiple-output Rician fading channel, the blocklength required to achieve 90% of capacity is about an order of magnitude smaller compared with the blocklength required for an AWGN channel with the same capacity. For this specific scenario, the coding/decoding schemes adopted in the LTE-Advanced standard are benchmarked against the finite-blocklength achievability and converse bounds.},
keywords = {channel dispersion, Decoding, error probability, finite blocklength regime, MIMO, MIMO channel, outage probability, quasi-static fading channel, Rayleigh channels, Receivers, Transmitters},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{gvazquez-isit2013,
title = {The Meta-Converse Bound is Tight},
author = {Gonzalo Vazquez-Vilar and Adria Tauste Campo and Albert Guill\'{e}n i F\`{a}bregas and Alfonso Martinez},
year  = {2013},
date = {2013-07-01},
booktitle = {2013 IEEE International Symposium on Information Theory (ISIT 2013)},
address = {Istanbul, Turkey},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Alvarado2013b,
title = {High-SNR Asymptotics of Mutual Information for Discrete Constellations},
author = {Alex Alvarado and Fredrik Brannstrom and Erik Agrell and Tobias Koch},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6620631},
issn = {2157-8095},
year  = {2013},
date = {2013-01-01},
booktitle = {2013 IEEE International Symposium on Information Theory},
pages = {2274--2278},
publisher = {IEEE},
address = {Istanbul},
abstract = {The asymptotic behavior of the mutual information (MI) at high signal-to-noise ratio (SNR) for discrete constellations over the scalar additive white Gaussian noise channel is studied. Exact asymptotic expressions for the MI for arbitrary one-dimensional constellations and input distributions are presented in the limit as the SNR tends to infinity. Asymptotics of the minimum mean-square error (MMSE) are also developed. It is shown that for any input distribution, the MI and the MMSE have an asymptotic behavior proportional to a Gaussian Q-function, whose argument depends on the minimum Euclidean distance of the constellation and the SNR. Closed-form expressions for the coefficients of these Q-functions are calculated.},
keywords = {AWGN channels, discrete constellations, Entropy, Fading, Gaussian Q-function, high-SNR asymptotics, IP networks, least mean squares methods, minimum mean-square error, MMSE, Mutual information, scalar additive white Gaussian noise channel, Signal to noise ratio, signal-to-noise ratio, Upper bound},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Koblents2013,
title = {Robust Mixture Population Monte Carlo Scheme with Adaptation of the Number of Components},
author = {Eugenia Koblents and Joaquin Miguez},
url = {http://www.eusipco2013.org/},
year  = {2013},
date = {2013-01-01},
booktitle = {European Signal Processing Conference (EUSIPCO) 2013},
address = {Marrakech},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Salamanca2013,
title = {Improving the BP Estimate over the AWGN Channel Using Tree-Structured Expectation Propagation},
author = {Luis Salamanca and Juan Jose Murillo-Fuentes and Pablo M Olmos and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6620774},
issn = {2157-8095},
year  = {2013},
date = {2013-01-01},
booktitle = {2013 IEEE International Symposium on Information Theory},
pages = {2990--2994},
publisher = {IEEE},
address = {Istanbul},
abstract = {In this paper, we propose the tree-structured expectation propagation (TEP) algorithm for low-density parity-check (LDPC) decoding over the binary additive white Gaussian noise (BI-AWGN) channel. By approximating the posterior distribution by a tree-structure factorization, the TEP has been proven to improve belief propagation (BP) decoding over the binary erasure channel (BEC). We show for the AWGN channel how the TEP decoder is also able to capture additional information disregarded by the BP solution, which leads to a noticeable reduction of the error rate for finite-length codes. We show that for the range of codes of interest, the TEP gain is obtained with a slight increase in complexity over that of the BP algorithm. An efficient way of constructing the tree-like structure is also described.},
keywords = {Approximation algorithms, Approximation methods, AWGN channels, BEC, belief propagation decoding, BI-AWGN channel, binary additive white Gaussian noise channel, binary erasure channel, BP estimation, Channel Coding, Complexity theory, error rate reduction, error statistics, Expectation, finite-length codes, Iterative decoding, LDPC codes, LDPC decoding, low-density parity-check decoding, Maximum likelihood decoding, parity check codes, posterior distribution, Propagation, TEP algorithm, tree-structured expectation propagation algorithm, trees (mathematics)},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Yang2013,
title = {Block-Fading Channels at Finite Blocklength},
author = {Wei Yang and Giuseppe Durisi and Tobias Koch and Yury Polyanskiy},
url = {http://publications.lib.chalmers.se/publication/185700},
year  = {2013},
date = {2013-01-01},
booktitle = {Proceedings of the International Symposium on Wireless Communication Systems (ISWCS), Ilmenau, Germany, Aug. 2013},
address = {Ilmenau},
abstract = {This tutorial paper deals with the problem of characterizing the maximal achievable rate R* (n,$epsilon$) at a given blocklength n; and error probability $epsilon$ over block-fading channels. We review recent results that establish tight bounds on R* (n ,$epsilon$) and characterize its asymptotic behavior. Comparison between the theoretical results and the data rates achievable with the coding scheme used in LTE-Advanced are reported.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Bifet2013,
title = {Machine Learning and Knowledge Discovery in Databases},
author = {Albert Bifet and Jesse Read and Indre Zliobaite and Bernhard Pfahringer and Geoff Holmes},
editor = {Hendrik Blockeel and Kristian Kersting and Siegfried Nijssen and Filip \v{Z}elezn\'{y}},
url = {http://link.springer.com/10.1007/978-3-642-40988-2},
isbn = {978-3-642-40987-5},
year  = {2013},
date = {2013-01-01},
booktitle = {ECML 2013: 24th European Conference on Machine Learning},
volume = {8188},
publisher = {Springer Berlin Heidelberg},
series = {Lecture Notes in Computer Science},
abstract = {Data stream classification plays an important role in modern data analysis, where data arrives in a stream and needs to be mined in real time. In the data stream setting the underlying distribution from which this data comes may be changing and evolving, and so classifiers that can update themselves during operation are becoming the state-of-the-art. In this paper we show that data streams may have an important temporal component, which currently is not considered in the evaluation and benchmarking of data stream classifiers. We demonstrate how a naive classifier considering the temporal component only outperforms a lot of current state-of-the-art classifiers on real data streams that have temporal dependence, i.e. data is autocorrelated. We propose to evaluate data stream classifiers taking into account temporal dependence, and introduce a new evaluation measure, which provides a more accurate gauge of data stream classifier performance. In response to the temporal dependence issue we propose a generic wrapper for data stream classifiers, which incorporates the temporal component into the attribute space.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ruiz2013,
title = {A Bayesian Nonparametric Receiver for Joint Channel Estimation and Symbol Detection for Multiple Users},
author = {Francisco J R Ruiz and Isabel Valera and Fernando Perez-Cruz},
url = {http://ita.ucsd.edu/workshop/13/talks},
year  = {2013},
date = {2013-01-01},
booktitle = {Information Theory and Applications (ITA)},
address = {San Diego},
abstract = {Bayesian nonparametric models allow solving estimation and detection problems with an unbounded number of degrees of freedom. In multi-user environments we might not know the number of active users and the channel they face and assuming maximal scenarios (maximum number of users and dispersive channels) might degrade the receiver performance. In this presentation, we propose a Bayesian nonparametric prior that it is able to detect an unbounded number of users with an unbounded channel delay. This generative model provides the dispersive channel model for each user and a probabilistic estimate for each received symbol without a preamble.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Durisi2013,
title = {On the Multiplexing Gain of MIMO Microwave Backhaul Links Affected by Phase Noise},
author = {Giuseppe Durisi and Alberto Tarable and Tobias Koch},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6655038},
issn = {1550-3607},
year  = {2013},
date = {2013-01-01},
booktitle = {2013 IEEE International Conference on Communications (ICC)},
pages = {3209--3214},
publisher = {IEEE},
address = {Budapest},
abstract = {We consider a multiple-input multiple-output (MIMO) AWGN channel affected by phase noise. Focusing on the 2 × 2 case, we show that no MIMO multiplexing gain is to be expected when the phase-noise processes at each antenna are independent, memoryless in time, and with uniform marginal distribution over [0, 2$pi$] (strong phase noise), and when the transmit signal is isotropically distributed on the real plane. The scenario of independent phase-noise processes across antennas is relevant for microwave backhaul links operating in the 20-40 GHz range.},
keywords = {AWGN channels, marginal distribution, Microwave antennas, microwave links, MIMO, MIMO AWGN channel, MIMO communication, MIMO microwave backhaul links, MIMO multiplexing gain, multiple-input multiple-output AWGN channel, Multiplexing, Phase noise, phase-noise processes, Receivers, Signal to noise ratio, strong phase noise, transmit signal, Transmitters},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Read2013,
title = {Eficient Monte Carlo Optimization for Multi-Label Classifier Chains},
author = {Jesse Read and Luca Martino and David Luengo},
year  = {2013},
date = {2013-01-01},
booktitle = {ICASSP 2013: The 38th International Conference on Acoustics, Speech, and Signal Processing},
address = {Vancouver},
abstract = {Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest- performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for nding a good chain sequence and performing ecient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.},
keywords = {Bayesian inference, Classifier chains, Monte Carlo methods, Multi-dimensional classification, Multi-label classification},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Luengo2013,
title = {Cross-Products LASSO},
author = {David Luengo and Javier Via and Sandra Monzon and Tom Trigano and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6638840},
issn = {1520-6149},
year  = {2013},
date = {2013-01-01},
booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing},
pages = {6118--6122},
publisher = {IEEE},
address = {Vancouver},
abstract = {Negative co-occurrence is a common phenomenon in many signal processing applications. In some cases the signals involved are sparse, and this information can be exploited to recover them. In this paper, we present a sparse learning approach that explicitly takes into account negative co-occurrence. This is achieved by adding a novel penalty term to the LASSO cost function based on the cross-products between the reconstruction coefficients. Although the resulting optimization problem is non-convex, we develop a new and efficient method for solving it based on successive convex approximations. Results on synthetic data, for both complete and overcomplete dictionaries, are provided to validate the proposed approach.},
keywords = {Approximation methods, approximation theory, concave programming, convex programming, Cost function, cross-product LASSO cost function, Dictionaries, dictionary, Encoding, LASSO, learning (artificial intelligence), negative co-occurrence, negative cooccurrence phenomenon, nonconvex optimization problem, Signal processing, signal processing application, signal reconstruction, sparse coding, sparse learning approach, Sparse matrices, sparsity-aware learning, successive convex approximation, Vectors},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Olmos2013a,
title = {A Closed-Form Scaling Law for Convolutional LDPC Codes Over the BEC},
author = {Pablo M Olmos and Rudiger Urbanke},
url = {http://itw2013.tsc.uc3m.es/authors},
year  = {2013},
date = {2013-01-01},
booktitle = {2013 IEEE Information Theory Workshop},
address = {Seville},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Vazquez2013,
title = {Automated Detection of Paroxysmal Gamma Waves in Meditation EEG},
author = {Manuel A Vazquez and Jing Jin and Justin Dauwels and Francois B Vialatte},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6637839},
issn = {1520-6149},
year  = {2013},
date = {2013-01-01},
booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing},
pages = {1192--1196},
publisher = {IEEE},
address = {Vancouver},
abstract = {Meditation is a fascinating topic, yet has received limited attention in the neuroscience and signal processing community so far. A few studies have investigated electroencephalograms (EEG) recorded during meditation. Strong EEG activity has been observed in the left temporal lobe of meditators. Meditators exhibit more paroxysmal gamma waves (PGWs) in active regions of the brain. In this paper, a method is proposed to automatically detect PGWs from meditation EEG. The proposed algorithm is able to identify multiple sources in the brain that generate PGWs, and the sources associated with different types of PGWs can be distinguished. The effectiveness of the proposed method is assessed on 3 subjects possessing different degrees of expertise in practicing a yoga type meditation known as Bhramari Pranayama.},
keywords = {automated detection, Bhramari Pranayama, Blind source separation, brain active region, brain multiple source identification, Detectors, EEG activity, Electroencephalogram, Electroencephalography, left temporal lobe, medical signal detection, Meditation, meditation EEG, meditator, neurophysiology, neuroscience, Paroxysmal gamma wave, paroxysmal gamma waves, PGW, Principal component analysis, Sensitivity, signal processing community, Spike detection, Temporal lobe, yoga type meditation},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Yang2013a,
title = {Quasi-Static SIMO Fading Channels at Finite Blocklength},
author = {Wei Yang and Giuseppe Durisi and Tobias Koch and Yury Polyanskiy},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6620483},
issn = {2157-8095},
year  = {2013},
date = {2013-01-01},
booktitle = {2013 IEEE International Symposium on Information Theory},
pages = {1531--1535},
publisher = {IEEE},
address = {Istanbul},
abstract = {We investigate the maximal achievable rate for a given blocklength and error probability over quasi-static single-input multiple-output (SIMO) fading channels. Under mild conditions on the channel gains, it is shown that the channel dispersion is zero regardless of whether the fading realizations are available at the transmitter and/or the receiver. The result follows from computationally and analytically tractable converse and achievability bounds. Through numerical evaluation, we verify that, in some scenarios, zero dispersion indeed entails fast convergence to outage capacity as the blocklength increases. In the example of a particular 1×2 SIMO Rician channel, the blocklength required to achieve 90% of capacity is about an order of magnitude smaller compared to the blocklength required for an AWGN channel with the same capacity.},
keywords = {achievability bounds, AWGN channel, AWGN channels, channel capacity, channel dispersion, channel gains, Dispersion, error probability, error statistics, Fading, fading channels, fading realizations, fast convergence, finite blocklength, maximal achievable rate, numerical evaluation, outage capacity, quasistatic SIMO fading channels, Random variables, Receivers, SIMO Rician channel, single-input multiple-output, Transmitters, zero dispersion},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Bifet2013a,
title = {Efficient Data Stream Classification via Probabilistic Adaptive Windows},
author = {Albert Bifet and Bernhard Pfahringer and Jesse Read and Geoff Holmes},
url = {http://dl.acm.org/citation.cfm?id=2480362.2480516},
isbn = {9781450316569},
year  = {2013},
date = {2013-01-01},
booktitle = {Proceedings of the 28th Annual ACM Symposium on Applied Computing - SAC '13},
publisher = {ACM Press},
address = {Coimbra},
abstract = {In the context of a data stream, a classifier must be able to learn from a theoretically-infinite stream of examples using limited time and memory, while being able to predict at any point. Many methods deal with this problem by basing their model on a window of examples. We introduce a probabilistic adaptive window (PAW) for data-stream learning, which improves this windowing technique with a mechanism to include older examples as well as the most recent ones, thus maintaining information on past concept drifts while being able to adapt quickly to new ones. We exemplify PAW with lazy learning methods in two variations: one to handle concept drift explicitly, and the other to add classifier diversity using an ensemble. Along with the standard measures of accuracy and time and memory use, we compare classifiers against state-of-the-art classifiers from the data-stream literature.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Koblents2013a,
title = {A Population Monte Carlo Scheme for Computational Inference in High Dimensional Spaces},
author = {Eugenia Koblents and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6638881},
issn = {1520-6149},
year  = {2013},
date = {2013-01-01},
booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing},
pages = {6318--6322},
publisher = {IEEE},
address = {Vancouver},
abstract = {In this paper we address the Monte Carlo approximation of integrals with respect to probability distributions in high-dimensional spaces. In particular, we investigate the population Monte Carlo (PMC) scheme, which is based on an iterative importance sampling (IS) approach. Both IS and PMC suffer from the well known problem of degeneracy of the importance weights (IWs), which is closely related to the curse-of-dimensionality, and limits their applicability in large-scale practical problems. In this paper we investigate a novel PMC scheme that consists in performing nonlinear transformations of the IWs in order to smooth their variations and avoid degeneracy. We apply the modified IS scheme to the well-known mixture-PMC (MPMC) algorithm, which constructs the importance functions as mixtures of kernels. We present numerical results that show how the modified version of MPMC clearly outperforms the original scheme.},
keywords = {Approximation methods, computational inference, degeneracy of importance weights, high dimensional spaces, Importance sampling, importance weights, iterative importance sampling, iterative methods, mixture-PMC, mixture-PMC algorithm, Monte Carlo methods, MPMC, nonlinear transformations, population Monte Carlo, population Monte Carlo scheme, Probability density function, probability distributions, Proposals, Sociology, Standards},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Olmos2013b,
title = {Tree-Structure Expectation Propagation for LDPC Decoding Over the BEC},
author = {Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6451276},
issn = {0018-9448},
year  = {2013},
date = {2013-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {59},
number = {6},
pages = {3354--3377},
abstract = {We present the tree-structure expectation propagation (Tree-EP) algorithm to decode low-density parity-check (LDPC) codes over discrete memoryless channels (DMCs). Expectation propagation generalizes belief propagation (BP) in two ways. First, it can be used with any exponential family distribution over the cliques in the graph. Second, it can impose additional constraints on the marginal distributions. We use this second property to impose pairwise marginal constraints over pairs of variables connected to a check node of the LDPC code's Tanner graph. Thanks to these additional constraints, the Tree-EP marginal estimates for each variable in the graph are more accurate than those provided by BP. We also reformulate the Tree-EP algorithm for the binary erasure channel (BEC) as a peeling-type algorithm (TEP) and we show that the algorithm has the same computational complexity as BP and it decodes a higher fraction of errors. We describe the TEP decoding process by a set of differential equations that represents the expected residual graph evolution as a function of the code parameters. The solution of these equations is used to predict the TEP decoder performance in both the asymptotic regime and the finite-length regimes over the BEC. While the asymptotic threshold of the TEP decoder is the same as the BP decoder for regular and optimized codes, we propose a scaling law for finite-length LDPC codes, which accurately approximates the TEP improved performance and facilitates its optimization.},
keywords = {Algorithm design and analysis, Approximation algorithms, Approximation methods, BEC, belief propagation, Belief-propagation (BP), binary erasure channel, Complexity theory, decode low-density parity-check codes, Decoding, discrete memoryless channels, expectation propagation, finite-length analysis, LDPC codes, LDPC decoding, parity check codes, peeling-type algorithm, Probability density function, random graph evolution, Tanner graph, tree-structure expectation propagation},
pubstate = {published},
tppubtype = {article}
}
@article{Asheghan2013,
title = {Robust Global Synchronization of two Complex Dynamical Networks},
author = {Mohammad Mostafa Asheghan and Joaquin Miguez},
url = {http://www.tsc.uc3m.es/~jmiguez/papers/P38_2013_Robust Global Synchronization of two Complex Dynamical Networks.pdf 
http://www.researchgate.net/publication/245026922_Robust_global_synchronization_of_two_complex_dynamical_networks},
issn = {1089-7682},
year  = {2013},
date = {2013-01-01},
journal = {Chaos (Woodbury, N.Y.)},
volume = {23},
number = {2},
pages = {023108},
abstract = {We investigate the synchronization of two coupled complex dynamical networks, a problem that has been termed outer synchronization in the literature. Our approach relies on (a) a basic lemma on the eigendecomposition of matrices resulting from Kronecker products and (b) a suitable choice of Lyapunov function related to the synchronization error dynamics. Starting from these two ingredients, a theorem that provides a sufficient condition for outer synchronization of the networks is proved. The condition in the theorem is expressed as a linear matrix inequality. When satisfied, synchronization is guaranteed to occur globally, i.e., independently of the initial conditions of the networks. The argument of the proof includes the design of the gain of the synchronizer, which is a constant square matrix with dimension dependent on the number of dynamic variables in a single network node, but independent of the size of the overall network, which can be much larger. This basic result is subsequently elaborated to simplify the design of the synchronizer, to avoid unnecessarily restrictive assumptions (e.g., diffusivity) on the coupling matrix that defines the topology of the networks and, finally, to obtain synchronizers that are robust to model errors in the parameters of the coupled networks. An illustrative numerical example for the outer synchronization of two networks of classical Lorenz nodes with perturbed parameters is presented.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Jingshan2013,
title = {Sparse ACEKF for Phase Reconstruction.},
author = {Zhong Jingshan and Justin Dauwels and Manuel A Vazquez and Laura Waller},
url = {http://www.opticsinfobase.org/viewmedia.cfm?uri=oe-21-15-18125\&amp;amp;seq=0\&amp;amp;html=true},
issn = {1094-4087},
year  = {2013},
date = {2013-01-01},
journal = {Optics express},
volume = {21},
number = {15},
pages = {18125--37},
publisher = {Optical Society of America},
abstract = {We propose a novel low-complexity recursive filter to efficiently recover quantitative phase from a series of noisy intensity images taken through focus. We first transform the wave propagation equation and nonlinear observation model (intensity measurement) into a complex augmented state space model. From the state space model, we derive a sparse augmented complex extended Kalman filter (ACEKF) to infer the complex optical field (amplitude and phase), and find that it converges under mild conditions. Our proposed method has a computational complexity of N(z)N logN and storage requirement of O(N), compared with the original ACEKF method, which has a computational complexity of O(NzN(3)) and storage requirement of O(N(2)), where Nz is the number of images and N is the number of pixels in each image. Thus, it is efficient, robust and recursive, and may be feasible for real-time phase recovery applications with high resolution images.},
keywords = {Image reconstruction techniques, Phase retrieval},
pubstate = {published},
tppubtype = {article}
}
@article{Salamanca2013a,
title = {Tree-Structured Expectation Propagation for LDPC Decoding over BMS Channels},
author = {Luis Salamanca and Pablo M Olmos and Fernando Perez-Cruz and Juan Jose Murillo-Fuentes},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6587624},
issn = {0090-6778},
year  = {2013},
date = {2013-01-01},
journal = {IEEE Transactions on Communications},
volume = {61},
number = {10},
pages = {4086--4095},
abstract = {In this paper, we put forward the tree-structured expectation propagation (TEP) algorithm for decoding block and convolutional low-density parity-check codes over any binary channel. We have already shown that TEP improves belief propagation (BP) over the binary erasure channel (BEC) by imposing marginal constraints over a set of pairs of variables that form a tree or a forest. The TEP decoder is a message-passing algorithm that sequentially builds a tree/forest of erased variables to capture additional information disregarded by the standard BP decoder, which leads to a noticeable reduction of the error rate for finite-length codes. In this paper, we show how the TEP can be extended to any channel, specifically to binary memoryless symmetric (BMS) channels. We particularly focus on how the TEP algorithm can be adapted for any channel model and, more importantly, how to choose the tree/forest to keep the gains observed for block and convolutional LDPC codes over the BEC.},
keywords = {Approximation algorithms, Approximation methods, BEC, belief propagation, binary erasure channel, binary memoryless symmetric channels, BMS channels, Channel Coding, Complexity theory, convolutional codes, convolutional low-density parity-check codes, Decoding, decoding block, expectation propagation, finite-length codes, LDPC decoding, message-passing algorithm, parity check codes, Probability density function, sparse linear codes, TEP algorithm, tree-structured expectation propagation, trees (mathematics), Vegetation},
pubstate = {published},
tppubtype = {article}
}
@article{Valera2013,
title = {On the Maximum Likelihood Estimation of the ToA Under an Imperfect Path Loss Exponent},
author = {Isabel Valera and Bamrung Sieskul and Joaquin Miguez},
url = {http://www.tsc.uc3m.es/~jmiguez/papers/P37_2013_On the Maximum Likelihood Estimation of the ToA Under an Imperfect Path Loss Exponent.pdf 
http://jwcn.eurasipjournals.com/content/2013/1/158},
issn = {1687-1499},
year  = {2013},
date = {2013-01-01},
journal = {EURASIP Journal on Wireless Communications and Networking},
volume = {2013},
number = {1},
pages = {158},
publisher = {Springer},
abstract = {We investigate the estimation of the time of arrival (ToA) of a radio signal transmitted over a flat-fading channel. The path attenuation is assumed to depend only on the transmitter-receiver distance and the path loss exponent (PLE) which, in turn, depends on the physical environment. All previous approaches to the problem either assume that the PLE is perfectly known or rely on estimators of the ToA which do not depend on the PLE. In this paper, we introduce a novel analysis of the performance of the maximum likelihood (ML) estimator of the ToA under an imperfect knowledge of the PLE. Specifically, we carry out a Taylor series expansion that approximates the bias and the root mean square error of the ML estimator in closed form as a function of the PLE error. The analysis is first carried out for a path loss model in which the received signal gain depends only on the PLE and the transmitter-receiver distance. Then, we extend the obtained results to account also for shadow fading scenarios. Our computer simulations show that this approximate analysis is accurate when the signal-to-noise ratio (SNR) of the received signal is medium to high. A simple Monte Carlo method based on the analysis is also proposed. This technique is computationally efficient and yields a better approximation of the ML estimator in the low SNR region. The obtained analytical (and Monte Carlo) approximations can be useful at the design stage of wireless communication and localization systems.},
keywords = {Maximum likelihood estimator, Path loss exponent, Time-of-arrival estimation},
pubstate = {published},
tppubtype = {article}
}
@article{Koch2013,
title = {At Low SNR, Asymmetric Quantizers are Better},
author = {Tobias Koch and Amos Lapidoth},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6545291},
issn = {0018-9448},
year  = {2013},
date = {2013-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {59},
number = {9},
pages = {5421--5445},
abstract = {We study the capacity of the discrete-time Gaussian channel when its output is quantized with a 1-bit quantizer. We focus on the low signal-to-noise ratio (SNR) regime, where communication at very low spectral efficiencies takes place. In this regime, a symmetric threshold quantizer is known to reduce channel capacity by a factor of 2/$pi$, i.e., to cause an asymptotic power loss of approximately 2 dB. Here, it is shown that this power loss can be avoided by using asymmetric threshold quantizers and asymmetric signaling constellations. To avoid this power loss, flash-signaling input distributions are essential. Consequently, 1-bit output quantization of the Gaussian channel reduces spectral efficiency. Threshold quantizers are not only asymptotically optimal: at every fixed SNR, a threshold quantizer maximizes capacity among all 1-bit output quantizers. The picture changes on the Rayleigh-fading channel. In the noncoherent case, a 1-bit output quantizer causes an unavoidable low-SNR asymptotic power loss. In the coherent case, however, this power loss is avoidable provided that we allow the quantizer to depend on the fading level.},
keywords = {1-bit quantizer, asymmetric signaling constellation, asymmetric threshold quantizers, asymptotic power loss, Capacity per unit energy, channel capacity, discrete-time Gaussian channel, flash-signaling input distribution, Gaussian channel, Gaussian channels, low signal-to-noise ratio (SNR), quantisation (signal), quantization, Rayleigh channels, Rayleigh-fading channel, signal-to-noise ratio, SNR, spectral efficiency},
pubstate = {published},
tppubtype = {article}
}
@article{Vazquez2013a,
title = {User Activity Tracking in DS-CDMA Systems},
author = {Manuel A Vazquez and Joaquin Miguez},
url = {http://www.tsc.uc3m.es/~jmiguez/papers/P39_2013_User Activity Tracking in DS-CDMA Systems.pdf http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6473922},
issn = {0018-9545},
year  = {2013},
date = {2013-01-01},
journal = {IEEE Transactions on Vehicular Technology},
volume = {62},
number = {7},
pages = {3188--3203},
abstract = {In modern multiuser communication systems, users are allowed to enter or leave the system at any given time. Thus, the number of active users is an unknown and time-varying parameter, and the performance of the system depends on how accurately this parameter is estimated over time. The so-called problem of user identification, which consists of determining the number and identities of users transmitting in a communication system, is usually solved prior to, and hence independently of, that posed by the detection of the transmitted data. Since both problems are tightly connected, a joint solution is desirable. In this paper, we focus on direct-sequence (DS) code-division multiple-access (CDMA) systems and derive, within a Bayesian framework, different receivers that cope with an unknown and time-varying number of users while performing joint channel estimation and data detection. The main feature of these receivers, compared with other recently proposed schemes for user activity detection, is that they are natural extensions of existing maximum a posteriori (MAP) equalizers for multiple-input-multiple-output communication channels. We assess the validity of the proposed receivers, including their reliability in detecting the number and identities of active users, by way of computer simulations.},
keywords = {Activity detection, activity tracking, Bayes methods, Bayesian framework, Channel estimation, code division multiple access, code-division multiple access (CDMA), computer simulations, data detection, direct sequence code division multiple-access, DS-CDMA systems, Equations, joint channel and data estimation, joint channel estimation, Joints, MAP equalizers, Mathematical model, maximum a posteriori, MIMO communication, Multiaccess communication, multiple-input-multiple-output communication chann, multiuser communication systems, per-survivor processing (PSP), radio receivers, Receivers, sequential Monte Carlo (SMC) methods, time-varying number, time-varying parameter, Vectors, wireless channels},
pubstate = {published},
tppubtype = {article}
}
@article{Bravo-Santos2013,
title = {Polar Codes for Gaussian Degraded Relay Channels},
author = {\'{A}ngel M Bravo-Santos},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6412681},
issn = {1089-7798},
year  = {2013},
date = {2013-01-01},
journal = {IEEE Communications Letters},
volume = {17},
number = {2},
pages = {365--368},
publisher = {IEEE},
abstract = {In this paper we apply polar codes for the Gaussian degraded relay channel. We study the conditions to be satisfied by the codes and provide an efficient method for constructing them. The relay-destination link is special because the noise is the sum of two components: the Gaussian noise and the signals from the source. We study this link and provide the log-likelihood expression to be used by the polar code detector. We perform simulations of the channel and the results show that polar codes of high rate and large codeword length are closer to the theoretical limit than other good codes.},
keywords = {channel capacity, Channel Coding, Decoding, Encoding, Gaussian channels, Gaussian degraded relay channel, Gaussian noise, Gaussian-degraded relay channels, log-likelihood expression, Markov coding, Noise, parity check codes, polar code detector, polar codes, relay-destination link, Relays, Vectors},
pubstate = {published},
tppubtype = {article}
}
@article{Perez-Cruz2013,
title = {Gaussian Processes for Nonlinear Signal Processing: An Overview of Recent Advances},
author = {Fernando Perez-Cruz and Steven Van Vaerenbergh and Juan Jose Murillo-Fuentes and Miguel Lazaro-Gredilla and Ignacio Santamaria},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6530761},
issn = {1053-5888},
year  = {2013},
date = {2013-01-01},
journal = {IEEE Signal Processing Magazine},
volume = {30},
number = {4},
pages = {40--50},
abstract = {Gaussian processes (GPs) are versatile tools that have been successfully employed to solve nonlinear estimation problems in machine learning but are rarely used in signal processing. In this tutorial, we present GPs for regression as a natural nonlinear extension to optimal Wiener filtering. After establishing their basic formulation, we discuss several important aspects and extensions, including recursive and adaptive algorithms for dealing with nonstationarity, low-complexity solutions, non-Gaussian noise models, and classification scenarios. Furthermore, we provide a selection of relevant applications to wireless digital communications.},
keywords = {adaptive algorithm, Adaptive algorithms, classification scenario, Gaussian processes, Learning systems, Machine learning, Noise measurement, nonGaussian noise model, Nonlinear estimation, nonlinear estimation problem, nonlinear signal processing, optimal Wiener filtering, recursive algorithm, Signal processing, Wiener filters, wireless digital communication},
pubstate = {published},
tppubtype = {article}
}
@article{Salamanca2013b,
title = {Tree Expectation Propagation for ML Decoding of LDPC Codes over the BEC},
author = {Luis Salamanca and Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6384612},
issn = {0090-6778},
year  = {2013},
date = {2013-01-01},
journal = {IEEE Transactions on Communications},
volume = {61},
number = {2},
pages = {465--473},
abstract = {We propose a decoding algorithm for LDPC codes that achieves the maximum likelihood (ML) solution over the binary erasure channel (BEC). In this channel, the tree-structured expectation propagation (TEP) decoder improves the peeling decoder (PD) by processing check nodes of degree one and two. However, it does not achieve the ML solution, as the tree structure of the TEP allows only for approximate inference. In this paper, we provide the procedure to construct the structure needed for exact inference. This algorithm, denoted as generalized tree-structured expectation propagation (GTEP), modifies the code graph by recursively eliminating any check node and merging this information in the remaining graph. The GTEP decoder upon completion either provides the unique ML solution or a tree graph in which the number of parent nodes indicates the multiplicity of the ML solution. We also explain the algorithm as a Gaussian elimination method, relating the GTEP to other ML solutions. Compared to previous approaches, it presents an equivalent complexity, it exhibits a simpler graphical message-passing procedure and, most interesting, the algorithm can be generalized to other channels.},
keywords = {approximate inference, Approximation algorithms, Approximation methods, BEC, binary codes, binary erasure channel, code graph, Complexity theory, equivalent complexity, Gaussian elimination method, Gaussian processes, generalized tree-structured expectation propagatio, graphical message-passing procedure, graphical models, LDPC codes, Maximum likelihood decoding, maximum likelihood solution, ML decoding, parity check codes, peeling decoder, tree expectation propagation, tree graph, Tree graphs, tree-structured expectation propagation, tree-structured expectation propagation decoder, trees (mathematics)},
pubstate = {published},
tppubtype = {article}
}
@article{Bravo-Santos2013a,
title = {Polar Codes for the Rayleigh Fading Channel},
author = {\'{A}ngel M Bravo-Santos},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6663750},
issn = {1089-7798},
year  = {2013},
date = {2013-01-01},
journal = {IEEE Communications Letters},
volume = {PP},
number = {99},
pages = {1--4},
abstract = {The application of polar codes for the Rayleigh fading channel is considered. We construct polar codes for the block Rayleigh fading channel with known channel side information (CSI) and for the Rayleigh channel with known channel distribution information (CDI). The construction of polar codes for the Rayleigh fading with known CSI allows them to work with any signal noise ratio (SNR). The rate of the codeword is adapted correspondingly. Polar codes for Rayleigh fading with known CDI suffer a penalty for not having complete information about the channel. The penalty, however, is small, about 1.3 dB. We perform simulations and compare the obtained results with the theoretical limits. We show that they are close to the theoretical limit. We compare polar codes with other good codes and the results show that long polar codes are closer to the limit.},
keywords = {fading channels, polar codes, Rayleigh channels},
pubstate = {published},
tppubtype = {article}
}
@article{Leiva-Murillo2013,
title = {Characterization of Suicidal Behaviour with Self-Organizing Maps},
author = {Jose M Leiva-Murillo and Jorge L\'{o}pez-Castrom\'{a}n and Enrique Baca-Garc\'{i}a},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3705862\&amp;amp;tool=pmcentrez\&amp;amp;rendertype=abstract},
issn = {1748-6718},
year  = {2013},
date = {2013-01-01},
journal = {Computational and mathematical methods in medicine},
volume = {2013},
pages = {136743},
abstract = {The study of the variables involved in suicidal behavior is important from a social, medical, and economical point of view. Given the high number of potential variables of interest, a large population of subjects must be analysed in order to get conclusive results. In this paper, we describe a method based on self-organizing maps (SOMs) for finding the most relevant variables even when their relation to suicidal behavior is strongly nonlinear. We have applied the method to a cohort with more than 8,000 subjects and 600 variables and discovered four groups of variables involved in suicidal behavior. According to the results, there are four main groups of risk factors that characterize the population of suicide attempters: mental disorders, alcoholism, impulsivity, and childhood abuse. The identification of specific subpopulations of suicide attempters is consistent with current medical knowledge and may provide a new avenue of research to improve the management of suicidal cases.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Martino2013,
title = {On the Flexibility of the Design of Multiple Try Metropolis Schemes},
author = {Luca Martino and Jesse Read},
url = {http://link.springer.com/10.1007/s00180-013-0429-2},
issn = {0943-4062},
year  = {2013},
date = {2013-01-01},
journal = {Computational Statistics},
volume = {28},
number = {6},
pages = {2797--2823},
abstract = {The multiple try Metropolis (MTM) method is a generalization of the classical Metropolis\textendashHastings algorithm in which the next state of the chain is chosen among a set of samples, according to normalized weights. In the literature, several extensions have been proposed. In this work, we show and remark upon the flexibility of the design of MTM-type methods, fulfilling the detailed balance condition. We discuss several possibilities, show different numerical simulations and discuss the implications of the results},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Manzano2013,
title = {Dynamic Cognitive Self-Organized TDMA for Medium Access Control in Real-Time Vehicle to Vehicle Communications},
author = {Mario Manzano and Felipe Espinosa and \'{A}ngel M Bravo-Santos and Enrique Santiso and Ignacio Bravo and David Garc\'{i}a},
url = {http://www.hindawi.com/journals/mpe/2013/574528/abs/},
year  = {2013},
date = {2013-01-01},
journal = {Mathematical Problems in Engineering},
volume = {2013},
abstract = {The emergence of intelligent transport systems has brought out a new set of requirements on wireless communication. To cope with these requirements, several proposals are currently under discussion. In this highly mobile environment, the design of a prompt, efficient, flexible, and reliable medium access control, able to cover the specific constraints of the named real-time communications applications, is still unsolved. This paper presents the original proposal integrating Non-Cooperative Cognitive Time Division Multiple Access (NCC-TDMA) based on Cognitive Radio (CR) techniques to obtain a mechanism which complies with the requirements of real-time communications. Though the proposed MAC uses a slotted channel, it can be adapted to operate on the physical layer of different standards. The authors’ analysis considers the IEEE WAVE and 802.11p as the standards of reference. The mechanism also offers other advantages, such as avoiding signalling and the adaptation capacity to channel conditions and interferences. The solution is applied to the problem of units merging a convoy. Comparison results between NCC-TDMA and Slotted-Aloha are included.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Koch2013a,
title = {On Noncoherent Fading Relay Channels at High Signal-to-Noise Ratio},
author = {Tobias Koch and Gerhard Kramer},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6378474},
issn = {0018-9448},
year  = {2013},
date = {2013-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {59},
number = {4},
pages = {2221--2241},
abstract = {The capacity of noncoherent regular-fading relay channels is studied where all terminals are aware of the fading statistics but not of their realizations. It is shown that if the fading coefficient of the channel between the transmitter and the receiver can be predicted more accurately from its infinite past than the fading coefficient of the channel between the relay and the receiver, then at high signal-to-noise ratio (SNR), the relay does not increase capacity. It is further shown that if the fading coefficient of the channel between the transmitter and the relay can be predicted more accurately from its infinite past than the fading coefficient of the channel between the relay and the receiver, then at high SNR, one can achieve communication rates that are within one bit of the capacity of the multiple-input single-output fading channel that results when the transmitter and the relay can cooperate.},
keywords = {channel capacity, Channel models, Fading, fading channels, MIMO communication, multiple-input single-output fading channel statis, noncoherent, noncoherent fading relay channel capacity, radio receiver, radio receivers, radio transmitter, radio transmitters, Receivers, relay channels, relay networks (telecommunication), Relays, Signal to noise ratio, signal-to-noise ratio, SNR, statistics, time selective, Transmitters, Upper bound},
pubstate = {published},
tppubtype = {article}
}
@article{Leiva-Murillo2013a,
title = {Multitask Remote Sensing Data Classification},
author = {Jose M Leiva-Murillo and Luis Gomez-Chova and Gustavo Camps-Valls},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6214595},
issn = {0196-2892},
year  = {2013},
date = {2013-01-01},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
volume = {51},
number = {1},
pages = {151--161},
publisher = {IEEE},
keywords = {Aggregates, angular image features, Cloud screening, covariate shift, covariate shift (CS), cross information, data processing problems, data set bias, domain adaptation, geophysical image processing, Hilbert space pairwise predictor Euclidean distanc, image classification, image feature nonstationary behavior, Kernel, land mine detection, land-mine detection, learning (artificial intelligence), Machine learning, matrix decomposition, matrix regularization, MTL, multisource image classification, multispectral images, multitask learning, multitask learning (MTL), multitask remote sensing data classification, multitemporal classification, multitemporal image classification, radar data, regularization schemes, relational operators, Remote sensing, small sample set problem, spatial image features, Standards, support vector machine, support vector machine (SVM), Support vector machines, SVM, temporal image features, Training, urban monitoring},
pubstate = {published},
tppubtype = {article}
}
@article{Read2013b,
title = {Multi-Dimensional Classification with Super-Classes},
author = {Jesse Read and Concha Bielza and Pedro Larranaga},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6648319},
issn = {1041-4347},
year  = {2013},
date = {2013-01-01},
journal = {IEEE Transactions on Knowledge and Data Engineering},
volume = {PP},
number = {99},
pages = {1--1},
abstract = {The multi-dimensional classification problem is a generalisation of the recently-popularised task of multi-label classification, where each data instance is associated with multiple class variables. There has been relatively little research carried out specific to multi-dimensional classification and, although one of the core goals is similar (modelling dependencies among classes), there are important differences; namely a higher number of possible classifications. In this paper we present method for multi-dimensional classification, drawing from the most relevant multi-label research, and combining it with important novel developments. Using a fast method to model the conditional dependence between class variables, we form super-class partitions and use them to build multi-dimensional learners, learning each super-class as an ordinary class, and thus explicitly modelling class dependencies. Additionally, we present a mechanism to deal with the many class values inherent to super-classes, and thus make learning efficient. To investigate the effectiveness of this approach we carry out an empirical evaluation on a range of multi-dimensional datasets, under different evaluation metrics, and in comparison with high-performing existing multi-dimensional approaches from the literature. Analysis of results shows that our approach offers important performance gains over competing methods, while also exhibiting tractable running time.},
keywords = {COMPREHENSION},
pubstate = {published},
tppubtype = {article}
}
@article{Asheghan2013a,
title = {Non-Fragile Control and Synchronization of a New Fractional Order Chaotic System},
author = {Mohammad Mostafa Asheghan and Saleh S Delshad and Mohammad Taghi Hamidi-Beheshti and Mohammad Saleh Tavazoei},
url = {http://www.sciencedirect.com/science/article/pii/S0096300313007959},
issn = {00963003},
year  = {2013},
date = {2013-01-01},
journal = {Applied Mathematics and Computation},
volume = {222},
pages = {712--721},
abstract = {In this paper, we address global non-fragile control and synchronization of a new fractional order chaotic system. First we inspect the chaotic behavior of the fractional order system under study and also find the lowest order (2.49) for the introduced dynamics to remain chaotic. Then, a necessary and sufficient condition which can be easily extended to other fractional-order systems is proposed in terms of Linear Matrix Inequality (LMI) to check whether the candidate state feedback controller with parameter uncertainty can guarantee zero convergence of error or not. In addition, the proposed method provides a global zero attraction of error that guarantees stability around all existing equilibrium points. Finally, numerical simulation are employed to verify the validity of the proposed algorithm.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Alvarado2013a,
title = {On the Asymptotic Optimality of Gray Codes for BICM and One-Dimensional Constellations},
author = {Alex Alvarado and Fredrik Br\"{a}nnstr\"{o}m and Erik Agrell and Tobias Koch},
year  = {2013},
date = {2013-01-01},
booktitle = {IEEE Communication Theory Workshop},
address = {Phuket},
keywords = {ALCIT},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Bifet2013b,
title = {Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them},
author = {Albert Bifet and Jesse Read and Indre Zliobaite and Bernhard Pfahringer and Geoff Holmes},
year  = {2013},
date = {2013-01-01},
booktitle = {ECML 2013: 24th European Conference on Machine Learning},
keywords = {COMPREHENSION},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Gopalan2013,
title = {Bayesian Nonparametric Poisson Factorization for Recommendation Systems},
author = {Prem Gopalan and Francisco J R Ruiz and Rajesh Ranganath and David M Blei},
year  = {2013},
date = {2013-01-01},
booktitle = {Workshop on Probabilistic Models for Big Data at Neural Information Processing Systems Conference 2013 (NIPS 2013)},
address = {Lake Tahoe},
keywords = {ALCIT},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Alvarez2013,
title = {Linear Latent Force Models Using Gaussian Processes},
author = {Mauricio Alvarez and David Luengo and Neil D Lawrence},
url = {http://dblp.uni-trier.de/db/journals/pami/pami35.html#AlvarezLL13 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6514873},
year  = {2013},
date = {2013-01-01},
journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
volume = {35},
number = {11},
pages = {2693--2705},
abstract = {Purely data-driven approaches for machine learning present difficulties when data are scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data-driven modeling with a physical model of the system. We show how different, physically inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from motion capture, computational biology, and geostatistics.},
keywords = {Analytical models, Computational modeling, Data models, Differential equations, Force, Gaussian processes, Mathematical mode},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Olmos2013c,
title = {A Finite Length Performance Analysis of LDPC Codes Constructed by Connecting Spatially Coupled Chains},
author = {Pablo M Olmos and David G M Mitchell and Dimitri Truhachev and Daniel J Costello},
url = {http://itw2013.tsc.uc3m.es/authors},
year  = {2013},
date = {2013-01-01},
booktitle = {2013 IEEE Information Theory Workshop},
address = {Seville},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Serrano-Drozdowskyj2013,
title = {1533 \textendash A Naturalistic Study of the Diagnostic Evolution of Schizophrenia},
author = {E Serrano-Drozdowskyj and Jorge L\'{o}pez-Castrom\'{a}n and Jose M Leiva-Murillo and Hilario Blasco-Fontecilla and R Garcia-Nieto and Antonio Art\'{e}s-Rodr\'{i}guez and C Morant-Ginestar and Carlos Blanco and Philippe Courtet and Enrique Baca-Garc\'{i}a},
url = {http://www.sciencedirect.com/science/article/pii/S0924933813765465},
year  = {2013},
date = {2013-01-01},
journal = {European Psychiatry},
volume = {28},
abstract = {INTRODUCTION In the absence of biological measures, diagnostic long-term stability provides the best evidence of diagnostic validity.Therefore,the study of diagnostic stability in naturalistic conditions may reflect clinical validity and utility of current schizophrenia diagnostic criteria. OBJECTIVES Describe the diagnostic evolution of schizophrenia in clinical settings. METHODS We examined the stability of schizophrenia first diagnoses (n=26,163) in public mental health centers of Madrid (Spain).Probability of maintaining the diagnosis of schizophrenia was calculated considering the cumulative percentage of each diagnosis per month during 48 months after the initial diagnosis of schizophrenia. RESULTS 65% of the subjects kept the diagnosis of schizophrenia in subsequent assessments (Figure 1). Patients who changed (35%) did so in the first 4-8 months. After that time gap the rates of each diagnostic category remained stable. Diagnostic shift from schizophrenia was more commonly toward the following diagnoses: personality disorders (F60), delusional disorders (F22), bipolar disorder (F31), persistent mood disorders (F34), acute and transient psychotic disorders (F23) or schizoaffective disorder (F25). CONCLUSIONS Once it is confirmed, clinical assessment repeatedly maintains the diagnosis of schizophrenia.The time lapse for its confirmation agrees with the current diagnostic criteria in DSM-IV. We will discuss the implications of these findings for the categorical versus dimensional debate in the diagnosis of schizophrenia.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Ruiz2013b,
title = {Infinite Continuous Feature Model for Psychiatric Comorbidity Analysis},
author = {Francisco J R Ruiz and Isabel Valera and Pablo M Olmos and Carlos Blanco and Fernando Perez-Cruz},
url = {https://googledrive.com/host/0B0TBaU3UgQ0Da3A2S2VWNTRzc1E/3.pdf},
year  = {2013},
date = {2013-01-01},
booktitle = {Workshop in Machine Learning for Clinical Data Analysis and Healthcare at Neural Information Processing Systems Conference 2013 (NIPS2013).},
address = {Lake Tahoe},
abstract = {Comorbidity analysis becomes particularly relevant in the field of psychiatry, where clinical ex- perience and several studies suggest that the relation among the psychiatric disorders may have etiological and treatment implications. Several studies have focused on the search of the underlying interrelationships among psychiatric disorders, which can be useful to analyze the structure of the diagnostic classification system, and guide treatment approaches for each disorder [1]. Motivated by this relevance, in this paper we aim at finding the latent structure behind a database of psychiatric disorders. In particular, making use of the database extracted from the analysis of the National Epi- demiologic Survey on Alcohol and Related Conditions 1 (NESARC) in [1], we focus on the analysis of 20 common psychiatric disorders, including substance abuse, mood and personality disorders. Our goal is to find comorbidity patterns in the database, allowing us to seek hidden causes and to provide a tool for detecting those subjects with a high risk of suffering from these disorders.},
keywords = {ALCIT},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ruiz2013a,
title = {Bayesian Nonparametric Comorbidity Analysis of Psychiatric Disorders},
author = {Francisco J R Ruiz and Isabel Valera and Carlos Blanco and Fernando Perez-Cruz},
year  = {2013},
date = {2013-01-01},
booktitle = {9th Conference on Bayesian Nonparametrics},
address = {Amsterdam},
keywords = {ALCIT},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{6545291,
title = {At Low SNR, Asymmetric Quantizers are Better},
author = {Tobias Koch and Amos Lapidoth},
doi = {10.1109/TIT.2013.2262919},
year  = {2013},
date = {2013-01-01},
urldate = {2013-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {59},
number = {9},
pages = {5421-5445},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{allerton2012,
title = {Converse Bounds for Finite-Length Joint Source-Channel Coding},
author = {Adria Tauste Campo and Gonzalo Vazquez-Vilar and Albert Guill\'{e}n i F\`{a}bregas and Tobias Koch and Alfonso Martinez},
year  = {2012},
date = {2012-10-01},
booktitle = {50th Annual Allerton Conference on Communication, Control and Computing (Allerton 2012)},
address = {Allerton, IL, USA},
note = {Invited},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{gvazquez-TSP12,
title = {Multiantenna GLR detection of rank-one signals with known power spectrum in white noise with unknown spatial correlation},
author = {Josep Sala and Gonzalo Vazquez-Vilar and Roberto L\'{o}pez-Valcarce},
doi = {10.1109/TSP.2012.2189767},
issn = {1053-587X},
year  = {2012},
date = {2012-06-01},
journal = {IEEE Transactions on Signal Processing},
volume = {60},
number = {6},
pages = {3065-3078},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{iccasp2012,
title = {Design of universal multicoset sampling patterns for compressed sensing of multiband sparse signals},
author = {Mar\'{i}a Elena Dom\'{i}nguez-Jim\'{e}nez and Nuria Gonz\'{a}lez-Prelcic and Gonzalo Vazquez-Vilar and Roberto L\'{o}pez-Valcarce},
year  = {2012},
date = {2012-03-01},
booktitle = {2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)},
address = {Kyoto, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Salamanca2012,
title = {Tree-Structured Expectation Propagation for LDPC Decoding over the AWGN Channel},
author = {Luis Salamanca and Juan Jose Murillo-Fuentes and Pablo M Olmos and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6349716},
issn = {1551-2541},
year  = {2012},
date = {2012-01-01},
booktitle = {2012 IEEE International Workshop on Machine Learning for Signal Processing},
pages = {1--6},
publisher = {IEEE},
address = {Santander},
abstract = {In this paper, we propose the tree-structured expectation propagation (TEP) algorithm for low-density parity-check (LDPC) decoding over the additive white Gaussian noise (AWGN) channel. By imposing a tree-like approximation over the graphical model of the code, this algorithm introduces pairwise marginal constraints over pairs of variables, which provide joint information of the variables related. Thanks to this, the proposed TEP decoder improves the performance of the standard belief propagation (BP) solution. An efficient way of constructing the tree-like structure is also described. The simulation results illustrate the TEP decoder gain in the finite-length regime, compared to the standard BP solution. For code lengths shorter than n = 512, the gain in the waterfall region achieves up to 0.25 dB. We also notice a remarkable reduction of the error floor.},
keywords = {additive white Gaussian noise channel, Approximation algorithms, Approximation methods, approximation theory, AWGN channel, AWGN channels, belief propagation solution, Bit error rate, Decoding, error floor reduction, finite-length regime, Gain, Joints, LDPC decoding, low-density parity-check decoding, pairwise marginal constraint, parity check codes, TEP decoder, tree-like approximation, tree-structured expectation propagation, trees (mathematics)},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Zhong2012,
title = {Low-Complexity Noise-Resilient Recovery of Phase and Amplitude from Defocused Intensity Images},
author = {Jingshan Zhong and Justin Dauwels and Manuel A Vazquez and Laura Waller},
url = {http://www.opticsinfobase.org/abstract.cfm?URI=COSI-2012-CTu4B.1},
isbn = {1-55752-947-7},
year  = {2012},
date = {2012-01-01},
booktitle = {Imaging and Applied Optics Technical Papers},
pages = {CTu4B.1},
publisher = {OSA},
address = {Washington, D.C.},
abstract = {A low-complexity augmented Kalman filter is proposed to efficiently recover the phase from a series of noisy intensity images. The proposed method is robust to noise, has low complexity, and may enable real-time phase recovery.},
keywords = {Image reconstruction techniques, Phase retrieval, Wave propagation},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Koch2012,
title = {The Capacity Loss of Dense Constellations},
author = {Tobias Koch and Alfonso Martinez and Albert Guillen i Fabregas},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6283482},
issn = {2157-8095},
year  = {2012},
date = {2012-01-01},
booktitle = {2012 IEEE International Symposium on Information Theory Proceedings},
pages = {572--576},
publisher = {IEEE},
address = {Cambridge, MA},
abstract = {We determine the loss in capacity incurred by using signal constellations with a bounded support over general complex-valued additive-noise channels for suitably high signal-to-noise ratio. Our expression for the capacity loss recovers the power loss of 1.53 dB for square signal constellations.},
keywords = {capacity loss, channel capacity, Constellation diagram, dense constellations, Entropy, general complex-valued additive-noise channels, high signal-to-noise ratio, loss 1.53 dB, power loss, Quadrature amplitude modulation, Random variables, signal constellations, Signal processing, Signal to noise ratio, square signal constellations, Upper bound},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{O'Mahony2012,
title = {A novel Sequential Bayesian Approach to GPS Acquisition},
author = {Niamh O'Mahony and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6232921},
isbn = {978-1-4673-1878-5},
year  = {2012},
date = {2012-01-01},
booktitle = {2012 3rd International Workshop on Cognitive Information Processing (CIP)},
pages = {1--6},
publisher = {IEEE},
address = {Baiona},
abstract = {In this work, a novel online learning algorithm is presented for the synchronization of Global Positioning System (GPS) signal parameters at the acquisition, or coarse synchronization, stage. The algorithm is based on a Bayesian approach, which has, to date, not been exploited for the acquisition problem. Simulated results are presented to illustrate the algorithm performance, in terms of accuracy and acquisition time, along with results from the acquisition of signals from live GPS satellites using both the new algorithm and a state-of-the-art approach for comparison.},
keywords = {Bayes methods, coarse synchronization, Correlators, data acquisition, Delay, Doppler effect, Global Positioning System, GPS acquisition, GPS signal parameters, learning (artificial intelligence), online learning algorithm, Receivers, Satellites, sequential Bayesian approach, signal acquisition, signal detection, Synchronization},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Perez-Cruz2012,
title = {New Tools to Generate Predictive Models for Attempts Suicide},
author = {Fernando Perez-Cruz},
year  = {2012},
date = {2012-01-01},
booktitle = {National Conference on Psychiatry},
address = {Bilbao},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Olmos2012,
title = {Finite-Length Performance of Spatially-Coupled LDPC Codes under TEP Decoding},
author = {Pablo M Olmos and Fernando Perez-Cruz and Luis Salamanca and Juan Jose Murillo-Fuentes},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6404722},
isbn = {978-1-4673-0223-4},
year  = {2012},
date = {2012-01-01},
booktitle = {2012 IEEE Information Theory Workshop},
pages = {1--6},
publisher = {IEEE},
address = {Lausanne},
keywords = {asymptotic limit, belief propagation decoding, Complexity theory, convolutional codes, convolutional LDPC codes, Decoding, decoding latency, decoding threshold, erasure channel, Error analysis, error rates, finite-length analysis, finite-length performance, maximum a posteriori threshold, maximum likelihood estimation, parity check codes, regular sparse codes, spatially-coupled LDPC codes, TEP decoding, tree-structured expectation propagation, underlying regular code, very large code length, window-sliding scheme},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Read2012,
title = {Advances in Intelligent Data Analysis XI},
author = {Jesse Read and Albert Bifet and Bernhard Pfahringer and Geoff Holmes},
editor = {Jaakko Hollm\'{e}n and Frank Klawonn and Allan Tucker},
url = {http://www.springerlink.com/index/10.1007/978-3-642-34156-4},
isbn = {978-3-642-34155-7},
year  = {2012},
date = {2012-01-01},
booktitle = {Proc. of The Eleventh International Symposium on Intelligent Data Analysis (IDA 2012)},
publisher = {Springer Berlin Heidelberg},
address = {Helsinki},
series = {Lecture Notes in Computer Science},
abstract = {Many real world problems involve the challenging context of data streams, where classifiers must be incremental: able to learn from a theoretically-infinite stream of examples using limited time and memory, while being able to predict at any point. Two approaches dominate the literature: batch-incremental methods that gather examples in batches to train models; and instance-incremental methods that learn from each example as it arrives. Typically, papers in the literature choose one of these approaches, but provide insufficient evidence or references to justify their choice. We provide a first in-depth analysis comparing both approaches, including how they adapt to concept drift, and an extensive empirical study to compare several different versions of each approach. Our results reveal the respective advantages and disadvantages of the methods, which we discuss in detail.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Florentino-Liano2012c,
title = {Hierarchical Dynamic Model for Human Daily Activity Recognition},
author = {Blanca Florentino-Lia\~{n}o and Niamh O'Mahony and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.biosignals.biostec.org/Abstracts/2012/BIOSIGNALS_2012_Abstracts.htm},
year  = {2012},
date = {2012-01-01},
booktitle = {BIOSIGNALS 2012 (BIOSTEC)},
volume = {85},
address = {Vilamoura},
abstract = {This work deals with the task of human daily activity recognition using miniature inertial sensors. The proposed method is based on the development of a hierarchical dynamic model, incorporating both inter-activity and intra-activity dynamics, thereby exploiting the inherently dynamic nature of the problem to aid the classification task. The method uses raw acceleration and angular velocity signals, directly recorded by inertial sensors, bypassing commonly used feature extraction and selection techniques and, thus, keeping all information regarding the dynamics of the signals. Classification results show a competitive performance compared to state-of-the-art methods.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Taborda2012,
title = {Derivative of the Relative Entropy over the Poisson and Binomial Channel},
author = {Camilo G Taborda and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6404699},
isbn = {978-1-4673-0223-4},
year  = {2012},
date = {2012-01-01},
booktitle = {2012 IEEE Information Theory Workshop},
pages = {386--390},
publisher = {IEEE},
address = {Lausanne},
abstract = {In this paper it is found that, regardless of the statistics of the input, the derivative of the relative entropy over the Binomial channel can be seen as the expectation of a function that has as argument the mean of the conditional distribution that models the channel. Based on this relationship we formulate a similar expression for the mutual information concept. In addition to this, using the connection between the Binomial and Poisson distribution we develop similar results for the Poisson channel. Novelty of the results presented here lies on the fact that, expressions obtained can be applied to a wide range of scenarios.},
keywords = {binomial channel, binomial distribution, Channel estimation, conditional distribution, Entropy, Estimation, function expectation, Mutual information, mutual information concept, Poisson channel, Poisson distribution, Random variables, relative entropy derivative, similar expression},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Florentino-Liano2012b,
title = {Long Term Human Activity Recognition with Automatic Orientation Estimation},
author = {Blanca Florentino-Lia\~{n}o and Niamh O'Mahony and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6349789},
issn = {1551-2541},
year  = {2012},
date = {2012-01-01},
booktitle = {2012 IEEE International Workshop on Machine Learning for Signal Processing},
pages = {1--6},
publisher = {IEEE},
address = {Santander},
abstract = {This work deals with the elimination of sensitivity to sensor orientation in the task of human daily activity recognition using a single miniature inertial sensor. The proposed method detects time intervals of walking, automatically estimating the orientation in these intervals and transforming the observed signals to a “virtual” sensor orientation. Classification results show that excellent performance, in terms of both precision and recall (up to 100%), is achieved, for long-term recordings in real-life settings.},
keywords = {Acceleration, Activity recognition, automatic orientation estimation, biomedical equipment, Estimation, Gravity, Hidden Markov models, human daily activity recognition, Humans, Legged locomotion, long term human activity recognition, medical signal processing, object recognition, orientation estimation, sensors, single miniature inertial sensor, time intervals, Vectors, virtual sensor orientation, wearable sensors},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Durisi2012,
title = {Diversity Versus Channel Knowledge at Finite Block-Length},
author = {Giuseppe Durisi and Tobias Koch and Yury Polyanskiy},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6404740},
isbn = {978-1-4673-0223-4},
year  = {2012},
date = {2012-01-01},
booktitle = {2012 IEEE Information Theory Workshop},
pages = {572--576},
publisher = {IEEE},
address = {Lausanne},
abstract = {We study the maximal achievable rate R*(n, ∈) for a given block-length n and block error probability o over Rayleigh block-fading channels in the noncoherent setting and in the finite block-length regime. Our results show that for a given block-length and error probability, R*(n, ∈) is not monotonic in the channel's coherence time, but there exists a rate maximizing coherence time that optimally trades between diversity and cost of estimating the channel.},
keywords = {Approximation methods, block error probability, channel coherence time, Channel estimation, channel knowledge, Coherence, diversity, diversity reception, error statistics, Fading, finite block-length, maximal achievable rate, noncoherent setting, Rayleigh block-fading channels, Rayleigh channels, Receivers, Signal to noise ratio, Upper bound},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Florentino-Liano2012a,
title = {Human Activity Recognition Using Inertial Sensors with Invariance to Sensor Orientation},
author = {Blanca Florentino-Lia\~{n}o and Niamh O'Mahony and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6232914},
isbn = {978-1-4673-1878-5},
year  = {2012},
date = {2012-01-01},
booktitle = {2012 3rd International Workshop on Cognitive Information Processing (CIP)},
pages = {1--6},
publisher = {IEEE},
address = {Baiona},
abstract = {This work deals with the task of human daily activity recognition using miniature inertial sensors. The proposed method reduces sensitivity to the position and orientation of the sensor on the body, which is inherent in traditional methods, by transforming the observed signals to a “virtual” sensor orientation. By means of this computationally low-cost transform, the inputs to the classification algorithm are made invariant to sensor orientation, despite the signals being recorded from arbitrary sensor placements. Classification results show that improved performance, in terms of both precision and recall, is achieved with the transformed signals, relative to classification using raw sensor signals, and the algorithm performs competitively compared to the state-of-the-art. Activity recognition using data from a sensor with completely unknown orientation is shown to perform very well over a long term recording in a real-life setting.},
keywords = {Acceleration, Accelerometers, biomechanics, classification algorithm, Gyroscopes, Hidden Markov models, human daily activity recognition, inertial measurement unit, Legged locomotion, miniature inertial sensors, raw sensor signal classification, sensor orientation invariance, sensor orientation sensitivity, sensor placement, sensor position sensitivity, sensors, signal classification, signal transformation, Training, triaxial accelerometer, triaxial gyroscope, virtual sensor orientation},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Garcia-Moreno2012,
title = {A Hold-out Method to Correct PCA Variance Inflation},
author = {Pablo Garcia-Moreno and Antonio Art\'{e}s-Rodr\'{i}guez and Lars Kai Hansen},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6232926},
isbn = {978-1-4673-1878-5},
year  = {2012},
date = {2012-01-01},
booktitle = {2012 3rd International Workshop on Cognitive Information Processing (CIP)},
pages = {1--6},
publisher = {IEEE},
address = {Baiona},
abstract = {In this paper we analyze the problem of variance inflation experienced by the PCA algorithm when working in an ill-posed scenario where the dimensionality of the training set is larger than its sample size. In an earlier article a correction method based on a Leave-One-Out (LOO) procedure was introduced. We propose a Hold-out procedure whose computational cost is lower and, unlike the LOO method, the number of SVD's does not scale with the sample size. We analyze its properties from a theoretical and empirical point of view. Finally we apply it to a real classification scenario.},
keywords = {Approximation methods, classification scenario, computational complexity, computational cost, Computational efficiency, correction method, hold-out method, hold-out procedure, leave-one-out procedure, LOO method, LOO procedure, Mathematical model, PCA algorithm, PCA variance inflation, Principal component analysis, singular value decomposition, Standards, SVD, Training},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Montoya-Martinez2012,
title = {Structured Sparsity Regularization Approach to the EEG Inverse Problem},
author = {Jair Montoya-Martinez and Antonio Art\'{e}s-Rodr\'{i}guez and Lars Kai Hansen and Massimiliano Pontil},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6232898},
isbn = {978-1-4673-1878-5},
year  = {2012},
date = {2012-01-01},
booktitle = {2012 3rd International Workshop on Cognitive Information Processing (CIP)},
pages = {1--6},
publisher = {IEEE},
address = {Baiona},
abstract = {Localization of brain activity involves solving the EEG inverse problem, which is an undetermined ill-posed problem. We propose a novel approach consisting in estimating, using structured sparsity regularization techniques, the Brain Electrical Sources (BES) matrix directly in the spatio-temporal source space. We use proximal splitting optimization methods, which are efficient optimization techniques, with good convergence rates and with the ability to handle large nonsmooth convex problems, which is the typical scenario in the EEG inverse problem. We have evaluated our approach under a simulated scenario, consisting in estimating a synthetic BES matrix with 5124 sources. We report results using ℓ1 (LASSO), ℓ1/ℓ2 (Group LASSO) and ℓ1 + ℓ1/ℓ2 (Sparse Group LASSO) regularizers.},
keywords = {BES, brain electrical sources matrix, Brain modeling, EEG inverse problem, Electrodes, Electroencephalography, good convergence, Inverse problems, large nonsmooth convex problems, medical signal processing, optimisation, Optimization, proximal splitting optimization methods, Sparse matrices, spatio-temporal source space, structured sparsity regularization approach, undetermined ill-posed problem},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Monzon2012,
title = {Sparse Spectral Analysis of Atrial Fibrillation Electrograms.},
author = {Sandra Monzon and Tom Trigano and David Luengo and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6349721},
issn = {1551-2541},
year  = {2012},
date = {2012-01-01},
booktitle = {2012 IEEE International Workshop on Machine Learning for Signal Processing},
pages = {1--6},
publisher = {IEEE},
address = {Santander},
abstract = {Atrial fibrillation (AF) is a common heart disorder. One of the most prominent hypothesis about its initiation and maintenance considers multiple uncoordinated activation foci inside the atrium. However, the implicit assumption behind all the signal processing techniques used for AF, such as dominant frequency and organization analysis, is the existence of a single regular component in the observed signals. In this paper we take into account the existence of multiple foci, performing a spectral analysis to detect their number and frequencies. In order to obtain a cleaner signal on which the spectral analysis can be performed, we introduce sparsity-aware learning techniques to infer the spike trains corresponding to the activations. The good performance of the proposed algorithm is demonstrated both on synthetic and real data.},
keywords = {Algorithm design and analysis, atrial fibrillation, atrial fibrillation electrogram, biomedical signal processing, dominant frequency, Doped fiber amplifiers, electrocardiography, Harmonic analysis, Heart, heart disorder, Indexes, Mathematical model, medical signal processing, multiple foci, multiple uncoordinated activation foci, signal processing technique, sparse spectral analysis, sparsity-aware learning, sparsity-aware learning technique, spectral analysis, spike train},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Koblents2012,
title = {Importance Sampling with Transformed Weights},
author = {Eugenia Koblents and Joaquin Miguez},
url = {http://www.oxford-man.ox.ac.uk/sites/default/files/events/Mon_24_JoaquinMiguez_06FINAL.pdf},
year  = {2012},
date = {2012-01-01},
booktitle = {Data Assimilation Workshop, Oxford\textendashMan Institute},
address = {Oxford},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Olmos2012a,
title = {Finite-Length Analysis of the TEP Decoder for LDPC Ensembles over the BEC},
author = {Pablo M Olmos and Fernando Perez-Cruz and Luis Salamanca and Juan Jose Murillo-Fuentes},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6283932},
issn = {2157-8095},
year  = {2012},
date = {2012-01-01},
booktitle = {2012 IEEE International Symposium on Information Theory Proceedings},
pages = {2346--2350},
publisher = {IEEE},
address = {Cambridge, MA},
abstract = {In this work, we analyze the finite-length performance of low-density parity check (LDPC) ensembles decoded over the binary erasure channel (BEC) using the tree-expectation propagation (TEP) algorithm. In a previous paper, we showed that the TEP improves the BP performance for decoding regular and irregular short LDPC codes, but the perspective was mainly empirical. In this work, given the degree-distribution of an LDPC ensemble, we explain and predict the range of code lengths for which the TEP improves the BP solution. In addition, for LDPC ensembles that present a single critical point, we propose a scaling law to accurately predict the performance in the waterfall region. These results are of critical importance to design practical LDPC codes for the TEP decoder.},
keywords = {Approximation methods, BEC, binary codes, binary erasure channel, Decoding, Error analysis, error probability, finite-length analysis, LDPC ensembles, low-density parity check ensembles, parity check codes, TEP decoder, Trajectory, tree-expectation propagation algorithm, waterfall region},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Pastore2012,
title = {Improved Capacity Lower Bounds for Fading Channels with Imperfect CSI Using Rate Splitting},
author = {Adriano Pastore and Tobias Koch and Javier Rodriguez Fonollosa},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6377031},
isbn = {978-1-4673-4681-8},
year  = {2012},
date = {2012-01-01},
booktitle = {2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel},
pages = {1--5},
publisher = {IEEE},
address = {Eilat},
abstract = {As shown by Medard (“The effect upon channel capacity in wireless communications of perfect and imperfect knowledge of the channel,” IEEE Trans. Inform. Theory, May 2000), the capacity of fading channels with imperfect channel-state information (CSI) can be lower-bounded by assuming a Gaussian channel input X, and by upper-bounding the conditional entropy h(XY, Ĥ), conditioned on the channel output Y and the CSI Ĥ, by the entropy of a Gaussian random variable with variance equal to the linear minimum mean-square error in estimating X from (Y, Ĥ). We demonstrate that, by using a rate-splitting approach, this lower bound can be sharpened: we show that by expressing the Gaussian input X as as the sum of two independent Gaussian variables X(1) and X(2), and by applying Medard's lower bound first to analyze the mutual information between X(1) and Y conditioned on Ĥ while treating X(2) as noise, and by applying the lower bound then to analyze the mutual information between X(2) and Y conditioned on (X(1), Ĥ), we obtain a lower bound on the capacity that is larger than Medard's lower bound.},
keywords = {channel capacity, channel capacity lower bounds, conditional entropy, Decoding, Entropy, Fading, fading channels, Gaussian channel, Gaussian channels, Gaussian random variable, imperfect channel-state information, imperfect CSI, independent Gaussian variables, linear minimum mean-square error, mean square error methods, Medard lower bound, Mutual information, Random variables, rate splitting approach, Resource management, Upper bound, wireless communications},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ruiz2012,
title = {Bayesian Nonparametric Modeling of Suicide Attempts},
author = {Francisco J R Ruiz and Isabel Valera and Carlos Blanco and Fernando Perez-Cruz},
url = {http://nips.cc/Conferences/2012/Program/event.php?ID=3582},
year  = {2012},
date = {2012-01-01},
booktitle = {Advances in Neural Information Processing Systems 25},
address = {Lake Tahoe},
abstract = {The National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) database contains a large amount of information, regarding the way of life, medical conditions, depression, etc., of a representative sample of the U.S. population. In the present paper, we are interested in seeking the hidden causes behind the suicide attempts, for which we propose to model the subjects using a nonparametric latent model based on the Indian Buffet Process (IBP). Due to the nature of the data, we need to adapt the observation model for discrete random variables. We propose a generative model in which the observations are drawn from a multinomial-logit distribution given the IBP matrix. The implementation of an efficient Gibbs sampler is accomplished using the Laplace approximation, which allows us to integrate out the weighting factors of the multinomial-logit likelihood model. Finally, the experiments over the NESARC database show that our model properly captures some of the hidden causes that model suicide attempts.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Zhong2012a,
title = {Efficient Gaussian Inference Algorithms for Phase Imaging},
author = {Jingshan Zhong and Justin Dauwels and Manuel A Vazquez and Laura Waller},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6287959},
issn = {1520-6149},
year  = {2012},
date = {2012-01-01},
booktitle = {2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {617--620},
publisher = {IEEE},
address = {Kyoto},
abstract = {Novel efficient algorithms are developed to infer the phase of a complex optical field from a sequence of intensity images taken at different defocus distances. The non-linear observation model is approximated by a linear model. The complex optical field is inferred by iterative Kalman smoothing in the Fourier domain: forward and backward sweeps of Kalman recursions are alternated, and in each such sweep, the approximate linear model is refined. By limiting the number of iterations, one can trade off accuracy vs. complexity. The complexity of each iteration in the proposed algorithm is in the order of N logN, where N is the number of pixels per image. The storage required scales linearly with N. In contrast, the complexity of existing phase inference algorithms scales with N3 and the required storage with N2. The proposed algorithms may enable real-time estimation of optical fields from noisy intensity images.},
keywords = {biomedical optical imaging, complex optical field, computational complexity, defocus distances, Fourier domain, Gaussian inference algorithms, image sequences, inference mechanisms, intensity image sequence, iterative Kalman smoothing, iterative methods, Kalman filter, Kalman filters, Kalman recursions, linear model, Manganese, Mathematical model, medical image processing, Noise, noisy intensity image, nonlinear observation model, Optical imaging, Optical sensors, Phase imaging, phase inference algorithms, smoothing methods},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Campo2012,
title = {Random Coding Bounds that Attain the Joint Source-Channel Exponent},
author = {Adria Tauste Campo and Gonzalo Vazquez-Vilar and Albert Guillen i F\`{a}bregas and Tobias Koch and Alfonso Martinez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6310910},
isbn = {978-1-4673-3140-1},
year  = {2012},
date = {2012-01-01},
booktitle = {2012 46th Annual Conference on Information Sciences and Systems (CISS)},
pages = {1--5},
publisher = {IEEE},
address = {Princeton},
abstract = {This paper presents a random-coding upper bound on the average error probability of joint source-channel coding that attains Csiszár's error exponent. The bound is based on a code construction for which source messages are assigned to disjoint subsets (classes), and codewords are generated according to a distribution that depends on the class of the source message. For a single class, the bound recovers Gallager's exponent; identifying the classes with source type classes, it recovers Csiszár's exponent. Moreover, it is shown that as a two appropriately designed classes are sufficient to attain Csiszár's exponent.},
keywords = {code construction, combined source-channel coding, Csiszár error exponent, Ducts, error probability, error statistics, Gallager exponent, joint source-channel coding, joint source-channel exponent, random codes, random-coding upper bound, Yttrium},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Campo2012a,
title = {Achieving Csisz\'{a}r's Source-Channel Coding Exponent with Product Distributions},
author = {Adria Tauste Campo and Gonzalo Vazquez-Vilar and Albert Guillen i Fabregas and Tobias Koch and Alfonso Martinez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6283524},
issn = {2157-8095},
year  = {2012},
date = {2012-01-01},
booktitle = {2012 IEEE International Symposium on Information Theory Proceedings},
pages = {1548--1552},
publisher = {IEEE},
address = {Cambridge, MA},
abstract = {We derive a random-coding upper bound on the average probability of error of joint source-channel coding that recovers Csiszár's error exponent when used with product distributions over the channel inputs. Our proof technique for the error probability analysis employs a code construction for which source messages are assigned to subsets and codewords are generated with a distribution that depends on the subset.},
keywords = {average probability of error, Channel Coding, code construction, codewords, Csiszár's source-channel coding, Decoding, Encoding, error probability, error statistics, Joints, Manganese, product distributions, random codes, random-coding upper bound, source coding, source messages, Upper bound},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Taborda2012a,
title = {Mutual Information and Relative Entropy over the Binomial and Negative Binomial Channels},
author = {Camilo G Taborda and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6284304},
issn = {2157-8095},
year  = {2012},
date = {2012-01-01},
booktitle = {2012 IEEE International Symposium on Information Theory Proceedings},
pages = {696--700},
publisher = {IEEE},
address = {Cambridge, MA},
abstract = {We study the relation of the mutual information and relative entropy over the Binomial and Negative Binomial channels with estimation theoretical quantities, in which we extend already known results for Gaussian and Poisson channels. We establish general expressions for these information theory concepts with a direct connection with estimation theory through the conditional mean estimation and a particular loss function.},
keywords = {Channel estimation, conditional mean estimation, Entropy, Estimation, estimation theoretical quantity, estimation theory, Gaussian channel, Gaussian channels, information theory concept, loss function, mean square error methods, Mutual information, negative binomial channel, Poisson channel, Random variables, relative entropy},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Salamanca2012a,
title = {Tree-Structured Expectation Propagation for LDPC Decoding in AWGN Channels},
author = {Luis Salamanca and Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://www.researchgate.net/publication/236006591_Tree-structured_expectation_propagation_for_LDPC_decoding_in_AWGN_channels},
year  = {2012},
date = {2012-01-01},
booktitle = {Proceeding of: Information Theory and Applications Workshop (ITA)},
address = {San Diego},
abstract = {In this paper, we propose the tree-structured expectation propagation (TEP) algorithm for low-density parity-check (LDPC) decoding over the additive white Gaussian noise (AWGN) channel. By imposing a tree-like approximation over the graphical model of the code, this algorithm introduces pairwise marginal constraints over pairs of variables, which provide joint information of the variables related. Thanks to this, the proposed TEP decoder improves the performance of the standard belief propagation (BP) solution. An efficient way of constructing the tree-like structure is also described. The simulation results illustrate the TEP decoder gain in the finite-length regime, compared to the standard BP solution. For code lengths shorter than n = 512, the gain in the waterfall region achieves up to 0.25 dB. We also notice a remarkable reduction of the error floo},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Henao-Mazo2012,
title = {Finding Diverse Shortest Paths for the Routing Task in Wireless Sensor Networks},
author = {W Henao-Mazo and \'{A}ngel M Bravo-Santos},
url = {http://www.iaria.org/conferences2012/ProgramICSNC12.html},
year  = {2012},
date = {2012-01-01},
booktitle = {ICSNC 2012. The Seventh International Conference on Systems and Networks Communications},
address = {Lisboa},
abstract = {Wireless Sensor Networks are deployed with the idea of collecting field information of different variables like temperature, position, humidity, etc., from several resourceconstrained sensor nodes, and then relay those data to a sink node or base station. Therefore, the path finding for routing must be carried out with strategies that make it possible to manage efficiently the network limited resources, whilst at the same time the network throughput is kept within appreciable levels. Many routing schemes search for one path, with low power dissipation that may not be convenient to increase the network lifetime and long-term connectivity. In an attempt to overcome such eventualities, we proposed a scenario for relaying that uses multiple diverse paths obtained considering the links among network nodes, that could provide reliable data transmission. When data is transmitted across various diverse paths in the network that offer low retransmission rates, the battery demand can be decreased and network lifetime is extended. We show, by using simulations, that the reliability in packets reception and the power dissipation that our scheme offers compare favourably with similar literature implementations.},
keywords = {Diverse Paths., K Shortest, Paths, Wireless Sensor Networks},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Perez-Cruz2012a,
title = {Coding and Approximate Inference},
author = {Fernando Perez-Cruz},
url = {http://mlss2012.tsc.uc3m.es/},
year  = {2012},
date = {2012-01-01},
booktitle = {Machine Learning Summer School (MLSS)},
address = {La Palma},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Achutegui2012,
title = {A Multi-Model Sequential Monte Carlo Methodology for Indoor Tracking: Algorithms and Experimental Results},
author = {Katrin Achutegui and Joaquin Miguez and Javier Rodas and Carlos J Escudero},
url = {http://www.tsc.uc3m.es/~jmiguez/papers/P32_2012_ Multi-Model Sequential Monte Carlo Methodology for Indoor Tracking- Algorithms and Experimental Results.pdf 
http://www.sciencedirect.com/science/article/pii/S0165168412001077},
year  = {2012},
date = {2012-01-01},
journal = {Signal Processing},
volume = {92},
number = {11},
pages = {2594--2613},
abstract = {In this paper we address the problem of indoor tracking using received signal strength (RSS) as a position-dependent data measurement. Since RSS is highly influenced by multipath propagation, it turns out very hard to adequately model the correspondence between the received power and the transmitter-to-receiver distance. Although various models have been proposed in the literature, they often require the use of very large collections of data in order to fit them and display great sensitivity to changes in the radio propagation environment. In this work we advocate the use of switching multiple models that account for different classes of target dynamics and propagation environments and propose a flexible probabilistic switching scheme. The resulting state-space structure is termed a generalized switching multiple model (GSMM) system. Within this framework, we investigate two types of models for the RSS data: polynomial models and classical logarithmic path-loss representation. The first model is more accurate however it demands an offline model fitting step. The second one is less precise but it can be fitted in an online procedure. We have designed two tracking algorithms built around a Rao-Blackwellized particle filter, tailored to the GSMM structure and assessed its performances both with synthetic and experimental measurements.},
keywords = {Data fusion, Indoor positioning, Indoor tracking, Interacting multiple models, Sequential Monte Carlo, Switching observation models},
pubstate = {published},
tppubtype = {article}
}
@article{Martino2012,
title = {A Multi-Point Metropolis Scheme with Generic Weight Functions},
author = {Luca Martino and Victor Pascual Del Olmo and Jesse Read},
url = {http://www.sciencedirect.com/science/article/pii/S0167715212001514},
year  = {2012},
date = {2012-01-01},
journal = {Statistics \&amp;amp; Probability Letters},
volume = {82},
number = {7},
pages = {1445--1453},
abstract = {The multi-point Metropolis algorithm is an advanced MCMC technique based on drawing several correlated samples at each step and choosing one of them according to some normalized weights. We propose a variation of this technique where the weight functions are not specified, i.e., the analytic form can be chosen arbitrarily. This has the advantage of greater flexibility in the design of high-performance MCMC samplers. We prove that our method fulfills the balance condition, and provide a numerical simulation. We also give new insight into the functionality of different MCMC algorithms, and the connections between them.},
keywords = {MCMC methods, Multi-point Metropolis algorithm, Multiple Try Metropolis algorithm},
pubstate = {published},
tppubtype = {article}
}
@article{Salamanca2012b,
title = {Bayesian Equalization for LDPC Channel Decoding},
author = {Luis Salamanca and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6129544},
issn = {1053-587X},
year  = {2012},
date = {2012-01-01},
journal = {IEEE Transactions on Signal Processing},
volume = {60},
number = {5},
pages = {2672--2676},
abstract = {We describe the channel equalization problem, and its prior estimate of the channel state information (CSI), as a joint Bayesian estimation problem to improve each symbol posterior estimates at the input of the channel decoder. Our approach takes into consideration not only the uncertainty due to the noise in the channel, but also the uncertainty in the CSI estimate. However, this solution cannot be computed in linear time, because it depends on all the transmitted symbols. Hence, we also put forward an approximation for each symbol's posterior, using the expectation propagation algorithm, which is optimal from the Kullback-Leibler divergence viewpoint and yields an equalization with a complexity identical to the BCJR algorithm. We also use a graphical model representation of the full posterior, in which the proposed approximation can be readily understood. The proposed posterior estimates are more accurate than those computed using the ML estimate for the CSI. In order to illustrate this point, we measure the error rate at the output of a low-density parity-check decoder, which needs the exact posterior for each symbol to detect the incoming word and it is sensitive to a mismatch in those posterior estimates. For example, for QPSK modulation and a channel with three taps, we can expect gains over 0.5 dB with same computational complexity as the ML receiver.},
keywords = {Approximation methods, Bayes methods, Bayesian equalization, Bayesian estimation problem, Bayesian inference, Bayesian methods, BCJR (Bahl\textendashCocke\textendashJelinek\textendashRaviv) algorithm, BCJR algorithm, Channel Coding, channel decoding, channel equalization, channel equalization problem, Channel estimation, channel state information, CSI, Decoding, equalisers, Equalizers, expectation propagation, expectation propagation algorithm, fading channels, graphical model representation, intersymbol interference, Kullback-Leibler divergence, LDPC, LDPC coding, low-density parity-check decoder, Modulation, parity check codes, symbol posterior estimates, Training},
pubstate = {published},
tppubtype = {article}
}
@article{Landa-Torres2012,
title = {Evaluating the Internationalization Success of Companies Through a Hybrid Grouping Harmony Search\textemdashExtreme Learning Machine Approach},
author = {Itziar Landa-Torres and Emilio G Ortiz-Garcia and Sancho Salcedo-Sanz and Mar\'{i}a J Segovia-Vargas and Sergio Gil-Lopez and Marta Miranda and Jose M Leiva-Murillo and Javier Del Ser},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6200298},
issn = {1932-4553},
year  = {2012},
date = {2012-01-01},
journal = {IEEE Journal of Selected Topics in Signal Processing},
volume = {6},
number = {4},
pages = {388--398},
abstract = {The internationalization of a company is widely understood as the corporative strategy for growing through external markets. It usually embodies a hard process, which affects diverse activities of the value chain and impacts on the organizational structure of the company. There is not a general model for a successful international company, so the success of an internationalization procedure must be estimated based on different variables addressing the status, strategy and market characteristics of the company at hand. This paper presents a novel hybrid soft-computing approach for evaluating the internationalization success of a company based on existing past data. Specifically, we propose a hybrid algorithm composed by a grouping-based harmony search (HS) approach and an extreme learning machine (ELM) ensemble. The proposed hybrid scheme further incorporates a feature selection method, which is obtained by means of a given group in the HS encoding format, whereas the ELM ensemble renders the final accuracy metric of the model. Practical results for the proposed hybrid technique are obtained in a real application based on the exporting success of Spanish manufacturing companies, which are shown to be satisfactory in comparison with alternative state-of-the-art techniques.},
keywords = {Companies, Company internationalization, corporative strategy, diverse activity, Economics, Electronic mail, ensembles, exporting, exporting performance, external markets, extreme learning machine ensemble, extreme learning machines, feature selection method, grouping-based harmony search, hard process, harmony search (HS), hybrid algorithm, hybrid algorithms, hybrid grouping harmony search-extreme learning ma, hybrid soft computing, international company, international trade, internationalization procedure, internationalization success, learning (artificial intelligence), Machine learning, organizational structure, Signal processing algorithms, Spanish manufacturing company, Training, value chain},
pubstate = {published},
tppubtype = {article}
}
@article{Luengo2012a,
title = {Efficient Sampling from Truncated Bivariate Gaussians via Box-Muller Transformation},
author = {David Luengo and Joaquin Miguez and Luca Martino},
url = {http://www.tsc.uc3m.es/~jmiguez/papers/P35_2012_Efficient Sampling from Truncated Bivariate Gaussians via Box-Muller Transformation.pdf 
http://www.researchgate.net/publication/235004345_Efficient_Sampling_from_Truncated_Bivariate_Gaussians_via_the_Box-Muller_Transformation},
issn = {0013-5194},
year  = {2012},
date = {2012-01-01},
journal = {Electronics Letters},
volume = {48},
number = {24},
pages = {1533--1534},
abstract = {Many practical simulation tasks demand procedures to draw samples efficiently from multivariate truncated Gaussian distributions. In this work, we introduce a novel rejection approach, based on the Box-Muller transformation, to generate samples from a truncated bivariate Gaussian density with an arbitrary support. Furthermore, for an important class of support regions the new method allows us to achieve exact sampling, thus becoming the most efficient approach possible. Introduction: The numerical simulation of many systems of practical interest demands the ability to produce Monte Carlo samples from truncated Gaussian distributions [5, 3, 7]. The simplest way to address this problem is to perform rejection sampling using the corresponding (non-truncated) Gaussian distribution as a proposal. This trivial method produces independent and identically distributed (i.i.d.) samples, but it is time consuming and computationally inefficient. For these two reasons, different methods have been introduced in the literature, e.g., using MCMC techniques [5, 7] or rejection sampling [1]. Unfortunately, MCMC schemes produce correlated samples, which can lead to a very slow convergence of the chain, whereas rejection methods can be computationally inefficient. In this paper, we introduce a novel approach, based on the Box-Muller transformation (BMT) [2], to generate i.i.d. samples from truncated bivariate Gaussian distributions. The main advantages of the proposed approach are the following: (1) it allows sampling within a generic domain D ⊆ R 2 without any restriction and (2) the inverse transformation of the BMT maps any region D ⊆ R 2 (either bounded or unbounded) into a bounded region, A ⊆ R = [0, 1] × [0, 1]. Hence, all the procedures developed for drawing efficiently uniform random variables within bounded regions, e.g., adaptive rejection sampling or strip methods [2, 4], can always be used. Furthermore, for an important class of support regions the BMT allows us to perform exact sampling (i.e., draw i.i.d. samples from the target distribution without any rejection), which is the most efficient situation possible. Problem Formulation: The problem considered here is related to drawing samples from a truncated multivariate Gaussian distribution. In particular, in this letter we focus on drawing samples from a bivariate truncated standard Gaussian PDF, denoted as Z ∼ T N (0, I, D), where the support domain D ⊆ R 2 is a non-null Borel set. Note that drawing samples from a non-truncated standard Gaussian distribution, Z ∼ N (0, I), enables us to draw samples from an arbitrary Gaussian distribution, X ∼ N (µ, $Sigma$), whenever $Sigma$ is positive definite. More precisely, since $Sigma$ is positive definite, it can be expressed as $Sigma$ = SS , using for instance the Cholesky decomposition, and the random vector X = SZ + µ has the desired distribution, X ∼ N (µ, $Sigma$). Similarly, sampling from a truncated bivariate standard Gaussian distribution allows us to generate samples from an arbitrary truncated bivariate Gaussian. In this case, if Z ∼ T N (0, I, D), then we can obtain X ∼ T N (µ, $Sigma$, D *) simply through the transformation X = SZ + µ, with $Sigma$ = SS and},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Leiva-Murillo2012,
title = {Algorithms for Maximum-Likelihood Bandwidth Selection in Kernel Density Estimators},
author = {Jose M Leiva-Murillo and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.tsc.uc3m.es/~antonio/papers/P45_2012_Algorithms for Maximum Likelihood Bandwidth Selection in Kernel Density Estimators.pdf 
http://www.sciencedirect.com/science/article/pii/S0167865512001948},
issn = {01678655},
year  = {2012},
date = {2012-01-01},
journal = {Pattern Recognition Letters},
volume = {33},
number = {13},
pages = {1717--1724},
publisher = {Elsevier Science Inc.},
abstract = {In machine learning and statistics, kernel density estimators are rarely used on multivariate data due to the difficulty of finding an appropriate kernel bandwidth to overcome overfitting. However, the recent advances on information-theoretic learning have revived the interest on these models. With this motivation, in this paper we revisit the classical statistical problem of data-driven bandwidth selection by cross-validation maximum likelihood for Gaussian kernels. We find a solution to the optimization problem under both the spherical and the general case where a full covariance matrix is considered for the kernel. The fixed-point algorithms proposed in this paper obtain the maximum likelihood bandwidth in few iterations, without performing an exhaustive bandwidth search, which is unfeasible in the multivariate case. The convergence of the methods proposed is proved. A set of classification experiments are performed to prove the usefulness of the obtained models in pattern recognition.},
keywords = {Kernel density estimation, Multivariate density modeling, Pattern recognition},
pubstate = {published},
tppubtype = {article}
}
@article{Maiz2012,
title = {A Particle Filtering Scheme for Processing Time Series Corrupted by Outliers},
author = {Cristina S Maiz and Elisa M Molanes-Lopez and Joaquin Miguez and Petar M Djuric},
url = {http://www.tsc.uc3m.es/~jmiguez/papers/P34_2012_A Particle Filtering Scheme for Processing Time Series Corrupted by Outliers.pdf http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6203606},
issn = {1053-587X},
year  = {2012},
date = {2012-01-01},
journal = {IEEE Transactions on Signal Processing},
volume = {60},
number = {9},
pages = {4611--4627},
abstract = {The literature in engineering and statistics is abounding in techniques for detecting and properly processing anomalous observations in the data. Most of these techniques have been developed in the framework of static models and it is only in recent years that we have seen attempts that address the presence of outliers in nonlinear time series. For a target tracking problem described by a nonlinear state-space model, we propose the online detection of outliers by including an outlier detection step within the standard particle filtering algorithm. The outlier detection step is implemented by a test involving a statistic of the predictive distribution of the observations, such as a concentration measure or an extreme upper quantile. We also provide asymptotic results about the convergence of the particle approximations of the predictive distribution (and its statistics) and assess the performance of the resulting algorithms by computer simulations of target tracking problems with signal power observations.},
keywords = {Kalman filters, Mathematical model, nonlinear state space model, Outlier detection, prediction theory, predictive distribution, Probability density function, State-space methods, state-space models, statistical distributions, Target tracking, time serie processing, Vectors, Yttrium},
pubstate = {published},
tppubtype = {article}
}
@article{Cruz-Roldan2012,
title = {On the Use of Discrete Cosine Transforms for Multicarrier Communications},
author = {Fernando Cruz-Roldan and Mar\'{i}a Elena Dominguez-Jimenez and Gabriela Sansigre Vidal and Pedro Amo-Lopez and Manuel Blanco-Velasco and \'{A}ngel M Bravo-Santos},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6252068},
issn = {1053-587X},
year  = {2012},
date = {2012-01-01},
journal = {IEEE Transactions on Signal Processing},
volume = {60},
number = {11},
pages = {6085--6090},
abstract = {In this correspondence, the conditions to use any kind of discrete cosine transform (DCT) for multicarrier data transmission are derived. The symmetric convolution-multiplication property of each DCT implies that when symmetric convolution is performed in the time domain, an element-by-element multiplication is performed in the corresponding discrete trigonometric domain. Therefore, appending symmetric redundancy (as prefix and suffix) into each data symbol to be transmitted, and also enforcing symmetry for the equivalent channel impulse response, the linear convolution performed in the transmission channel becomes a symmetric convolution in those samples of interest. Furthermore, the channel equalization can be carried out by means of a bank of scalars in the corresponding discrete cosine transform domain. The expressions for obtaining the value of each scalar corresponding to these one-tap per subcarrier equalizers are presented. This study is completed with several computer simulations in mobile broadband wireless communication scenarios, considering the presence of carrier frequency offset (CFO). The obtained results indicate that the proposed systems outperform the standardized ones based on the DFT.},
keywords = {broadband networks, carrier frequency offset, Carrier-frequency offset (CFO), CFO, channel equalization, computer simulations, Convolution, Data communication, data symbol, DCT, DFT, discrete cosine transform (DCT), discrete cosine transform domain, Discrete cosine transforms, discrete Fourier transforms, discrete multitone modulation (DMT), discrete trigonometric domain, element-by-element multiplication, equalisers, equivalent channel impulse response, linear convolution, mobile broadband wireless communication, mobile radio, Modulation, multicarrier communications, multicarrier data transmission, multicarrier modulation (MCM), multicarrier transceiver, OFDM, orthogonal frequency-division multiplexing (OFDM), Receivers, Redundancy, subcarrier equalizers, symmetric convolution-multiplication property, symmetric redundancy, time-domain analysis, transient response, transmission channel},
pubstate = {published},
tppubtype = {article}
}
@article{Leiva-Murillo2012a,
title = {Information-Theoretic Linear Feature Extraction Based on Kernel Density Estimators: A Review},
author = {Jose M Leiva-Murillo and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.tsc.uc3m.es/~antonio/papers/P44_2012_Information Theoretic Linear Feature Extraction Based on Kernel Density Estimators A Review.pdf http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6185689},
issn = {1094-6977},
year  = {2012},
date = {2012-01-01},
journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)},
volume = {42},
number = {6},
pages = {1180--1189},
abstract = {In this paper, we provide a unified study of the application of kernel density estimators to supervised linear feature extraction by means of criteria inspired by information and detection theory. We enrich this study by the incorporation of two novel criteria to the study, i.e., the mutual information and the likelihood ratio test, and perform both a theoretical and an experimental comparison between the new methods and other ones previously described in the literature. The impact of the bandwidth selection of the density estimator in the classification performance is discussed. Some theoretical results that bound classification performance as a function or mutual information are also compiled. A set of experiments on different real-world datasets allows us to perform an empirical comparison of the methods, in terms of both accuracy and computational complexity. We show the suitability of these methods to determine the dimension of the subspace that contains the discriminative information.},
keywords = {Bandwidth, Density, detection theory, Entropy, Estimation, Feature extraction, Feature extraction (FE), information theoretic linear feature extraction, information theory, information-theoretic learning (ITL), Kernel, Kernel density estimation, kernel density estimators, Machine learning},
pubstate = {published},
tppubtype = {article}
}
@article{Luengo2012b,
title = {Novel Fast Random Search Clustering Algorithm for Mixing Matrix Identification in MIMO Linear Blind Inverse Problems with Sparse Inputs},
author = {David Luengo and Sandra Monzon and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.tsc.uc3m.es/~antonio/papers/P43_2012_Novel Fast Random Search Clustering Algorithm for Mixing Matrix Identification in MIMO Linear Blind Inverse Problems with Sparse Inputs.pdf 
http://www.sciencedirect.com/science/article/pii/S0925231212000744},
year  = {2012},
date = {2012-01-01},
journal = {Neurocomputing},
volume = {87},
pages = {62--78},
abstract = {In this paper we propose a novel fast random search clustering (RSC) algorithm for mixing matrix identification in multiple input multiple output (MIMO) linear blind inverse problems with sparse inputs. The proposed approach is based on the clustering of the observations around the directions given by the columns of the mixing matrix that occurs typically for sparse inputs. Exploiting this fact, the RSC algorithm proceeds by parameterizing the mixing matrix using hyperspherical coordinates, randomly selecting candidate basis vectors (i.e. clustering directions) from the observations, and accepting or rejecting them according to a binary hypothesis test based on the Neyman\textendashPearson criterion. The RSC algorithm is not tailored to any specific distribution for the sources, can deal with an arbitrary number of inputs and outputs (thus solving the difficult under-determined problem), and is applicable to both instantaneous and convolutive mixtures. Extensive simulations for synthetic and real data with different number of inputs and outputs, data size, sparsity factors of the inputs and signal to noise ratios confirm the good performance of the proposed approach under moderate/high signal to noise ratios.},
keywords = {Line orientation clustering, Linear blind inverse problems, MIMO systems, Neyman\textendashPearson hypothesis test, Sparse signals},
pubstate = {published},
tppubtype = {article}
}
@article{Oquendo2012,
title = {Machine Learning and Data Mining: Strategies for Hypothesis Generation},
author = {Maria A Oquendo and Enrique Baca-Garc\'{i}a and Antonio Art\'{e}s-Rodr\'{i}guez and Fernando Perez-Cruz and H C Galfalvy and Hilario Blasco-Fontecilla and D Madigan and N Duan},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22230882},
issn = {1476-5578},
year  = {2012},
date = {2012-01-01},
journal = {Molecular psychiatry},
volume = {17},
number = {10},
pages = {956--959},
abstract = {Strategies for generating knowledge in medicine have included observation of associations in clinical or research settings and more recently, development of pathophysiological models based on molecular biology. Although critically important, they limit hypothesis generation to an incremental pace. Machine learning and data mining are alternative approaches to identifying new vistas to pursue, as is already evident in the literature. In concert with these analytic strategies, novel approaches to data collection can enhance the hypothesis pipeline as well. In data farming, data are obtained in an \'{o}rganic' way, in the sense that it is entered by patients themselves and available for harvesting. In contrast, in evidence farming (EF), it is the provider who enters medical data about individual patients. EF differs from regular electronic medical record systems because frontline providers can use it to learn from their own past experience. In addition to the possibility of generating large databases with farming approaches, it is likely that we can further harness the power of large data sets collected using either farming or more standard techniques through implementation of data-mining and machine-learning strategies. Exploiting large databases to develop new hypotheses regarding neurobiological and genetic underpinnings of psychiatric illness is useful in itself, but also affords the opportunity to identify novel mechanisms to be targeted in drug discovery and development.},
keywords = {Artificial Intelligence, Biological, Data Mining, Humans, Mental Disorders, Mental Disorders: diagnosis, Mental Disorders: therapy, Models},
pubstate = {published},
tppubtype = {article}
}
@article{Reyes-Guerrero2012,
title = {Remote Detection of Interfered Downlinks in Wireless Cellular Systems},
author = {J C Reyes-Guerrero and Juan Jose Murillo-Fuentes and Pablo M Olmos},
url = {http://doi.wiley.com/10.1002/ett.2501},
issn = {21613915},
year  = {2012},
date = {2012-01-01},
journal = {Transactions on Emerging Telecommunications Technologies},
volume = {23},
number = {5},
pages = {444--453},
abstract = {This work provides a novel technological solution to jamming in wireless systems, particularly to remotely detect interfered communications in a cellular network. The new system is focused on the detection of a failure in a link between a base station and a fixed wireless terminal located in a residential or business area. It has an important impact in security systems based on wireless terminals to transmit an alarm to a central station. In these systems, non-authorised people can prevent the transmission of the alarm by using a short-range jammer. The main advantage of this proposal is that it is non-intrusive; that is, no modification is needed in the base station, and no protocol modification is performed in the terminal. The detection is implemented in an external unit developed on a software-defined radio platform. The novel system proposed is valid for any cellular system and operator. In this work, we focus on its implementation in the GSM/GPRS system to illustrate its benefits and outline the method for Universal Mobile Telecommunications System. We describe the results of some experiments where the system successfully detects the presence of a short-range jammer in a real scenario.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Olmos2012b,
title = {On the Design of LDPC-Convolutional Ensembles Using the TEP Decoder},
author = {Pablo M Olmos and Luis Salamanca and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6168872},
issn = {1089-7798},
year  = {2012},
date = {2012-01-01},
journal = {IEEE Communications Letters},
volume = {16},
number = {5},
pages = {726--729},
abstract = {Low-density parity-check convolutional (LDPCC) codes asymptotically achieve channel capacity under belief propagation (BP) decoding. In this paper, we decode LDPCC codes using the Tree-Expectation Propagation (TEP) decoder, recently proposed as an alternative decoding method to the BP algorithm for the binary erasure channel (BEC). We show that, for LDPCC codes, the TEP decoder improves the BP solution with a comparable complexity or, alternatively, it allows using shorter codes to achieve similar error rates. We also propose a window-sliding scheme for the TEP decoder to reduce the decoding latency.},
keywords = {belief propagation decoding, binary erasure channel, channel capacity, Complexity theory, convolutional codes, convolutional LDPC codes, Decoding, design, Error analysis, finite-length analysis, Iterative decoding, LDPC-convolutional ensemble design, LDPCC code decoding, low-density parity-check convolutional code, parity check codes, tree-expectation propagation decoder, tree-structured expectation propagation, window-sliding scheme},
pubstate = {published},
tppubtype = {article}
}
@article{Luengo2012bb,
title = {Efficient Random Variable Generation: Ratio of Uniforms and Polar Rejection Sampling},
author = {David Luengo and Luca Martino},
url = {http://digital-library.theiet.org/content/journals/10.1049/el.2012.0206},
issn = {00135194},
year  = {2012},
date = {2012-01-01},
journal = {Electronics Letters},
volume = {48},
number = {6},
pages = {326--327},
abstract = {Monte Carlo techniques, which require the generation of samples from some target density, are often the only alternative for performing Bayesian inference. Two classic sampling techniques to draw independent samples are the ratio of uniforms (RoU) and rejection sampling (RS). An efficient sampling algorithm is proposed combining the RoU and polar RS (i.e. RS inside a sector of a circle using polar coordinates). Its efficiency is shown in drawing samples from truncated Cauchy and Gaussian random variables, which have many important applications in signal processing and communications.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Read2012b,
title = {Batch-Incremental versus Instance-Incremental Learning in Dynamic and Evolving Data},
author = {Jesse Read and Albert Bifet and Bernhard Pfahringer and Geoff Holmes},
year  = {2012},
date = {2012-01-01},
booktitle = {The Eleventh International Symposium on Intelligent Data Analysis (IDA 2012).},
address = {Helsinki},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Luengo2012ab,
title = {Almost rejectionless sampling from Nakagami-m distributions (m≥1)},
author = {David Luengo and Luca Martino},
url = {http://digital-library.theiet.org/content/view.action?itemId=http://iet.metastore.ingenta.com/content/journals/10.1049/el.2012.3513\&amp;amp;view=\&amp;amp;itemType=http://pub2web.metastore.ingenta.com/ns/Article?itemId=http://iet.metastore.ingenta.com/content/journals/10.1049/el.2012.3513\&amp;amp;view=\&amp;amp;itemType=http://pub2web.metastore.ingenta.com/ns/Article},
issn = {0013-5194},
year  = {2012},
date = {2012-01-01},
journal = {Electronics Letters},
volume = {48},
number = {24},
pages = {1559--1561},
publisher = {IET Digital Library},
abstract = {The Nakagami-textitm distribution is widely used for the simulation of fading channels in wireless communications. A novel, simple and extremely efficient acceptance-rejection algorithm is introduced for the generation of independent Nakagami-textitm random variables. The proposed method uses another Nakagami density with a half-integer value of the fading parameter},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Lopez-Castroman2012,
title = {P-1266 - Dimensional Schizophrenia: not an Easy Transition},
author = {Jorge L\'{o}pez-Castrom\'{a}n and Jose M Leiva-Murillo and Hilario Blasco-Fontecilla and R Garcia-Nieto and C Morant-Ginestar and Carlos Blanco and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a},
url = {http://www.sciencedirect.com/science/article/pii/S0924933812754330},
year  = {2012},
date = {2012-01-01},
journal = {European Psychiatry},
volume = {27},
pages = {1},
abstract = {Recently, several authors have argued in favor of extending the less common clinical phenotype of schizophrenia to a vulnerability phenotype of schizophrenia in the general population. It has been proposed that high levels in any of four different symptom dimensions (affective, psychosis, negative and cognitive) would lead to clinical assessment, identification of correlated symptoms in other dimensions and finally, the diagnosis of schizophrenia. Being so, we would expect to find such a dimensional pattern in the previous diagnoses of schizophrenic patients. We examined previous contacts of a large cohort of patients diagnosed, according to the International Classification of Diseases (ICD-10), with schizophrenia (n=26,163) in public mental health centers of Madrid (Spain) from 1980 to 2008. Of those patients, 56.7% received another diagnosis prior to schizophrenia. Non-schizophrenia diagnoses within the category of ‘schizophrenia, schizotypal and delusional disorders’ were common (F2; 40.0%). The other most frequent prior diagnoses were ‘neurotic, stress-related and somatoform disorders’ (F4; 47.3%), ‘mood disorders’ (F3; 41.4%), and ‘disorders of adult personality and behavior’ (F6; 20.8%). We then examined the probability of progression to schizophrenia, considering also time proximity. The strongest associations were between several F2 spectrum diagnoses with schizophrenia. However, some affective disorders (F3x) were also linked with schizophrenia but anxiety (F4) or personality disorders (F6) were not. Our findings support two of the previously described dimensions (psychotic, affective) in the development of schizophrenia. Several limitations of the dimensional model will be discussed in view of these findings.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{twc11,
title = {Multiantenna spectrum sensing exploiting spectral a priori information},
author = {Gonzalo Vazquez-Vilar and Roberto L\'{o}pez-Valcarce and Josep Sala},
doi = {10.1109/TWC.2011.101211.110665},
issn = {1536-1276},
year  = {2011},
date = {2011-12-01},
journal = {IEEE Transactions on Wireless Communications},
volume = {10},
number = {12},
pages = {4345-4355},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{tsp11,
title = {Spectrum sensing exploiting guard bands and weak channels},
author = {Gonzalo Vazquez-Vilar and Roberto L\'{o}pez-Valcarce},
doi = {10.1109/TSP.2011.2167615},
issn = {1053-587X},
year  = {2011},
date = {2011-12-01},
journal = {IEEE Transactions on Signal Processing},
volume = {59},
number = {12},
pages = {6045-6057},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{cogart2011,
title = {Spatial rank estimation in Cognitive Radio Networks with uncalibrated multiple antennas},
author = {Gonzalo Vazquez-Vilar and David Ramirez and Roberto L\'{o}pez-Valcarce and Javier Via and Ignacio Santamaria},
year  = {2011},
date = {2011-10-01},
booktitle = {4th International Conference on Cognitive Radio and Advanced Spectrum Management (CogART 2011)},
address = {Barcelona, Spain},
note = {Invited},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{ramirez11,
title = {Detection of rank-P Signals in Cognitive Radio Networks With Uncalibrated Multiple Antennas},
author = {David Ramirez and Gonzalo Vazquez-Vilar and Roberto Lopez-Valcarce and Javier Via and Ignacio Santamaria},
doi = {10.1109/TSP.2011.2146779},
issn = {1053-587X},
year  = {2011},
date = {2011-08-01},
journal = {IEEE Transactions on Signal Processing},
volume = {59},
number = {8},
pages = {3764-3774},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{isit2011,
title = {Random-Coding Joint Source-Channel Bounds},
author = {Adria Tauste Campo and Gonzalo Vazquez-Vilar and Albert Guill\'{e}n i F\`{a}bregas and Alfonso Martinez},
year  = {2011},
date = {2011-07-01},
booktitle = {2011 IEEE International Symposium on Information Theory (ISIT 2011)},
address = {Saint Petersburg, Russia},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{iccasp2011a,
title = {Detection diversity of multiantenna spectrum sensors},
author = {Gonzalo Vazquez-Vilar and Roberto L\'{o}pez-Valcarce and Ashish Pandharipande},
year  = {2011},
date = {2011-05-01},
booktitle = {2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)},
address = {Prague, Czech Republic},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{iccasp2011b,
title = {Multiantenna Detection under Noise uncertainty and primary user's spatial structure},
author = {David Ramirez and Gonzalo Vazquez-Vilar and Roberto L\'{o}pez-Valcarce and Javier Via and Ignacio Santamaria},
year  = {2011},
date = {2011-05-01},
booktitle = {2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)},
address = {Prague, Czech Republic},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Salamanca2011,
title = {Reduced Complexity MAP Decoder for LDPC Codes over the BEC Using Tree-Structure Expectation Propagation},
author = {Luis Salamanca and Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://www.researchgate.net/publication/236006584_Reduced_Complexity_MAP_decoder_for_LDPC_codes_over_the_BEC_using_Tree-Structure_Expectation_Propagation},
year  = {2011},
date = {2011-01-01},
booktitle = {Information Theory and Applications (ITA)},
address = {San Diego},
abstract = {In this paper, we propose an algorithm that achieves the MAP solution to decode LDPC codes over the binary erasure channel (BEC). This algorithm, denoted as generalized tree-structured expectation propagation (GTEP), extends the idea of our previous work: the TEP decoder. Both proposals borrow from the tree-structured expectation propagation algorithm, which imposes a tree-like approximation over the original graphical model. However, whereas the TEP decoder only considers up to degree two check nodes, the proposed GTEP modifies the graph by eliminating a check node of any degree and merging this information with the remaining graph. The decoder builds a tree graph of relations between the erased variable nodes with respect to some parent variables. The GTEP algorithm upon completion either provides the unique MAP solution or a tree graph in which the number of parent nodes indicates the multiplicity of the MAP solution. This algorithm can be easily described for the BEC, and it can be cast as a generalized peeling decoder. The GTEP decoder can look for checks nodes of minimum degree to be eliminated first, optimizing the complexity of the decoder. Furthermore, this procedure yields an upper bound for the complexity of the MAP decoder. We include an analysis of the computational complexity of this novel decoder to show that it is a function of the erasure value of the channel, the length of the codeword and the ensemble of the code. We illustrate the proposed algorithm with regular codes, which do not present error floors and achieve capacity when the number of ones per column increases.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Balasingam2011,
title = {Efficient Distributed Resampling for Particle Filters},
author = {Balakumar Balasingam and Miodrag Bolic and Petar M Djuric and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5947172},
issn = {1520-6149},
year  = {2011},
date = {2011-01-01},
booktitle = {2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {3772--3775},
publisher = {IEEE},
address = {Prague},
abstract = {In particle filtering, resampling is the only step that cannot be fully parallelized. Recently, we have proposed algorithms for distributed resampling implemented on architectures with concurrent processing elements (PEs). The objective of distributed resampling is to reduce the communication among the PEs while not compromising the performance of the particle filter. An additional objective for implementation is to reduce the communication among the PEs. In this paper, we report an improved version of the distributed resampling algorithm that optimally selects the particles for communication between the PEs of the distributed scheme. Computer simulations are provided that demonstrate the improved performance of the proposed algorithm.},
keywords = {Approximation algorithms, Copper, Covariance matrix, distributed resampling, Markov processes, Probability density function, Sequential Monte-Carlo methods, Signal processing, Signal processing algorithms},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ruiz2011,
title = {Zero-Error Codes for the Noisy-Typewriter Channel},
author = {Francisco J R Ruiz and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6089510},
isbn = {978-1-4577-0437-6},
year  = {2011},
date = {2011-01-01},
booktitle = {2011 IEEE Information Theory Workshop},
pages = {495--497},
publisher = {IEEE},
address = {Paraty},
abstract = {In this paper, we propose nontrivial codes that achieve a non-zero zero-error rate for several odd-letter noisy-typewriter channels. Some of these codes (specifically, those which are defined for a number of letters of the channel of the form 2n + 1) achieve the best-known lower bound on the zero-error capacity. We build the codes using linear codes over rings, as we do not require the multiplicative inverse to build the codes.},
keywords = {channel capacity, Channel Coding, Equations, Linear code, Noise measurement, noisy-typewriter channel, nontrivial codes, nonzero zero-error rate, odd-letter noisy-typewriter channels, Upper bound, Vectors, zero-error capacity, zero-error codes},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Koch2011,
title = {Asymmetric Quantizers are Better at Low SNR},
author = {Tobias Koch and Amos Lapidoth},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6034037},
issn = {2157-8095},
year  = {2011},
date = {2011-01-01},
booktitle = {2011 IEEE International Symposium on Information Theory Proceedings},
pages = {2592--2596},
publisher = {IEEE},
address = {St. Petersburg},
abstract = {We study the behavior of channel capacity when a one-bit quantizer is employed at the output of the discrete-time average-power-limited Gaussian channel. We focus on the low signal-to-noise ratio regime, where communication at very low spectral efficiencies takes place, as in Spread-Spectrum and Ultra-Wideband communications. It is well known that, in this regime, a symmetric one-bit quantizer reduces capacity by 2/$pi$, which translates to a power loss of approximately two decibels. Here we show that if an asymmetric one-bit quantizer is employed, and if asymmetric signal constellations are used, then these two decibels can be recovered in full.},
keywords = {asymmetric one-bit quantizer, asymmetric signal constellations, channel capacity, Channel Coding, Constellation diagram, Decoding, discrete-time average-power-limited Gaussian chann, Gaussian channels, quantization, Signal to noise ratio, signal-to-noise ratio, SNR, spread spectrum communication, spread-spectrum communications, ultra wideband communication, ultrawideband communications, Upper bound},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Olmos2011,
title = {Scaling Behavior of Convolutional LDPC Ensembles over the BEC},
author = {Pablo M Olmos and Rudiger Urbanke},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6033863},
issn = {2157-8095},
year  = {2011},
date = {2011-01-01},
booktitle = {2011 IEEE International Symposium on Information Theory Proceedings},
pages = {1816--1820},
publisher = {IEEE},
address = {Saint Petersburg},
abstract = {We study the scaling behavior of coupled sparse graph codes over the binary erasure channel. In particular, let 2L+1 be the length of the coupled chain, let M be the number of variables in each of the 2L+1 local copies, let ℓ be the number of iterations, let Pb denote the bit error probability, and let ∈ denote the channel parameter. We are interested in how these quantities scale when we let the blocklength (2L + 1)M tend to infinity. Based on empirical evidence we show that the threshold saturation phenomenon is rather stable with respect to the scaling of the various parameters and we formulate some general rules of thumb which can serve as a guide for the design of coding systems based on coupled graphs.},
keywords = {BEC, binary codes, binary erasure channel, Bit error rate, convolutional codes, convolutional LDPC ensembles, coupled sparse graph codes, Couplings, Decoding, error probability, Iterative decoding, parity check codes, scaling behavior},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Taborda2011,
title = {Information Theory Concepts and their Relationship with the Bregman Loss Functions},
author = {Camilo G Taborda and Fernando Perez-Cruz},
url = {http://www.it.pt/auto_temp_web_page_preview.asp?id=961},
year  = {2011},
date = {2011-01-01},
booktitle = {Workshop on Topics in Information Theory and Communications (WTITC’11)},
address = {Porto},
abstract = {Among the past eight years the information theory has become interested in the exploration of links between the information and estimation theory. The best known results show how the mean square error and the mutual information between two random variables (input and output) over a Gaussian channel can be related. Similar results illustrate that, for the Poisson channel, exists different loss functions that can be associated with information theory concepts such as the mutual information and the relative entropy. The talk is oriented in the following way; initially we analyzed different properties that share the mean square error and its counterparts for the Poisson channel. Some results obtained early by the research community can be seen as consequences of the behavior of the analyzed loss functions. In addition, we present a broader version of the results obtained previously for both channels, we also establish the behavior of the mutual information between two random variables when the conditional distribution of the channel comes from the exponential family. One of the main issues explored along the talk is determining in which cases the loss function involved in the behavior of the mutual information corresponds to a Bregman Loss Function, situation that enable us to establish geometrical properties of the found relations},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Goparaju2011,
title = {When to Add Another Dimension when Communicating over MIMO Channels},
author = {S Goparaju and A R Calderbank and W R Carson and Miguel R D Rodrigues and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5946351},
issn = {1520-6149},
year  = {2011},
date = {2011-01-01},
booktitle = {2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {3100--3103},
publisher = {IEEE},
address = {Prague},
abstract = {This paper introduces a divide and conquer approach to the design of transmit and receive filters for communication over a Multiple Input Multiple Output (MIMO) Gaussian channel subject to an average power constraint. It involves conversion to a set of parallel scalar channels, possibly with very different gains, followed by coding per sub-channel (i.e. over time) rather than coding across sub-channels (i.e. over time and space). The loss in performance is negligible at high signal-to-noise ratio (SNR) and not significant at medium SNR. The advantages are reduction in signal processing complexity and greater insight into the SNR thresholds at which a channel is first allocated power. This insight is a consequence of formulating the optimal power allocation in terms of an upper bound on error rate that is determined by parameters of the input lattice such as the minimum distance and kissing number. The resulting thresholds are given explicitly in terms of these lattice parameters. By contrast, when the optimization problem is phrased in terms of maximizing mutual information, the solution is mercury waterfilling, and the thresholds are implicit.},
keywords = {divide and conquer approach, divide and conquer methods, error probability, error rate, error statistics, Gaussian channels, Lattices, Manganese, MIMO, MIMO channel, MIMO communication, multiple input multiple output Gaussian channel, Mutual information, optimal power allocation, power allocation, power constraint, receive filter, Resource management, Signal to noise ratio, signal-to-noise ratio, transmit filter, Upper bound},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Leiva-Murillo2011,
title = {Visualization and Prediction of Disease Interactions with Continuous-Time Hidden Markov Models},
author = {Jose M Leiva-Murillo and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a},
url = {http://eprints.pascal-network.org/archive/00009110/},
year  = {2011},
date = {2011-01-01},
booktitle = {NIPS 2011 Workshop on Personalized Medicine.},
address = {Sierra Nevada},
abstract = {This paper describes a method for discovering disease relationships and the evolution of diseases from medical records. The method makes use of continuous-time Markov chain models that overcome some drawbacks of the more widely used discrete-time chain models. The model addresses uncertainty in the diagnoses, possible diagnosis errors and the existence of multiple alternative diagnoses in the records. A set of experiments, performed on a dataset of psychiatric medical records, shows the capability of the model to visualize maps of comorbidity and causal interactions among diseases as well as to perform predictions of future evolution of diseases.},
keywords = {Computational, Information-Theoretic Learning with Statistics, Theory \&amp;amp;amp; Algorithms},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Olmos2011a,
title = {An Application of Tree-Structured Expectation Propagation for Channel Decoding},
author = {Pablo M Olmos and Luis Salamanca and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
year  = {2011},
date = {2011-01-01},
booktitle = {Neural Information Processing Systems Foundation (NIPS)},
address = {Granada},
abstract = {We show an application of a tree structure for approximate inference in graphical models using the expectation propagation algorithm. These approximations are typically used over graphs with short-range cycles. We demonstrate that these approximations also help in sparse graphs with long-range loops, as the ones used in coding theory to approach channel capacity. For asymptotically large sparse graph, the expectation propagation algorithm together with the tree structure yields a completely disconnected approximation to the graphical model but, for for finite-length practical sparse graphs, the tree structure approximation to the code graph provides accurate estimates for the marginal of each variable. Furthermore, we propose a new method for constructing the tree structure on the fly that might be more amenable for sparse graphs with general factors.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Olmos2011b,
title = {Capacity Achieving LDPC Ensembles for the TEP Decoder in Erasure Channels},
author = {Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6033993},
issn = {2157-8095},
year  = {2011},
date = {2011-01-01},
booktitle = {2011 IEEE International Symposium on Information Theory Proceedings},
pages = {2398--2402},
publisher = {IEEE},
address = {St. Petersburg},
abstract = {In this work we address the design of degree distributions (DD) of low-density parity-check (LDPC) codes for the tree-expectation propagation (TEP) decoder. The optimization problem to find distributions to maximize the TEP decoding threshold for a fixed-rate code can not be analytically solved. We derive a simplified optimization problem that can be easily solved since it is based in the analytic expressions of the peeling decoder. Two kinds of solutions are obtained from this problem: we either design LDPC ensembles for which the BP threshold equals the MAP threshold or we get LDPC ensembles for which the TEP threshold outperforms the BP threshold, even achieving the MAP capacity in some cases. Hence, we proved that there exist ensembles for which the MAP solution can be obtained with linear complexity even though the BP threshold does not achieve the MAP threshold.},
keywords = {BP threshold, Complexity theory, Decoding, Differential equations, erasure channels, fixed-rate code, Iterative decoding, LDPC, low-density parity-check codes, MAP capacity, MAP threshold, optimisation, Optimization, optimization problem, parity check codes, TEP decoder, tree-expectation propagation decoder},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Asyhari2011,
title = {Nearest Neighbour Decoding and Pilot-Aided Channel Estimation in Stationary Gaussian Flat-Fading Channels},
author = {Taufiq A Asyhari and Tobias Koch and Albert Guill\'{e}n i F\`{a}bregas},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6034081},
issn = {2157-8095},
year  = {2011},
date = {2011-01-01},
booktitle = {2011 IEEE International Symposium on Information Theory Proceedings},
pages = {2786--2790},
publisher = {IEEE},
address = {St. Petersburg},
abstract = {We study the information rates of non-coherent, stationary, Gaussian, multiple-input multiple-output (MIMO) flat-fading channels that are achievable with nearest neighbour decoding and pilot-aided channel estimation. In particular, we analyse the behaviour of these achievable rates in the limit as the signal-to-noise ratio (SNR) tends to infinity. We demonstrate that nearest neighbour decoding and pilot-aided channel estimation achieves the capacity pre-log-which is defined as the limiting ratio of the capacity to the logarithm of SNR as the SNR tends to infinity-of non-coherent multiple-input single-output (MISO) flat-fading channels, and it achieves the best so far known lower bound on the capacity pre-log of non-coherent MIMO flat-fading channels.},
keywords = {Channel estimation, Decoding, Fading, fading channels, Gaussian channels, MIMO, MIMO communication, MISO, multiple-input multiple-output, nearest neighbour decoding, noncoherent multiple-input single-output, pilot-aided channel estimation, Receiving antennas, Signal to noise ratio, signal-to-noise ratio, SNR, stationary Gaussian flat-fading channels, Wireless communication},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Maiz2011,
title = {On the Optimization of Transportation Routes with Multiple Destinations in Random Networks},
author = {Cristina S Maiz and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5967701},
isbn = {978-1-4577-0569-4},
year  = {2011},
date = {2011-01-01},
booktitle = {2011 IEEE Statistical Signal Processing Workshop (SSP)},
pages = {349--352},
publisher = {IEEE},
address = {Nice},
abstract = {Various practical problems in transportation research and routing in communication networks can be reduced to the computation of the best path that traverses a certain graph and visits a set of D specified destination nodes. Simple versions of this problem have received attention in the literature. Optimal solutions exist for the cases in which (a) D \&gt;; 1 and the graph is deterministic or (b) D = 1 and the graph is stochastic (and possibly time-dependent). Here, we address the general problem in which both D \&gt;; 1 and the costs of the edges in the graph are stochastic and time-varying. We tackle this complex global optimization problem by first converting it into an equivalent estimation problem and then computing a numerical solution using a sequential Monte Carlo algorithm. The advantage of the proposed technique over some standard methods (devised for graphs with time-invariant statistics) is illustrated by way of computer simulations.},
keywords = {Approximation algorithms, communication networks, Estimation, graph theory, Histograms, intelligent transportation, Monte Carlo algorithm, Monte Carlo methods, multiple destinations, optimisation, Optimization, random networks, route optimization, routing, Sequential Monte Carlo, Signal processing algorithms, stochastic graph, Stochastic processes, telecommunication network routing, time-varying graph, transportation routes},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Salamanca2011a,
title = {MAP Decoding for LDPC Codes over the Binary Erasure Channel},
author = {Luis Salamanca and Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6089364},
isbn = {978-1-4577-0437-6},
year  = {2011},
date = {2011-01-01},
booktitle = {2011 IEEE Information Theory Workshop},
pages = {145--149},
publisher = {IEEE},
address = {Paraty},
abstract = {In this paper, we propose a decoding algorithm for LDPC codes that achieves the MAP solution over the BEC. This algorithm, denoted as generalized tree-structured expectation propagation (GTEP), extends the idea of our previous work, the TEP decoder. The GTEP modifies the graph by eliminating a check node of any degree and merging this information with the remaining graph. The GTEP decoder upon completion either provides the unique MAP solution or a tree graph in which the number of parent nodes indicates the multiplicity of the MAP solution. This algorithm can be easily described for the BEC, and it can be cast as a generalized peeling decoder. The GTEP naturally optimizes the complexity of the decoder, by looking for checks nodes of minimum degree to be eliminated first.},
keywords = {binary erasure channel, Channel Coding, computational complexity, Decoding, generalized peeling decoder, generalized tree-structured expectation propagatio, graphical models, Iterative decoding, LDPC codes, MAP decoding, MAP decoding algorithm, Maximum likelihood decoding, parity check codes, TEP decoder, tree graph theory, Tree graphs, tree-structured expectation propagation, trees (mathematics)},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Achutegui2011,
title = {A Parallel Resampling Scheme and its Application to Distributed Particle Filtering in Wireless Networks},
author = {Katrin Achutegui and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6136051},
isbn = {978-1-4577-2105-2},
year  = {2011},
date = {2011-01-01},
booktitle = {2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)},
pages = {81--84},
publisher = {IEEE},
address = {San Juan},
abstract = {We address the design of a particle filter (PF) that can be implemented in a distributed manner over a network of wireless sensor nodes, each of them collecting their own local data. This is a problem that has received considerable attention lately and several methods based on consensus, the transmission of likelihood information, the truncation and/or the quantization of data have been proposed. However, all existing schemes suffer from limitations related either to the amount of required communications among the nodes or the accuracy of the filter outputs. In this work we propose a novel distributed PF that is built around the distributed resampling with non-proportional allocation (DRNA) algorithm. This scheme guarantees the properness of the particle approximations produced by the filter and has been shown to be both efficient and accurate when compared with centralized PFs. The standard DRNA technique, however, places stringent demands on the communications among nodes that turn out impractical for a typical wireless sensor network (WSN). In this paper we investigate how to reduce this communication load by using (i) a random model for the spread of data over the WSN and (ii) methods that enable the out-of-sequence processing of sensor observations. A simple numerical illustration of the performance of the new algorithm compared with a centralized PF is provided.},
keywords = {Approximation algorithms, Approximation methods, Artificial neural networks, distributed resampling, DRNA technique, Markov processes, nonproportional allocation algorithm, parallel resampling scheme, PF, quantization, Signal processing, Vectors, Wireless sensor network, Wireless Sensor Networks, WSN},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Asyhari2011a,
title = {Nearest Neighbour Decoding with Pilot-Assisted Channel Estimation for Fading Multiple-Access Channels},
author = {Taufiq A Asyhari and Tobias Koch and Albert Guillen i Fabregas},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6120371},
isbn = {978-1-4577-1818-2},
year  = {2011},
date = {2011-01-01},
booktitle = {2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton)},
pages = {1686--1693},
publisher = {IEEE},
address = {Allerton},
abstract = {This paper studies a noncoherent multiple-input multiple-output (MIMO) fading multiple-access channel (MAC). The rate region that is achievable with nearest neighbour decoding and pilot-assisted channel estimation is analysed and the corresponding pre-log region, defined as the limiting ratio of the rate region to the logarithm of the signal-to-noise ratio (SNR) as the SNR tends to infinity, is determined.},
keywords = {Channel estimation, Decoding, Fading, fading channels, fading multiple-access channels, MIMO, MIMO communication, multi-access systems, multiple-input multiple-output channel, nearest-neighbour decoding, noncoherent MIMO fading MAC channel, pilot-assisted channel estimation, Receiving antennas, Signal to noise ratio, signal-to-noise ratio, Time division multiple access, Vectors},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Ruiz2011a,
title = {Zero-Error Codes for the Noisy-Typewriter Channel},
author = {Francisco J R Ruiz and Fernando Perez-Cruz},
url = {http://suri.epfl.ch/past/2011},
year  = {2011},
date = {2011-01-01},
booktitle = {Summer Research Institute (SuRi)},
address = {Lausanne},
abstract = {In this paper we propose nontrivial codes that achieve a non-zero zero-error rate for several odd-letter noisy-typewriter channels. Some of these codes (specifically those which are defined for a number of letters of the channel of the form 2^n+1) achieve the best-known lower bound on the zero-error capacity. We build the codes using linear codes over rings as we do not require the multiplicative inverse to build the codes.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Shan2011,
title = {Fast Background Elimination in Fluorescence Microbiology Images: Comparison of Four Algorithms},
author = {Gong Shan and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://link.springer.com/chapter/10.1007/978-3-642-23960-1_34},
year  = {2011},
date = {2011-01-01},
booktitle = {7th Artificial Intelligence Applications and Innovations Conference},
pages = {285 -- 290},
address = {Corf\'{u}},
abstract = {In this work, we investigate a fast background elimination front-end of an automatic bacilli detection system. This background eliminating system consists of a feature descriptor followed by a linear-SVMs classifier. Four state-of-the-art feature extraction algorithms are analyzed and modified. Extensive experiments have been made on real sputum fluorescence images and the results reveal that 96.92% of the background content can be correctly removed from one image with an acceptable computational complexity.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Koblents2011,
title = {A Population Monte Carlo Method for Bayesian Inference and its Application to Stochastic Kinetic Models},
author = {Eugenia Koblents and Joaquin Miguez},
url = {http://www.eurasip.org/Proceedings/Eusipco/Eusipco2011/papers/1569427761.pdf},
year  = {2011},
date = {2011-01-01},
booktitle = {EUSIPCO 2011},
address = {Barcelona},
abstract = {We introduce an extension of the population Monte Carlo (PMC) methodology to address the problem of Bayesian in- ference in high dimensional models. Specifically, we intro- duce a technique for the selection and update of importance functions based on the construction of Gaussian Bayesian networks. The structure of the latter graphical model en- ables a sequential sampling procedure that requires draw- ing only from unidimensional conditional distributions an d leads to very efficient PMC algorithms. In order to illus- trate the potential of the new technique we have consid- ered the estimation of rate parameters in stochastic kineti c models (SKMs). SKMs are multivariate systems that model molecular interactions in biological and chemical problem s. We present some numerical results based on a simple SKM known as predator-prey mode},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Vazquez2011,
title = {A Per-Survivor Processing Receiver for MIMO Transmission Systems With One Unknown Channel Order Per Output},
author = {Manuel A Vazquez and Joaquin Miguez},
url = {http://www.tsc.uc3m.es/~jmiguez/papers/P31_2011_A Per-Survivor Processing Receiver for MIMO Transmission Systems With One Unknown Channel Order Per Output.pdf http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6032763},
issn = {0018-9545},
year  = {2011},
date = {2011-01-01},
journal = {IEEE Transactions on Vehicular Technology},
volume = {60},
number = {9},
pages = {4415--4426},
abstract = {The order of a communications channel is the length of its impulse response. Recently, several works have tackled the problem of estimating the order of a frequency-selective multiple-input-multiple-output (MIMO) channel. However, all of them consider a single order, despite the fact that a MIMO channel comprises several subchannels (specifically, as many as the number of inputs times the number of outputs), each one possibly with its own order. In this paper, we introduce an algorithm for maximum-likelihood sequence detection (MLSD) in frequency- and time-selective MIMO channels that incorporates full estimation of the MIMO channel impulse response (CIR) coefficients, including one channel order per output. Simulation results following the analytical derivation of the algorithm suggest that the proposed receiver can achieve significant improvements in performance when transmitting through a MIMO channel that effectively comprises subchannels of different lengths.},
keywords = {Channel estimation, communication channel, Complexity theory, dynamic programming, frequency-selective MIMO channel, frequency-selective multiple-input multiple-output, maximum likelihood detection, maximum likelihood sequence detection, maximum likelihood sequence estimation, MIMO, MIMO channel impulse response coefficient, MIMO communication, MIMO transmission system, multipath channels, mutiple-input\textendashmultiple-output (MIMO), per-survivor processing receiver, Receiving antennas, Signal processing algorithms, time-selective MIMO channel, Transmitting antennas, Viterbi algorithm},
pubstate = {published},
tppubtype = {article}
}
@article{Olmos2011c,
title = {Tree-Structured Expectation Propagation for Decoding Finite-Length LDPC Codes},
author = {Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5682215},
issn = {1089-7798},
year  = {2011},
date = {2011-01-01},
journal = {IEEE Communications Letters},
volume = {15},
number = {2},
pages = {235--237},
abstract = {In this paper, we propose Tree-structured Expectation Propagation (TEP) algorithm to decode finite-length Low-Density Parity-Check (LDPC) codes. The TEP decoder is able to continue decoding once the standard Belief Propagation (BP) decoder fails, presenting the same computational complexity as the BP decoder. The BP algorithm is dominated by the presence of stopping sets (SSs) in the code graph. We show that the TEP decoder, without previous knowledge of the graph, naturally avoids some fairly common SSs. This results in a significant improvement in the system performance.},
keywords = {belief propagation decoder, BP algorithm, BP decoder, code graph, communication complexity, computational complexity, Decoding, finite-length analysis, finite-length low-density parity-check code, LDPC code, LDPC decoding, parity check codes, radiowave propagation, stopping set, TEP algorithm, TEP decoder, tree-structured expectation propagation},
pubstate = {published},
tppubtype = {article}
}
@article{Asheghan2011,
title = {Robust Outer Synchronization between two Complex Networks with Fractional Order Dynamics},
author = {Mohammad Mostafa Asheghan and Joaquin Miguez and Mohammad Taghi Hamidi-Beheshti and Mohammad Saleh Tavazoei},
url = {http://www.tsc.uc3m.es/~jmiguez/papers/P32_2011_Robust Outer Synchronization between two Complex Networks with Fractional Order Dynamics.pdf 
http://scitation.aip.org/content/aip/journal/chaos/21/3/10.1063/1.3629986},
issn = {1089-7682},
year  = {2011},
date = {2011-01-01},
journal = {Chaos (Woodbury, N.Y.)},
volume = {21},
number = {3},
pages = {033121},
publisher = {AIP Publishing},
abstract = {Synchronization between two coupled complex networks with fractional-order dynamics, hereafter referred to as outer synchronization, is investigated in this work. In particular, we consider two systems consisting of interconnected nodes. The state variables of each node evolve with time according to a set of (possibly nonlinear and chaotic) fractional-order differential equations. One of the networks plays the role of a master system and drives the second network by way of an open-plus-closed-loop (OPCL) scheme. Starting from a simple analysis of the synchronization error and a basic lemma on the eigenvalues of matrices resulting from Kronecker products, we establish various sets of conditions for outer synchronization, i.e., for ensuring that the errors between the state variables of the master and response systems can asymptotically vanish with time. Then, we address the problem of robust outer synchronization, i.e., how to guarantee that the states of the nodes converge to common values when the parameters of the master and response networks are not identical, but present some perturbations. Assuming that these perturbations are bounded, we also find conditions for outer synchronization, this time given in terms of sets of linear matrix inequalities (LMIs). Most of the analytical results in this paper are valid both for fractional-order and integer-order dynamics. The assumptions on the inner (coupling) structure of the networks are mild, involving, at most, symmetry and diffusivity. The analytical results are complemented with numerical examples. In particular, we show examples of generalized and robust outer synchronization for networks whose nodes are governed by fractional-order Lorenz dynamics.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Delgado-Gomez2011a,
title = {Improving Sale Performance Prediction Using Support Vector Machines},
author = {David Delgado-G\'{o}mez and David Aguado and Jorge Lopez-Castroman and Carlos Santacruz and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.tsc.uc3m.es/~antonio/papers/P41_2011_Improving Sale Performance Prediction Using Support Vector Machines.pdf 
http://www.sciencedirect.com/science/article/pii/S0957417410011322},
year  = {2011},
date = {2011-01-01},
journal = {Expert Systems with Applications},
volume = {38},
number = {5},
pages = {5129--5132},
abstract = {In this article, an expert system based on support vector machines is developed to predict the sale performance of some insurance company candidates. The system predicts the performance of these candidates based on some scores, which are measurements of cognitive characteristics, personality, selling skills and biodata. An experiment is conducted to compare the accuracy of the proposed system with respect to previously reported systems which use discriminant functions or decision trees. Results show that the proposed system is able to improve the accuracy of a baseline linear discriminant based system by more than 10% and that also exceeds the state of the art systems by almost 5%. The proposed approach can help to reduce considerably the direct and indirect expenses of the companies.},
keywords = {Recruitment process, Sale performance prediction, Support vector machines},
pubstate = {published},
tppubtype = {article}
}
@article{Delgado-Gomez2011b,
title = {Improving the Accuracy of Suicide Attempter Classification},
author = {David Delgado-G\'{o}mez and Hilario Blasco-Fontecilla and AnaLucia A Alegria and Teresa Legido-Gil and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a},
url = {http://www.sciencedirect.com/science/article/pii/S0933365711000595},
year  = {2011},
date = {2011-01-01},
journal = {Artificial Intelligence in Medicine},
volume = {52},
number = {3},
pages = {165--168},
abstract = {OBJECTIVE Psychometrical questionnaires such as the Barrat’s impulsiveness scale version 11 (BIS-11) have been used in the assessment of suicidal behavior. Traditionally, BIS-11 items have been considered as equally valuable but this might not be true. The main objective of this article is to test the discriminative ability of the BIS-11 and the international personality disorder evaluation screening questionnaire (IPDE-SQ) to predict suicide attempter (SA) status using different classification techniques. In addition, we examine the discriminative capacity of individual items from both scales. MATERIALS AND METHODS Two experiments aimed at evaluating the accuracy of different classification techniques were conducted. The answers of 879 individuals (345 SA, 384 healthy blood donors, and 150 psychiatric inpatients) to the BIS-11 and IPDE-SQ were used to compare the classification performance of two techniques that have successfully been applied in pattern recognition issues, Boosting and support vector machines (SVM) with respect to linear discriminant analysis, Fisher linear discriminant analysis, and the traditional psychometrical approach. RESULTS The most discriminative BIS-11 and IPDE-SQ items are “I am self controlled” (Item 6) and “I often feel empty inside” (item 40), respectively. The SVM classification accuracy was 76.71% for the BIS-11 and 80.26% for the IPDE-SQ. CONCLUSIONS The IPDE-SQ items have better discriminative abilities than the BIS-11 items for classifying SA. Moreover, IPDE-SQ is able to obtain better SA and non-SA classification results than the BIS-11. In addition, SVM outperformed the other classification techniques in both questionnaires.},
keywords = {Barratt’s impulsiveness scale, Boosting, International personality disorder evaluation scre, Suicide prediction, Support vector machines},
pubstate = {published},
tppubtype = {article}
}
@article{Miguez2011,
title = {On the Convergence of Two Sequential Monte Carlo Methods for Maximum a Posteriori Sequence Estimation and Stochastic Global Optimization},
author = {Joaquin Miguez and Dan Crisan and Petar M Djuric},
url = {http://www.researchgate.net/publication/225447686_On_the_convergence_of_two_sequential_Monte_Carlo_methods_for_maximum_a_posteriori_sequence_estimation_and_stochastic_global_optimization},
issn = {0960-3174},
year  = {2011},
date = {2011-01-01},
journal = {Statistics and Computing},
volume = {23},
number = {1},
pages = {91--107},
abstract = {This paper addresses the problem of maximum a posteriori (MAP) sequence estimation in general state-space models. We consider two algorithms based on the sequential Monte Carlo (SMC) methodology (also known as particle filtering). We prove that they produce approximations of the MAP estimator and that they converge almost surely. We also derive a lower bound for the number of particles that are needed to achieve a given approximation accuracy. In the last part of the paper, we investigate the application of particle filtering and MAP estimation to the global optimization of a class of (possibly non-convex and possibly non-differentiable) cost functions. In particular, we show how to convert the cost-minimization problem into one of MAP sequence estimation for a state-space model that is “matched” to the cost of interest. We provide examples that illustrate the application of the methodology as well as numerical results.},
keywords = {Global optimization, MAP sequence estimation, Sequential Monte Carlo, State space models},
pubstate = {published},
tppubtype = {article}
}
@article{Lopez-Castroman2011,
title = {Distinguishing the Relevant Features of Frequent Suicide Attempters},
author = {Jorge Lopez-Castroman and Mercedes M Perez-Rodriguez and Isabelle Jaussent and AnaLucia A Alegria and Antonio Art\'{e}s-Rodr\'{i}guez and Peter Freed and S\'{e}bastien Guillaume and Fabrice Jollant and Jose M Leiva-Murillo and Alain Malafosse and Maria A Oquendo and Mario de Prado-Cumplido and Jeronimo Saiz-Ruiz and Enrique Baca-Garc\'{i}a and Philippe Courtet},
url = {http://www.tsc.uc3m.es/~antonio/papers/P39_2011_Distinguishing the Relevant Features of Frequent Suicide Attempters.pdf 
http://www.ncbi.nlm.nih.gov/pubmed/21055768},
issn = {1879-1379},
year  = {2011},
date = {2011-01-01},
journal = {Journal of psychiatric research},
volume = {45},
number = {5},
pages = {619--625},
abstract = {BACKGROUND: In spite of the high prevalence of suicide behaviours and the magnitude of the resultant burden, little is known about why individuals reattempt. We aim to investigate the relationships between clinical risk factors and the repetition of suicidal attempts. METHODS: 1349 suicide attempters were consecutively recruited in the Emergency Room (ER) of two academic hospitals in France and Spain. Patients were extensively assessed and demographic and clinical data obtained. Data mining was used to determine the minimal number of variables that blinded the rest in relation to the number of suicide attempts. Using this set, a probabilistic graph ranking relationships with the target variable was constructed. RESULTS: The most common diagnoses among suicide attempters were affective disorders, followed by anxiety disorders. Risk of frequent suicide attempt was highest among middle-aged subjects, and diminished progressively with advancing age of onset at first attempt. Anxiety disorders significantly increased the risk of presenting frequent suicide attempts. Pathway analysis also indicated that frequent suicide attempts were linked to greater odds for alcohol and substance abuse disorders and more intensive treatment. CONCLUSIONS: Novel statistical methods found several clinical features that were associated with a history of frequent suicide attempts. The identified pathways may promote new hypothesis-driven studies of suicide attempts and preventive strategies.},
keywords = {Adult, Attempted, Attempted: psychology, Attempted: statistics \&amp;amp;amp; numerical data, Female, France, Humans, Interview, Male, Middle Aged, Prevalence, Probability, Psychiatric Status Rating Scales, Psychological, Risk Factors, ROC Curve, Spain, Suicide},
pubstate = {published},
tppubtype = {article}
}
@article{Tuia2011,
title = {Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation},
author = {D Tuia and J Verrelst and L Alonso and Fernando Perez-Cruz and Gustavo Camps-Valls},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5735189},
issn = {1545-598X},
year  = {2011},
date = {2011-01-01},
journal = {IEEE Geoscience and Remote Sensing Letters},
volume = {8},
number = {4},
pages = {804--808},
abstract = {This letter proposes a multioutput support vector regression (M-SVR) method for the simultaneous estimation of different biophysical parameters from remote sensing images. General retrieval problems require multioutput (and potentially nonlinear) regression methods. M-SVR extends the single-output SVR to multiple outputs maintaining the advantages of a sparse and compact solution by using an $epsilon$-insensitive cost function. The proposed M-SVR is evaluated in the estimation of chlorophyll content, leaf area index and fractional vegetation cover from a hyperspectral compact high-resolution imaging spectrometer images. The achieved improvement with respect to the single-output regression approach suggests that M-SVR can be considered a convenient alternative for nonparametric biophysical parameter estimation and model inversion.},
keywords = {Biological system modeling, Biomedical imaging, Biophysical parameter estimation, chlorophyll content estimation, Estimation, fractional vegetation cover, geophysical image processing, hyperspectral compact high-resolution imaging spec, image resolution, leaf area index, model inversion, multioutput support vector regression method, nonparametric biophysical parameter estimation, Parameter estimation, regression, regression analysis, Remote sensing, remote sensing biophysical parameter estimation, remote sensing image, single-output support vector regression method, spectrometers, Support vector machines, support vector regression (SVR), Vegetation mapping},
pubstate = {published},
tppubtype = {article}
}
@article{Santiago-Mozos2011,
title = {Extended Input Space Support Vector Machine},
author = {Ricardo Santiago-Mozos and Fernando Perez-Cruz and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.tsc.uc3m.es/~antonio/papers/P38_2011_Extended Input Space Support Vector Machine.pdf 
http://www.ncbi.nlm.nih.gov/pubmed/21095866},
issn = {1941-0093},
year  = {2011},
date = {2011-01-01},
journal = {IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council},
volume = {22},
number = {1},
pages = {158--163},
abstract = {In some applications, the probability of error of a given classifier is too high for its practical application, but we are allowed to gather more independent test samples from the same class to reduce the probability of error of the final decision. From the point of view of hypothesis testing, the solution is given by the Neyman-Pearson lemma. However, there is no equivalent result to the Neyman-Pearson lemma when the likelihoods are unknown, and we are given a training dataset. In this brief, we explore two alternatives. First, we combine the soft (probabilistic) outputs of a given classifier to produce a consensus labeling for K test samples. In the second approach, we build a new classifier that directly computes the label for K test samples. For this second approach, we need to define an extended input space training set and incorporate the known symmetries in the classifier. This latter approach gives more accurate results, as it only requires an accurate classification boundary, while the former needs an accurate posterior probability estimate for the whole input space. We illustrate our results with well-known databases.},
keywords = {Algorithms, Artificial Intelligence, Automated, Automated: standards, Computer Simulation, Computer Simulation: standards, Neural Networks (Computer), Pattern recognition, Problem Solving, Software Design, Software Validation},
pubstate = {published},
tppubtype = {article}
}
@article{Plata-Chaves2011b,
title = {Closed-Form Error Exponent for the Neyman\textendashPearson Fusion of Dependent Local Decisions in a One-Dimensional Sensor Network},
author = {Jorge Plata-Chaves and Marcelino Lazaro},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5654602},
issn = {1053-587X},
year  = {2011},
date = {2011-01-01},
journal = {IEEE Transactions on Signal Processing},
volume = {59},
number = {3},
pages = {1239--1254},
abstract = {We consider a distributed detection system formed by a large number of local detectors and a data fusion center that performs a Neyman-Pearson fusion of the binary quantizations of the sensor observations. In the analyzed two-stage detection system the local decisions are taken with no kind of cooperation among the devices and they are transmitted to the fusion center over an error free parallel access channel. In addition, the sensors are randomly deployed along a straight line, and the corresponding sensor spacings are drawn independently from a common probability density function (pdf). For both hypothesis, H0 and H1, depending on the correlation structure of the observed phenomenon the local decisions might be dependent. In the case of being dependent, their correlation structure is modelled with a one-dimensional Markov random field with nearest neighbor dependency and binary state space. Under this scenario, we first derive a closed-form error exponent for the Neyman-Pearson fusion of the local decisions when the involved data fusion center only knows the distribution of the sensor spacings. Second, based on a single parameter that captures the mean correlation strength among the local decisions, some analytical properties of the error exponent are investigated. Finally, we develop a physical model for the conditional probabilities of the Markov random fields that might be present under each hypothesis. Using this model we characterize the error exponent for two well-known models of the sensor spacing: i) equispaced sensors with failures, and ii) exponentially spaced sensors with failures.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Barbosa2011,
title = {Distributed Target Detection in Centralized Wireless Sensor Networks with Communication Constraints},
author = {J L Barbosa and David Luengo},
year  = {2011},
date = {2011-01-01},
booktitle = {19th European Signal Processing Conference (EUSIPCO)},
address = {Barcelona},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Crisan2011,
title = {Particle Aproximation of the Filtering Density and Its Derivates in General State. Space Models},
author = {Dan Crisan and Joaquin Miguez},
year  = {2011},
date = {2011-01-01},
booktitle = {Bayesian Inference and Stochastic Processes (BISP 7)},
address = {Getafe},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Plata-Chaves2011bb,
title = {Optimal Neyman-Pearson Fusion in Two-Dimensional Densor Networks with Serial Architecture and Dependent Observations},
author = {Jorge Plata-Chaves and Marcelino Lazaro and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5977545\&amp;amp;searchWithin%3Dartes+rodriguez%26sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A5977431%29},
isbn = {978-1-4577-0267-9},
year  = {2011},
date = {2011-01-01},
booktitle = {Information Fusion (FUSION), 2011 Proceedings of the 14th International Conference on},
pages = {1--6},
address = {Chicago},
abstract = {In this correspondence, we consider a sensor network with serial architecture. When solving a binary distributed detection problem where the sensor observations are dependent under each one of the two possible hypothesis, each fusion stage of the network applies a local decision rule. We assume that, based on the information available at each fusion stage, the decision rules provide a binary message regarding the presence or absence of an event of interest. Under this scenario and under a Neyman-Pearson formulation, we derive the optimal decision rules associated with each fusion stage. As it happens when the sensor observations are independent, we are able to show that, under the Neyman-Pearson criterion, the optimal fusion rules of a serial configuration with dependent observations also match optimal Neyman-Pearson tests.},
keywords = {Bayesian methods, binary distributed detection problem, decision theory, dependent observations, Joints, local decision rule, Measurement uncertainty, Network topology, Neyman-Pearson criterion, optimal Neyman-Pearson fusion, optimum distributed detection, Parallel architectures, Performance evaluation, Probability density function, sensor dependent observations, sensor fusion, serial architecture, serial network topology, two-dimensional sensor networks, Wireless Sensor Networks},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{5955285,
title = {Doppler radar and MEMS gyro augmented DGPS for large vehicle navigation},
author = {Jussi Parviainen and Martti Kirkko-Jaakkola and Pavel Davidson and Manuel A V\'{a}zquez and Jussi Collin},
doi = {10.1109/ICL-GNSS.2011.5955285},
year  = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
booktitle = {2011 International Conference on Localization and GNSS (ICL-GNSS)},
pages = {140-145},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{twc2010,
title = {Primary User Enters the Game: Performance of Dynamic Spectrum Leasing in Cognitive Radio Networks},
author = {Gonzalo Vazquez-Vilar and Carlos Mosquera and Sudharman K Jayaweera},
doi = {10.1109/TWC.2010.101310.101056},
issn = {1536-1276},
year  = {2010},
date = {2010-12-01},
journal = {IEEE Transactions on Wireless Communications},
volume = {9},
number = {12},
pages = {3625-3629},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{cip2010,
title = {Multiantenna spectrum sensing for Cognitive Radio: overcoming noise uncertainty},
author = {Roberto L\'{o}pez-Valcarce and Gonzalo Vazquez-Vilar and Josep Sala},
year  = {2010},
date = {2010-06-01},
booktitle = {The 2nd International Workshop on Cognitive Information Processing (CIP 2010)},
address = {Elba Island (Tuscany), Italy},
note = {Invited},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{tvt2010,
title = {Dynamic Spectrum Leasing (DSL): A New Paradigm for Spectrum Sharing in Cognitive Radio Networks},
author = {Sudharman K Jayaweera and Gonzalo Vazquez-Vilar and Carlos Mosquera},
doi = {10.1109/TVT.2010.2042741},
issn = {0018-9545},
year  = {2010},
date = {2010-06-01},
journal = {IEEE Transactions on Vehicular Technology},
volume = {59},
number = {5},
pages = {2328-2339},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{crisp2010,
title = {Dynamic Spectrum Leasing (DSL) in Dynamic Channels},
author = {Georges El-Howayek and Sudharman K Jayaweera and Kamrul Hakim and Gonzalo Vazquez-Vilar and Carlos Mosquera},
year  = {2010},
date = {2010-05-01},
booktitle = {ICC'10 Workshop on Cognitive Radio Interfaces and Signal Processing (ICC'10 Workshop CRISP)},
address = {Cape Town, South Africa},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{icassp2010,
title = {Wideband Spectral Estimation from Compressed Measurements Exploiting Spectral a priori Information in Cognitive Radio Systems},
author = {Gonzalo Vazquez-Vilar and Roberto L\'{o}pez-Valcarce and Carlos Mosquera and Nuria Gonz\'{a}lez-Prelcic},
year  = {2010},
date = {2010-03-01},
booktitle = {2010 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010)},
address = {Dallas, U.S.A.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Vazquez2010,
title = {Adaptive MLSD for MIMO Transmission Systems with Unknown Subchannel Orders},
author = {Manuel A Vazquez and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5624335},
issn = {2154-0217},
year  = {2010},
date = {2010-01-01},
booktitle = {2010 7th International Symposium on Wireless Communication Systems},
pages = {451--455},
publisher = {IEEE},
address = {York},
abstract = {In the equalization of frequency-selective multiple-input multiple-output (MIMO) channels it is usually assumed that the length of the channel impulse response (CIR), also referred to as the channel order, is known. However, this is not true in most practical situations and, in order to avoid the serious performance degradation that occurs when the CIR length is underestimated, a channel with "more than enough" taps is usually considered. This very frequently leads to overestimating the channel order, which increases the computational complexity of any maximum likelihood sequence detection (MLSD) algorithm, while degrading its performance at the same time. The problem of estimating a single channel order for a time and frequency selective MIMO channel has recently been tackled. However, this is an idealized approach, since a MIMO channel comprises multiple subchannels (as many as the number of inputs times that of the outputs), each of them possibly with its own order. In this paper, we introduce an algorithm for MLSD that incorporates the full estimation of the MIMO CIR parameters, including one channel order per output. The proposed technique is based on the per survivor processing (PSP) methodology, it admits both blind and semiblind implementations, depending on the availability of pilot data, and it is designed to work with time-selective channels. Besides the analytical derivation of the algorithm, we provide computer simulation results that illustrate the effectiveness of the resulting receiver.},
keywords = {Bit error rate, Channel estimation, channel impulse response, computational complexity, Estimation, frequency-selective multiple-input multiple-output, maximum likelihood sequence detection, maximum likelihood sequence estimation, MIMO, MIMO communication, MIMO transmission systems, multiple subchannels, per survivor processing methodology, pilot data, Receivers, Signal to noise ratio, Time frequency analysis, time selective MIMO channel},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Valera2010,
title = {A Hybrid SS-ToA Wireless Ge- olocation Based on Path Attenuation under Imperfect Path Loss Exponent},
author = {Isabel Valera and B T Sieskul and F Zheng and T Kaiser},
url = {http://www.eurasip.org/Proceedings/Eusipco/Eusipco2010/Contents/papers/1569292415.pdf},
year  = {2010},
date = {2010-01-01},
booktitle = {18th European Signal Processing Conference (EUSIPCO-2010)},
address = {Aalborg},
abstract = {We consider the wireless geolocationusing the time of arrival (ToA) of radio signals in a cellular setting. The main concern in this paper involves the effects of the error knowledge of the path loss exponent (PLE). We derive the asymptotic error performance of the maximum likelihood (ML) estimator un- der the imperfect PLE. We point out that a previous method provides inaccurate performance prediction and then present a new method based on the Taylor series expansion. Numer- ical examples illustrate that the Taylor analysis captures the bias and the error variance of the ML estimator under the im- perfect PLE better than the conventional method. Simulation results also illustrate that in the threshold region, the ML es- timator outperforms the MC estimator even in the presence of the PLE error. However, in the asymptotic region the MC estimator and the ML estimator with the perfect PLE outper- form the ML estimator under the imperfect PLE.},
keywords = {hood estimator, maximum likeli-, Path loss exponent, Time-of-arrival estimation},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Martino2010,
title = {A Rejection Sampling Scheme for Posterior Probability Distributions via the Ratio-of-Uniforms Method},
author = {Luca Martino and Joaquin Miguez},
url = {http://www.academia.edu/2355638/A_rejection_sampling_scheme_for_posterior_probability_distributions_via_the_ratio-of-uniforms_method},
year  = {2010},
date = {2010-01-01},
booktitle = {18th European Signal Processing Conference (EUSIPCO-2010)},
address = {Aalborg},
abstract = {Accept/reject sampling is a well-known method to generaterandom samples from arbitrary target probability distribu-tions. It demands the design of a suitable proposal probabil-ity density function (pdf) from which candidate samples canbe drawn. The main limitation to the use of RS is the needto find an adequate upper bound for the ratio of the targetpdf over the proposal pdf from which the samples are gener-ated. There are no general methods to analytically find thisbound, except when the target pdf is log-concave. In thispaper we introduce a novel procedure using the ratio of uni-forms method to efficiently perform rejection sampling fora large class of target densities. The candidate samples aregenerated using only two independent uniform random vari-ables. In order to illustrate the application of the proposedtechnique, we provide a numerical example},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Martinez-Ruiz2010,
title = {New Initiatives for Imagery Transmission over a Tactical Data Link. A Case Study: JPEG2000 Compressed Images Transmitted in a Link-16 Network. Method and Results},
author = {Manuel Martinez Ruiz and Antonio Art\'{e}s-Rodr\'{i}guez and Jose Antonio Diaz-Rico and Jose Blanco Fuentes},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5680102},
issn = {2155-7578},
year  = {2010},
date = {2010-01-01},
booktitle = {2010 - MILCOM 2010 MILITARY COMMUNICATIONS CONFERENCE},
pages = {1163--1168},
publisher = {IEEE},
address = {San Jose},
abstract = {This paper presents the results of an initiative to transmit imagery content through a Link-16 tactical network using a multirresolution approach based on wavelets to compress images. Firstly, we identify the operational requirements. Secondly, we justify why JPEG2000 is our choice for coding still images. Thirdly, we propose a method to map the JPEG2000 code-stream into Link-16 free-text messages. We propose to send the most important part of the JPEG2000 compressed image in a more error resistant Link-16 packed structure and the remaining of the image in less robust data structures but at higher data rates. Finally, we present our results based on software simulations and laboratory tests with real Link-16 terminals including a comparative analysis with Link-16 enhance throughput. A configuration using two MIDS-LVTs has being set up, along with JPEG2000 coding and decoding software tools.},
keywords = {Bit rate, code stream, data stream, Decoding, discrete wavelet transforms, Image coding, image compression, imagery transmission, JPEG-2000, JPEG2000 compressed images, link-16, Link-16 Enhance Throughput, Link-16 tactical network, MIDS-LVT, military communication, multirresolution, operational requirement, packing limit, PSNR, Security, Streaming media, tactical data link, time slot, Transform coding, wavelet discrete transforms, wavelets},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Koch2010,
title = {Increased Capacity per Unit-Cost by Oversampling},
author = {Tobias Koch and Amos Lapidoth},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5662127},
isbn = {978-1-4244-8681-6},
year  = {2010},
date = {2010-01-01},
booktitle = {2010 IEEE 26-th Convention of Electrical and Electronics Engineers in Israel},
pages = {000684--000688},
publisher = {IEEE},
address = {Eliat},
abstract = {It is demonstrated that doubling the sampling rate recovers some of the loss in capacity incurred on the bandlimited Gaussian channel with a one-bit output quantizer.},
keywords = {AWGN, AWGN channels, bandlimited Gaussian channel, channel capacity, Gaussian channels, increased capacity per unit cost, Information rates, one bit output quantizer, oversampling, quantisation (signal), quantization, sampling rate recovery, signal sampling},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Murillo-Fuentes2010,
title = {Analyzing the Maxwell Decoder for LDPC Codes in Binary Erasure Channels},
author = {Juan Jose Murillo-Fuentes and Pablo M Olmos and Fernando Perez-Cruz},
url = {http://ita.ucsd.edu/workshop/10/files/abstract/abstract_1462.txt},
year  = {2010},
date = {2010-01-01},
booktitle = {Information Theory and Applications (ITA)},
address = {San Diego},
abstract = {The Maxwell decoder has been proposed for bridging the gap between the achievable capacity by belief propagation decoding and the maximum a posteriori decoder in binary erasure channels of LDPC codes. The Maxwell decoder, once the belief-propagation decoder gets stuck in a nonempty stopping set, guesses a bit and replicates any running copies of the decoding process. Density evolution and EXIT chart analyses of this iterative decoder show that MAP performance can be derived from the performance of the BP decoder. The complexity of the Maxwell decoder depends exponentially on the number of guesses and a priori we cannot bound the number of guesses, which limits its applicability as a LDPC decoder. In this paper, we adapt the expectation propagation algorithm for LDPC decoding. Our algorithm can be understood as a Maxwell decoder with a bounded complexity. For unbounded complexity it achieves maximum a posteriori decoding. In this paper, we analyze in detail the simplest version of the algorithm, whose complexity is identical to belief propagation, and we demonstrate that the achieved capacity is higher than that of the belief propagation decoder.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Olmos2010,
title = {Tree-Structure Expectation Propagation for Decoding LDPC Codes over Binary Erasure Channels},
author = {Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5513636},
isbn = {978-1-4244-7892-7},
year  = {2010},
date = {2010-01-01},
booktitle = {2010 IEEE International Symposium on Information Theory},
pages = {799--803},
publisher = {IEEE},
address = {Austin, TX},
abstract = {Expectation Propagation is a generalization to Belief Propagation (BP) in two ways. First, it can be used with any exponential family distribution over the cliques in the graph. Second, it can impose additional constraints on the marginal distributions. We use this second property to impose pair-wise marginal distribution constraints in some check nodes of the LDPC Tanner graph. These additional constraints allow decoding the received codeword when the BP decoder gets stuck. In this paper, we first present the new decoding algorithm, whose complexity is identical to the BP decoder, and we then prove that it is able to decode codewords with a larger fraction of erasures, as the block size tends to infinity. The proposed algorithm can be also understood as a simplification of the Maxwell decoder, but without its computational complexity. We also illustrate that the new algorithm outperforms the BP decoder for finite block-size codes.},
keywords = {belief propagation, binary erasure channels, Bipartite graph, BP decoder, Capacity planning, Channel Coding, codeword, computational complexity, Decoding, Finishing, graph theory, H infinity control, LDPC code decoding, LDPC Tanner graph, Maxwell decoder, parity check codes, Performance analysis, tree structure expectation propagation, trees (mathematics), Upper bound},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Djuric2010,
title = {Evaluation of a Method's Robustness},
author = {Petar M Djuric and Pau Closas and Monica F Bugallo and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5495921},
issn = {1520-6149},
year  = {2010},
date = {2010-01-01},
booktitle = {2010 IEEE International Conference on Acoustics, Speech and Signal Processing},
pages = {3598--3601},
publisher = {IEEE},
address = {Dallas},
abstract = {In signal processing, it is typical to develop or use a method based on a given model. In practice, however, we almost never know the actual model and we hope that the assumed model is in the neighborhood of the true one. If deviations exist, the method may be more or less sensitive to them. Therefore, it is important to know more about this sensitivity, or in other words, how robust the method is to model deviations. To that end, it is useful to have a metric that can quantify the robustness of the method. In this paper we propose a procedure for developing a variety of metrics for measuring robustness. They are based on a discrete random variable that is generated from observed data and data generated according to past data and the adopted model. This random variable is uniform if the model is correct. When the model deviates from the true one, the distribution of the random variable deviates from the uniform distribution. One can then employ measures for differences between distributions in order to quantify robustness. In this paper we describe the proposed methodology and demonstrate it with simulated data.},
keywords = {Electronic mail, Extraterrestrial measurements, Filtering, Gaussian processes, method's robustness, Random variables, robustness, sequential methods, Signal processing, statistical distributions, Telecommunications, uniform distribution, Wireless communication},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Salamanca2010,
title = {Bayesian BCJR for Channel Equalization and Decoding},
author = {Luis Salamanca and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5589201},
issn = {1551-2541},
year  = {2010},
date = {2010-01-01},
booktitle = {2010 IEEE International Workshop on Machine Learning for Signal Processing},
pages = {53--58},
publisher = {IEEE},
address = {Kittila},
abstract = {In this paper we focus on the probabilistic channel equalization in digital communications. We face the single input single output (SISO) model to show how the statistical information about the multipath channel can be exploited to further improve our estimation of the a posteriori probabilities (APP) during the equalization process. We consider not only the uncertainty due to the noise in the channel, but also in the estimate of the channel estate information (CSI). Thus, we resort to a Bayesian approach for the computation of the APP. This novel algorithm has the same complexity as the BCJR, exhibiting lower bit error rate at the output of the channel decoder than the standard BCJR that considers maximum likelihood (ML) to estimate the CSI.},
keywords = {a posteriori probability, Bayes methods, Bayesian BCJR, Bayesian methods, Bit error rate, channel decoding, channel estate information, Channel estimation, Decoding, digital communication, digital communications, equalisers, Equalizers, error statistics, Markov processes, Maximum likelihood decoding, maximum likelihood estimation, multipath channel, probabilistic channel equalization, Probability, single input single output model, SISO model, statistical information, Training},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Vinuelas-Peris2010,
title = {Bayesian Joint Recovery of Correlated Signals in Distributed Compressed Sensing},
author = {Pablo Vinuelas-Peris and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5604103},
isbn = {978-1-4244-6459-3},
year  = {2010},
date = {2010-01-01},
booktitle = {2010 2nd International Workshop on Cognitive Information Processing},
pages = {382--387},
publisher = {IEEE},
address = {Elba},
abstract = {In this paper we address the problem of Distributed Compressed Sensing (DCS) of correlated signals. We model the correlation using the sparse components correlation coefficient of signals, a general and simple measure. We develop an sparse Bayesian learning method for this setting, that can be applied to both random and optimized projection matrices. As a result, we obtain a reduction of the number of measurements needed for a given recovery error that is dependent on the correlation coefficient, as shown by computer simulations in different scenarios.},
keywords = {Bayes methods, Bayesian joint recovery, Bayesian methods, correlated signal, Correlation, correlation methods, Covariance matrix, Dictionaries, distributed compressed sensing, matrix decomposition, Noise measurement, sensors, sparse component correlation coefficient},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Achutegui2010,
title = {A Model-Switching Sequential Monte Carlo Algorithm for Indoor Tracking with Experimental RSS Data},
author = {Katrin Achutegui and Javier Rodas and Carlos J Escudero and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5648053},
isbn = {978-1-4244-5862-2},
year  = {2010},
date = {2010-01-01},
booktitle = {2010 International Conference on Indoor Positioning and Indoor Navigation},
pages = {1--8},
publisher = {IEEE},
address = {Zurich},
abstract = {In this paper we address the problem of indoor tracking using received signal strength (RSS) as position-dependent data. This type of measurements are very appealing because they can be easily obtained with a variety of (inexpensive) wireless technologies. However, the extraction of accurate location information from RSS in indoor scenarios is not an easy task. Due to the multipath propagation, it is hard to adequately model the correspondence between the received power and the transmitter-to-receiver distance. For that reason, we propose the use of a compound model that combines several sub-models, whose parameters are adjusted to different propagation environments. This methodology, called Interacting Multiple Models (IMM), has been used in the past either for modeling the motion of maneuvering targets or the relationship between the target position and the observations. Here, we extend its application to handle both types of uncertainty simultaneously and we refer to the resulting state-space model as a generalized IMM (GIMM) system. The flexibility of the GIMM approach is attained at the expense of an increase in the number of random processes that must be accurately tracked. To overcome this difficulty, we introduce a Rao-Blackwellized sequential Monte Carlo tracking algorithm that exhibits good performance both with synthetic and experimental data.},
keywords = {Approximation methods, Computational modeling, Data models, generalized IMM system, GIMM approach, indoor radio, Indoor tracking, Kalman filters, maneuvering target motion, Mathematical model, model switching sequential Monte Carlo algorithm, Monte Carlo methods, multipath propagation, multiple model interaction, propagation environment, radio receivers, radio tracking, radio transmitters, random processes, Rao-Blackwellized sequential Monte Carlo tracking, received signal strength, RSS data, sensors, state space model, target position dependent data, transmitter-to-receiver distance, wireless technology},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Helander2010,
title = {Maximum a Posteriori Voice Conversion Using Sequential Monte Carlo Methods},
author = {E Helander and H Sil\'{e}n and Joaquin Miguez and M Gabbouj},
url = {http://www.isca-speech.org/archive/interspeech_2010/i10_1716.html},
year  = {2010},
date = {2010-01-01},
booktitle = {Eleventh Annual Conference of the International Speech Communication Association (INTERSPEECH)},
address = {Makuhari, Chiba, Japan},
abstract = {Many voice conversion algorithms are based on frame-wise mapping from source features into target features. This ignores the inherent temporal continuity that is present in speech and can degrade the subjective quality. In this paper, we propose to optimize the speech feature sequence after a frame-based conversion algorithm has been applied. In particular, we select the sequence of speech features through the minimization of a cost function that involves both the conversion error and the smoothness of the sequence. The estimation problem is solved using sequential Monte Carlo methods. Both subjective and objective results show the effectiveness of the method.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Salamanca2010a,
title = {Channel Decoding with a Bayesian Equalizer},
author = {Luis Salamanca and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5513348},
isbn = {978-1-4244-7892-7},
year  = {2010},
date = {2010-01-01},
booktitle = {2010 IEEE International Symposium on Information Theory},
pages = {1998--2002},
publisher = {IEEE},
address = {Austin, TX},
abstract = {Low-density parity-check (LPDC) decoders assume the channel estate information (CSI) is known and they have the true a posteriori probability (APP) for each transmitted bit. But in most cases of interest, the CSI needs to be estimated with the help of a short training sequence and the LDPC decoder has to decode the received word using faulty APP estimates. In this paper, we study the uncertainty in the CSI estimate and how it affects the bit error rate (BER) output by the LDPC decoder. To improve these APP estimates, we propose a Bayesian equalizer that takes into consideration not only the uncertainty due to the noise in the channel, but also the uncertainty in the CSI estimate, reducing the BER after the LDPC decoder.},
keywords = {a posteriori probability, Bayesian equalizer, Bayesian methods, BER, Bit error rate, Channel Coding, channel decoding, channel estate information, Communication channels, Decoding, equalisers, Equalizers, error statistics, low-density parity-check decoders, LPDC decoders, Maximum likelihood decoding, maximum likelihood detection, maximum likelihood estimation, Noise reduction, parity check codes, Probability, Uncertainty},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Perez-Cruz2010,
title = {Robust and Low Complexity Distributed Kernel Least Squares Learning in Sensor Networks},
author = {Fernando Perez-Cruz and S R Kulkarni},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5395679},
issn = {1070-9908},
year  = {2010},
date = {2010-01-01},
journal = {IEEE Signal Processing Letters},
volume = {17},
number = {4},
pages = {355--358},
abstract = {We present a novel mechanism for consensus building in sensor networks. The proposed algorithm has three main properties that make it suitable for sensor network learning. First, the proposed algorithm is based on robust nonparametric statistics and thereby needs little prior knowledge about the network and the function that needs to be estimated. Second, the algorithm uses only local information about the network and it communicates only with nearby sensors. Third, the algorithm is completely asynchronous and robust. It does not need to coordinate the sensors to estimate the underlying function and it is not affected if other sensors in the network stop working. Therefore, the proposed algorithm is an ideal candidate for sensor networks deployed in remote and inaccessible areas, which might need to change their objective once they have been set up.},
keywords = {communication complexity, Consensus, distributed learning, kernel methods, learning (artificial intelligence), low complexity distributed kernel least squares le, message passing, message-passing algorithms, robust nonparametric statistics, sensor network learning, sensor networks, telecommunication computing, Wireless Sensor Networks},
pubstate = {published},
tppubtype = {article}
}
@article{Carballo2010,
title = {Stability of Childhood Anxiety Disorder Diagnoses: a Follow-Up Naturalistic Study in Psychiatric Care},
author = {Juan J Carballo and Enrique Baca-Garc\'{i}a and Carlos Blanco and Mercedes M Perez-Rodriguez and Miguel A Jimenez-Arriero and Antonio Art\'{e}s-Rodr\'{i}guez and Moira Rynn and David Shaffer and Maria A Oquendo},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19826859},
issn = {1435-165X},
year  = {2010},
date = {2010-01-01},
journal = {European child \&amp;amp; adolescent psychiatry},
volume = {19},
number = {4},
pages = {395--403},
abstract = {Few studies have examined the stability of major psychiatric disorders in pediatric psychiatric clinical populations. The objective of this study was to examine the long-term stability of anxiety diagnoses starting with pre-school age children through adolescence evaluated at multiple time points. Prospective cohort study was conducted of all children and adolescents receiving psychiatric care at all pediatric psychiatric clinics belonging to two catchment areas in Madrid, Spain, between 1 January, 1992 and 30 April, 2006. Patients were selected from among 24,163 children and adolescents who received psychiatric care. Patients had to have a diagnosis of an ICD-10 anxiety disorder during at least one of the consultations and had to have received psychiatric care for the anxiety disorder. We grouped anxiety disorder diagnoses according to the following categories: phobic disorders, social anxiety disorders, obsessive-compulsive disorder (OCD), stress-related disorders, and \"{o}ther" anxiety disorders which, among others, included generalized anxiety disorder, and panic disorder. Complementary indices of diagnostic stability were calculated. As much as 1,869 subjects were included and had 27,945 psychiatric/psychological consultations. The stability of all ICD-10 anxiety disorder categories studied was high regardless of the measure of diagnostic stability used. Phobic and social anxiety disorders showed the highest diagnostic stability, whereas OCD and \"{o}ther" anxiety disorders showed the lowest diagnostic stability. No significant sex differences were observed on the diagnostic stability of the anxiety disorder categories studied. Diagnostic stability measures for phobic, social anxiety, and \"{o}ther" anxiety disorder diagnoses varied depending on the age at first evaluation. In this clinical pediatric outpatient sample it appears that phobic, social anxiety, and stress-related disorder diagnoses in children and adolescents treated in community outpatient services may have high diagnostic stability.},
keywords = {Adolescent, Ambulatory Care, Ambulatory Care: utilization, Anxiety Disorders, Anxiety Disorders: diagnosis, Anxiety Disorders: epidemiology, Catchment Area (Health), Child, Cohort Studies, Female, Follow-Up Studies, Humans, International Classification of Diseases, Male, Mental Health Services, Mental Health Services: utilization, Preschool, Prospective Studies, Severity of Illness Index, Spain, Spain: epidemiology},
pubstate = {published},
tppubtype = {article}
}
@article{Delgado-Gomez2010,
title = {Individual Identification Using Personality Traits},
author = {David Delgado-G\'{o}mez and Federico Sukno and David Aguado and Carlos Santacruz and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.sciencedirect.com/science/article/pii/S1084804509001453},
issn = {10848045},
year  = {2010},
date = {2010-01-01},
journal = {Journal of Network and Computer Applications},
volume = {33},
number = {3},
pages = {293--299},
abstract = {In this article, a pioneer study is conducted to evaluate the possibility of identifying people through their personality traits. The study is conducted using the answers of a population of 734 individuals to a collection of 206 items. These items aim at measuring five common different personality traits usually called the big five. These five levels are neuroticism, extraversion, agreeableness, conscientiousness and openness. The traits are estimated using the widely used Samejima's model and then used to discriminate the individuals. Results point biometrics using personality traits as a new promising biometric modality.},
keywords = {Biometrics, Personality traits, Psychometrics, Samejima's model},
pubstate = {published},
tppubtype = {article}
}
@article{Koch2010a,
title = {Gaussian Fading Is the Worst Fading},
author = {Tobias Koch and Amos Lapidoth},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5429105},
issn = {0018-9448},
year  = {2010},
date = {2010-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {56},
number = {3},
pages = {1158--1165},
abstract = {The capacity of peak-power limited, single-antenna, noncoherent, flat-fading channels with memory is considered. The emphasis is on the capacity pre-log, i.e., on the limiting ratio of channel capacity to the logarithm of the signal-to-noise ratio (SNR), as the SNR tends to infinity. It is shown that, among all stationary and ergodic fading processes of a given spectral distribution function and whose law has no mass point at zero, the Gaussian process gives rise to the smallest pre-log. The assumption that the law of the fading process has no mass point at zero is essential in the sense that there exist stationary and ergodic fading processes whose law has a mass point at zero and that give rise to a smaller pre-log than the Gaussian process of equal spectral distribution function. An extension of these results to multiple-input single-output (MISO) fading channels with memory is also presented.},
keywords = {Additive noise, channel capacity, channels with memory, Distribution functions, ergodic fading processes, Fading, fading channels, flat fading, flat-fading channel capacity, Gaussian channels, Gaussian fading, Gaussian processes, H infinity control, high signal-to-noise ratio (SNR), Information technology, information theory, multiple-input single-output fading channels, multiplexing gain, noncoherent, noncoherent channel capacity, peak-power limited channel capacity, Signal to noise ratio, signal-to-noise ratio, single-antenna channel capacity, spectral distribution function, time-selective, Transmitters},
pubstate = {published},
tppubtype = {article}
}
@article{Martino2010a,
title = {Generalized Rejection Sampling Schemes and Applications in Signal Processing},
author = {Luca Martino and Joaquin Miguez},
url = {http://www.sciencedirect.com/science/article/pii/S0165168410001866},
year  = {2010},
date = {2010-01-01},
journal = {Signal Processing},
volume = {90},
number = {11},
pages = {2981--2995},
abstract = {Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques, such as Markov chain Monte Carlo (MCMC) and particle filters, have become very popular in signal processing over the last years. However, in many problems of practical interest these techniques demand procedures for sampling from probability distributions with non-standard forms, hence we are often brought back to the consideration of fundamental simulation algorithms, such as rejection sampling (RS). Unfortunately, the use of RS techniques demands the calculation of tight upper bounds for the ratio of the target probability density function (pdf) over the proposal density from which candidate samples are drawn. Except for the class of log-concave target pdf's, for which an efficient algorithm exists, there are no general methods to analytically determine this bound, which has to be derived from scratch for each specific case. In this paper, we introduce new schemes for (a) obtaining upper bounds for likelihood functions and (b) adaptively computing proposal densities that approximate the target pdf closely. The former class of methods provides the tools to easily sample from a posteriori probability distributions (that appear very often in signal processing problems) by drawing candidates from the prior distribution. However, they are even more useful when they are exploited to derive the generalized adaptive RS (GARS) algorithm introduced in the second part of the paper. The proposed GARS method yields a sequence of proposal densities that converge towards the target pdf and enable a very efficient sampling of a broad class of probability distributions, possibly with multiple modes and non-standard forms. We provide some simple numerical examples to illustrate the use of the proposed techniques, including an example of target localization using range measurements, often encountered in sensor network applications.},
keywords = {Adaptive rejection sampling, Gibbs sampling, Monte Carlo integration, Rejection sampling, sensor networks, Target localization},
pubstate = {published},
tppubtype = {article}
}
@article{Djuric2010a,
title = {Assessment of Nonlinear Dynamic Models by Kolmogorov\textendashSmirnov Statistics},
author = {Petar M Djuric and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5491124},
issn = {1053-587X},
year  = {2010},
date = {2010-01-01},
journal = {IEEE Transactions on Signal Processing},
volume = {58},
number = {10},
pages = {5069--5079},
abstract = {Model assessment is a fundamental problem in science and engineering and it addresses the question of the validity of a model in the light of empirical evidence. In this paper, we propose a method for the assessment of dynamic nonlinear models based on empirical and predictive cumulative distributions of data and the Kolmogorov-Smirnov statistics. The technique is based on the generation of discrete random variables that come from a known discrete distribution if the entertained model is correct. We provide simulation examples that demonstrate the performance of the proposed method.},
keywords = {Cumulative distributions, discrete random variables, dynamic nonlinear models, Electrical capacitance tomography, Filtering, filtering theory, Iron, Kolmogorov-Smirnov statistics, Kolomogorov\textendashSmirnov statistics, model assessment, nonlinear dynamic models, nonlinear dynamical systems, Permission, predictive cumulative distributions, predictive distributions, Predictive models, Random variables, Robots, statistical analysis, statistical distributions, statistics, Telecommunication control},
pubstate = {published},
tppubtype = {article}
}
@article{Perez-Cruz2010a,
title = {MIMO Gaussian Channels With Arbitrary Inputs: Optimal Precoding and Power Allocation},
author = {Fernando Perez-Cruz and Miguel R D Rodrigues and Sergio Verdu},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5429131},
issn = {0018-9448},
year  = {2010},
date = {2010-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {56},
number = {3},
pages = {1070--1084},
abstract = {In this paper, we investigate the linear precoding and power allocation policies that maximize the mutual information for general multiple-input-multiple-output (MIMO) Gaussian channels with arbitrary input distributions, by capitalizing on the relationship between mutual information and minimum mean-square error (MMSE). The optimal linear precoder satisfies a fixed-point equation as a function of the channel and the input constellation. For non-Gaussian inputs, a nondiagonal precoding matrix in general increases the information transmission rate, even for parallel noninteracting channels. Whenever precoding is precluded, the optimal power allocation policy also satisfies a fixed-point equation; we put forth a generalization of the mercury/waterfilling algorithm, previously proposed for parallel noninterfering channels, in which the mercury level accounts not only for the non-Gaussian input distributions, but also for the interference among inputs.},
keywords = {Collaborative work, Equations, fixed-point equation, Gaussian channels, Gaussian noise channels, Gaussian processes, Government, Interference, linear precoding, matrix algebra, mean square error methods, mercury-waterfilling algorithm, MIMO, MIMO communication, MIMO Gaussian channel, minimum mean-square error, minimum mean-square error (MMSE), multiple-input-multiple-output channel, multiple-input\textendashmultiple-output (MIMO) systems, Mutual information, nondiagonal precoding matrix, optimal linear precoder, optimal power allocation policy, optimal precoding, optimum power allocation, Phase shift keying, precoding, Quadrature amplitude modulation, Telecommunications, waterfilling},
pubstate = {published},
tppubtype = {article}
}
@article{Olmos2010a,
title = {Joint Nonlinear Channel Equalization and Soft LDPC Decoding with Gaussian Processes},
author = {Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5290078},
issn = {1053-587X},
year  = {2010},
date = {2010-01-01},
journal = {IEEE Transactions on Signal Processing},
volume = {58},
number = {3},
pages = {1183--1192},
abstract = {In this paper, we introduce a new approach for nonlinear equalization based on Gaussian processes for classification (GPC). We propose to measure the performance of this equalizer after a low-density parity-check channel decoder has detected the received sequence. Typically, most channel equalizers concentrate on reducing the bit error rate, instead of providing accurate posterior probability estimates. We show that the accuracy of these estimates is essential for optimal performance of the channel decoder and that the error rate output by the equalizer might be irrelevant to understand the performance of the overall communication receiver. In this sense, GPC is a Bayesian nonlinear classification tool that provides accurate posterior probability estimates with short training sequences. In the experimental section, we compare the proposed GPC-based equalizer with state-of-the-art solutions to illustrate its improved performance.},
keywords = {Bayesian nonlinear classification tool, Bit error rate, Channel Coding, channel equalizers, Channel estimation, Coding, equalisers, equalization, error statistics, Gaussian processes, GPC, joint nonlinear channel equalization, low-density parity-check (LDPC), low-density parity-check channel decoder, Machine learning, nonlinear channel, nonlinear codes, parity check codes, posterior probability estimates, soft LDPC decoding, soft-decoding, support vector machine (SVM)},
pubstate = {published},
tppubtype = {article}
}
@article{Koch2010b,
title = {On Multipath Fading Channels at High SNR},
author = {Tobias Koch and Amos Lapidoth},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5625630},
issn = {0018-9448},
year  = {2010},
date = {2010-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {56},
number = {12},
pages = {5945--5957},
abstract = {A noncoherent multipath fading channel is considered, where neither the transmitter nor the receiver is cognizant of the realization of the path gains, but both are cognizant of their statistics. It is shown that if the delay spread is large in the sense that the variances of the path gains decay exponentially or slower, then capacity is bounded in the signal-to-noise ratio (SNR). For such channels, capacity does not tend to infinity as the SNR tends to infinity. In contrast, if the variances of the path gains decay faster than exponentially, then capacity is unbounded in the SNR. It is further demonstrated that if the number of paths is finite, then at high SNR capacity grows double-logarithmically with the SNR, and the capacity pre-loglog-defined as the limiting ratio of capacity to loglog(SNR) as the SNR tends to infinity-is 1 irrespective of the number of paths. The results demonstrate that at high SNR multipath fading channels with an infinite number of paths cannot be approximated by multipath fading channels with only a finite number of paths. The number of paths that are needed to approximate a multipath fading channel typically depends on the SNR and may grow to infinity as the SNR tends to infinity.},
keywords = {approximation theory, capacity pre-loglog, capacity to loglog, channel capacity, channels with memory, Delay, Fading, fading channels, frequency-selective fading, high signal-to-noise ratio, high SNR, Limiting, multipath, multipath channels, noncoherent, noncoherent multipath fading channel, Receivers, Signal to noise ratio, signal-to-noise ratio, Transmitters},
pubstate = {published},
tppubtype = {article}
}
@article{Fresia2010,
title = {Joint Source and Channel Coding},
author = {Maria Fresia and Fernando Perez-Cruz and Vincent H Poor and Sergio Verdu},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5563107},
issn = {1053-5888},
year  = {2010},
date = {2010-01-01},
journal = {IEEE Signal Processing Magazine},
volume = {27},
number = {6},
pages = {104--113},
abstract = {The objectives of this article are two-fold: First, to present the problem of joint source and channel (JSC) coding from a graphical model perspective and second, to propose a structure that uses a new graphical model for jointly encoding and decoding a redundant source. In the first part of the article, relevant contributions to JSC coding, ranging from the Slepian-Wolf problem to joint decoding of variable length codes with state-of-the-art source codes, are reviewed and summarized. In the second part, a double low-density parity-check (LDPC) code for JSC coding is proposed. The double LDPC code can be decoded as a single bipartite graph using standard belief propagation (BP) and its limiting performance is analyzed by using extrinsic information transfer (EXIT) chart approximations.},
keywords = {belief propagation, Channel Coding, combined source-channel coding, Decoding, Encoding, graphical model, Hidden Markov models, Iterative decoding, joint source channel coding, JSC coding, LDPC code, low density parity check code, Markov processes, parity check codes, Slepian-Wolf problem, variable length codes},
pubstate = {published},
tppubtype = {article}
}
@article{Martino2010b,
title = {A Generalization of the Adaptive Rejection Sampling Algorithm},
author = {Luca Martino and Joaquin Miguez},
url = {http://link.springer.com/10.1007/s11222-010-9197-9},
issn = {0960-3174},
year  = {2010},
date = {2010-01-01},
journal = {Statistics and Computing},
volume = {21},
number = {4},
pages = {633--647},
abstract = {Rejection sampling is a well-known method to generate random samples from arbitrary target probability distributions. It demands the design of a suitable proposal probability density function (pdf) from which candidate samples can be drawn. These samples are either accepted or rejected depending on a test involving the ratio of the target and proposal densities. The adaptive rejection sampling method is an efficient algorithm to sample from a log-concave target density, that attains high acceptance rates by improving the proposal density whenever a sample is rejected. In this paper we introduce a generalized adaptive rejection sampling procedure that can be applied with a broad class of target probability distributions, possibly non-log-concave and exhibiting multiple modes. The proposed technique yields a sequence of proposal densities that converge toward the target pdf, thus achieving very high acceptance rates. We provide a simple numerical example to illustrate the basic use of the proposed technique, together with a more elaborate positioning application using real data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Zoubir2010,
title = {Analysis of a Sequential Monte Carlo Method for Optimization in Dynamical Systems},
author = {A Zoubir and M Viberg and B Yang and Joaquin Miguez},
url = {http://www.sciencedirect.com/science/article/pii/S0165168409004708},
year  = {2010},
date = {2010-01-01},
journal = {Signal Processing},
volume = {90},
number = {5},
pages = {1609--1622},
abstract = {We investigate a recently proposed sequential Monte Carlo methodology for recursively tracking the minima of a cost function that evolves with time. These methods, subsequently referred to as sequential Monte Carlo minimization (SMCM) procedures, have an algorithmic structure similar to particle filters: they involve the generation of random paths in the space of the signal of interest (SoI), the stochastic selection of the fittest paths and the ranking of the survivors according to their cost. In this paper, we propose an extension of the original SMCM methodology (that makes it applicable to a broader class of cost functions) and introduce an asymptotic-convergence analysis. Our analytical results are based on simple induction arguments and show how the SoI-estimates computed by a SMCM algorithm converge, in probability, to a sequence of minimizers of the cost function. We illustrate these results by means of two computer simulation examples.},
keywords = {Dynamic optimization, Nonlinear dynamics, Nonlinear tracking, Sequential Monte Carlo, Stochastic optimization},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Alvarez2010,
title = {Efficient Multioutput Gaussian Processes Through Variational Inducing Kernels},
author = {Mauricio Alvarez and David Luengo and Michalis Titsias and Neil D Lawrence},
url = {http://eprints.pascal-network.org/archive/00006397/},
year  = {2010},
date = {2010-01-01},
booktitle = {AISTATS 2010},
address = {Sardinia},
abstract = {Interest in multioutput kernel methods is increasing, whether under the guise of multitask learning, multisensor networks or structured output data. From the Gaussian process perspective a multioutput Mercer kernel is a covariance function over correlated output functions. One way of constructing such kernels is based on convolution processes (CP). A key problem for this approach is efficient inference. Alvarez and Lawrence recently presented a sparse approximation for CPs that enabled efficient inference. In this paper, we extend this work in two directions: we introduce the concept of variational inducing functions to handle potential non-smooth functions involved in the kernel CP construction and we consider an alternative approach to approximate inference based on variational methods, extending the work by Titsias (2009) to the multiple output case. We demonstrate our approaches on prediction of school marks, compiler performance and financial time series.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Plata-Chaves2010,
title = {Closed-Form Error Exponent for the Neyman-Pearson Fusion of Two-Dimensional Markov Local Decisions},
author = {Jorge Plata-Chaves and Marcelino Lazaro},
url = {http://www.eurasip.org/Proceedings/Eusipco/Eusipco2010/Contents/papers/1569292447.pdf},
year  = {2010},
date = {2010-01-01},
booktitle = {European Signal Processing Conference (EUSIPCO 2010)},
address = {Aalborg},
abstract = {We consider a distributed detection system formed by a large num- ber of local detectors and a fusion center that performs a Neyman- Pearson fusion of the binary quantizations of the sensor observa- tions. The aforementioned local decisions are taken with no kind of cooperation and transmitted to the fusion center over error free parallel access channels. Furthermore, the devices are located on a rectangular lattice so that sensors belonging to a specific row or column are equally spaced. For each hypothesis H 0 and H 1 , the correlation structure of the local decisions is modelled with a two- dimensional causal field where the rows and columns are outcomes of the same first-order binary Markov chain. Under this scenario, we derive a closed-form error exponent for the Neyman-Pearson fusion of the local decisions. Afterwards, using the derived error exponent we study the effect of different design parameters of the network on its overall detection performance},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{nczink2009,
title = {Spatial Separation of Multi-User MIMO Channels},
author = {Nicolai Czink and Bernd Bandemer and Gonzalo Vazquez-Vilar and Louay Jalloul and Claude Oestges and Arogyaswami Paulraj},
year  = {2009},
date = {2009-09-01},
booktitle = {20th Personal, Indoor and Mobile Radio Communications Symposium 2009 (PIMRC 09)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{bbandemer2009,
title = {On the Sum Capacity of A Class of Cyclically Symmetric Deterministic Interference Channels},
author = {Bernd Bandemer and Gonzalo Vazquez-Vilar and Abbas El Gamal},
year  = {2009},
date = {2009-06-01},
booktitle = {2009 IEEE International Symposium on Information Theory (ISIT 2009)},
address = {Coex, Seoul, Korea},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{crowncom2009,
title = {Multiantenna detection of multicarrier primary signals exploiting spectral a priori information},
author = {Roberto L\'{o}pez-Valcarce and Gonzalo Vazquez-Vilar and Marcos \'{A}lvarez-D\'{i}az},
year  = {2009},
date = {2009-06-01},
booktitle = {4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications (Crowncom 2009)},
address = {Hannover, Germany},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{spawc2009,
title = {Wideband Spectrum Sensing in Cognitive Radio: Joint Estimation of Noise Variance and Multiple Signal Levels},
author = {Roberto L\'{o}pez-Valcarce and Gonzalo Vazquez-Vilar},
year  = {2009},
date = {2009-06-01},
booktitle = {2009 IEEE International Workshop on Signal Processing Advances for Wireless Communications (Spawc 2009)},
address = {Perugia, Italy},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Olmos2009,
title = {Soft LDPC Decoding in Nonlinear Channels with Gaussian Processes for Classification},
author = {Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://www.eurasip.org/Proceedings/Eusipco/Eusipco2009/contents/papers/1569186781.pdf},
year  = {2009},
date = {2009-01-01},
booktitle = {European Signal Processing Conference (EUSIPCO)},
address = {Glasgow},
abstract = {In this paper, we propose a new approach for nonlinear equalization based on Gaussian processes for classification (GPC).We also measure the performance of the equalizer after a low-density parity-check channel decoder has detected the received sequence. Typically, most channel equalizers concentrate on reducing the bit error rate, instead of providing accurate posterior probability estimates. GPC is a Bayesian nonlinear classification tool that provides accurate posterior probability estimates with short training sequences. We show that the accuracy of these estimates is essential for optimal performance of the channel decoder and that the error rate outputted by the equalizer might be irrelevant to understand the performance of the overall communication receiver. We compare the proposed equalizers with state-ofthe- art solutions.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Bravo-Santos2009,
title = {Cooperative Relay Communications in Mesh Networks},
author = {\'{A}ngel M Bravo-Santos and Petar M Djuric},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5161835},
isbn = {978-1-4244-3695-8},
year  = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications},
pages = {499--503},
publisher = {IEEE},
address = {Perugia},
abstract = {In previous literature on cooperative relay communications, the emphasis has been on the study of multi-hop networks. In this paper we address mesh wireless networks that use decode-and-forward relays for which we derive the optimal node decision rules in case of binary transmission. We also obtain the expression for the overall bit error probability. We compare the mesh networks with multi-hop networks and show the improvement in performance that can be achieved with them when both networks have the same number of nodes and equal number of hops.},
keywords = {binary transmission, bit error probability, Bit error rate, cooperative relay communications, decode-and-forward relays, Detectors, error statistics, Maximum likelihood decoding, maximum likelihood detection, Mesh networks, mesh wireless networks, multi-hop networks, Network topology, optimal node decision rules, Peer to peer computing, radio networks, Relays, spread spectrum communication, telecommunication network topology, Wireless Sensor Networks},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Bugallo2009,
title = {Cost-Reference Particle Filters and Fusion of Information},
author = {Monica F Bugallo and Cristina S Maiz and Joaquin Miguez and Petar M Djuric},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4785936},
year  = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop},
pages = {286--291},
publisher = {IEEE},
address = {Marco Island, FL},
abstract = {Cost-reference particle filtering is a methodology for tracking unknowns in a system without reliance on probabilistic information about the noises in the system. The methodology is based on analogous principles as the ones of standard particle filtering. Unlike the random measures of standard particle filters that are composed of particles and weights, the random measures of cost-reference particle filters contain particles and user-defined costs. In this paper, we discuss a few scenarios where we need to meld random measures of two or more cost-reference particle filters. The objective is to obtain a fused random measure that combines the information from the individual cost-reference particle filters.},
keywords = {costs, distributed processing, Electronic mail, fusion, Information filtering, Information filters, information fusion, Measurement standards, probabilistic information, random measures, sensor fusion, smoothing methods, Weight measurement},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Djuric2009,
title = {Model Assessment with Kolmogorov-Smirnov Statistics},
author = {Petar M Djuric and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4960248},
issn = {1520-6149},
year  = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE International Conference on Acoustics, Speech and Signal Processing},
pages = {2973--2976},
publisher = {IEEE},
address = {Taipei},
abstract = {One of the most basic problems in science and engineering is the assessment of a considered model. The model should describe a set of observed data and the objective is to find ways of deciding if the model should be rejected. It seems that this is an ill-conditioned problem because we have to test the model against all the possible alternative models. In this paper we use the Kolmogorov-Smirnov statistic to develop a test that shows if the model should be kept or it should be rejected. We explain how this testing can be implemented in the context of particle filtering. We demonstrate the performance of the proposed method by computer simulations.},
keywords = {Bayesian methods, Computer Simulation, Context modeling, Electronic mail, Filtering, ill-conditioned problem, Kolmogorov-Smirnov statistics, model assessment, modelling, Predictive models, Probability, statistical analysis, statistics, Testing},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Maiz2009,
title = {Particle Filtering in the Presence of Outliers},
author = {Cristina S Maiz and Joaquin Miguez and Petar M Djuric},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5278645},
isbn = {978-1-4244-2709-3},
year  = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE/SP 15th Workshop on Statistical Signal Processing},
pages = {33--36},
publisher = {IEEE},
address = {Cardiff},
abstract = {Particle filters have become very popular signal processing tools for problems that involve nonlinear tracking of an unobserved signal of interest given a series of related observations. In this paper we propose a new scheme for particle filtering when the observed data are possibly contaminated with outliers. An outlier is an observation that has been generated by some (unknown) mechanism different from the assumed model of the data. Therefore, when handled in the same way as regular observations, outliers may drastically degrade the performance of the particle filter. To address this problem, we introduce an auxiliary particle filtering scheme that incorporates an outlier detection step. We propose to implement it by means of a test involving statistics of the predictive distributions of the observations. Specifically, we investigate the use of a proposed statistic called spatial depth that can easily be applied to multidimensional random variates. The performance of the resulting algorithm is assessed by computer simulations of target tracking based on signal-power observations.},
keywords = {computer simulations, Degradation, Filtering, multidimensional random variates, Multidimensional signal processing, Multidimensional systems, Nonlinear tracking, Outlier detection, predictive distributions, Signal processing, signal processing tools, signal-power observations, spatial depth, statistical analysis, statistical distributions, statistics, Target tracking, Testing},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Martino2009,
title = {A Novel Rejection Sampling Scheme for Posterior Probability Distributions},
author = {Luca Martino and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4960235},
issn = {1520-6149},
year  = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE International Conference on Acoustics, Speech and Signal Processing},
pages = {2921--2924},
publisher = {IEEE},
address = {Taipei},
abstract = {Rejection sampling (RS) is a well-known method to draw from arbitrary target probability distributions, which has important applications by itself or as a building block for more sophisticated Monte Carlo techniques. The main limitation to the use of RS is the need to find an adequate upper bound for the ratio of the target probability density function (pdf) over the proposal pdf from which the samples are generated. There are no general methods to analytically find this bound, except in the particular case in which the target pdf is log-concave. In this paper we adopt a Bayesian view of the problem and propose a general RS scheme to draw from the posterior pdf of a signal of interest using its prior density as a proposal function. The method enables the analytical calculation of the bound and can be applied to a large class of target densities. We illustrate its use with a simple numerical example.},
keywords = {Additive noise, arbitrary target probability distributions, Bayes methods, Bayesian methods, Monte Carlo integration, Monte Carlo methods, Monte Carlo techniques, Overbounding, posterior probability distributions, Probability density function, Probability distribution, Proposals, Rejection sampling, rejection sampling scheme, Sampling methods, Signal processing algorithms, signal sampling, Upper bound},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Achutegui2009,
title = {A Multi-Model Particle Filtering Algorithm for Indoor Tracking of Mobile Terminals Using RSS Data},
author = {Katrin Achutegui and Luca Martino and Javier Rodas and Carlos J Escudero and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5280960},
isbn = {978-1-4244-4601-8},
year  = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE International Conference on Control Applications},
pages = {1702--1707},
publisher = {IEEE},
address = {Saint Petersburg},
abstract = {In this paper we address the problem of indoor tracking using received signal strength (RSS) as a position-dependent data measurement. This type of measurements is very appealing because they can be easily obtained with a variety of wireless technologies which are relatively inexpensive. The extraction of accurate location information from RSS in indoor scenarios is not an easy task, though. Since RSS is highly influenced by multipath propagation, it turns out very hard to adequately model the correspondence between the received power and the transmitter-to-receiver distance. The measurement models proposed in the literature are site-specific and require a great deal of information regarding the structure of the building where the tracking will be performed and therefore are not useful for a general application. For that reason we propose the use of a compound model that combines several sub-models, whose parameters are adjusted to specific and different propagation environments. This methodology, is called interacting multiple models (IMM), has been used in the past for modeling the motion of maneuvering targets. Here, we extend its application to handle also the uncertainty in the RSS observations and we refer to the resulting state-space model as a generalized IMM (GIMM) system. The flexibility of the GIMM approach is attained at the expense of an increase in the number of random processes that must be accurately tracked. To overcome this difficulty, we introduce a Rao-Blackwellized sequential Monte Carlo tracking algorithm that exhibits good performance both with synthetic and experimental data.},
keywords = {Bayesian methods, Control systems, Filtering algorithms, generalized interacting multiple model, GIMM, indoor radio, Indoor tracking, mobile radio, mobile terminal, Monte Carlo methods, multipath propagation, position-dependent data measurement, random process, random processes, Rao-Blackwellized sequential Monte Carlo tracking, received signal strength, RSS data, Sliding mode control, State-space methods, state-space model, Target tracking, tracking, transmitter-to-receiver distance, wireless network, wireless technology},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Djuric2009a,
title = {Measuring the Robustness of Sequential Methods},
author = {Petar M Djuric and Monica F Bugallo and Pau Closas and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5413275},
isbn = {978-1-4244-5179-1},
year  = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop},
pages = {29--32},
publisher = {IEEE},
address = {Aruba, Dutch Antilles},
abstract = {Whenever we apply methods for processing data, we make a number of model assumptions. In reality, these assumptions are not always correct. Robust methods can withstand model inaccuracies, that is, despite some incorrect assumptions they can still produce good results. We often want to know how robust employed methods are. To that end we need to have a yardstick for measuring robustness. In this paper, we propose an approach for constructing such metrics for sequential methods. These metrics are derived from the Kolmogorov-Smirnov distance between the cumulative distribution functions of the actual observations and the ones based on the assumed model. The use of the proposed metrics is demonstrated with simulation examples.},
keywords = {Additive noise, cumulative distribution functions, data processing method, extended Kalman filtering, Extraterrestrial measurements, Filtering, Gaussian distribution, Gaussian noise, Kalman filters, Kolmogorov-Smirnov distance, Least squares approximation, Noise robustness, nonlinear filters, robustness, sequential methods, statistical distributions, telecommunication computing},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Martino2009a,
title = {New Accept/Reject Methods for Independent Sampling from Posterior Probability Distributions},
author = {Luca Martino and Joaquin Miguez},
url = {http://www.academia.edu/2355641/NEW_ACCEPT_REJECT_METHODS_FOR_INDEPENDENT_SAMPLING_FROM_POSTERIOR_PROBABILITY_DISTRIBUTIONS},
year  = {2009},
date = {2009-01-01},
booktitle = {17th European Signal Processing Conference (EUSIPCO 2009)},
address = {Glasgow},
abstract = {Rejection sampling (RS) is a well-known method to generate(pseudo-)random samples from arbitrary probability distributionsthat enjoys important applications, either by itself or as a tool inmore sophisticated Monte Carlo techniques. Unfortunately, the useof RS techniques demands the calculation of tight upper bounds forthe ratio of the target probability density function (pdf) over theproposal density from which candidate samples are drawn. Exceptfor the class of log-concave target pdf’s, for which an efficientalgorithm exists, there are no general methods to analyticallydetermine this bound, which has to be derived from scratch foreach specific case. In this paper, we tackle the general problemof applying RS to draw from an arbitrary posterior pdf using theprior density as a proposal function. This is a scenario that appearsfrequently in Bayesian signal processing methods. We derive ageneral geometric procedure for the calculation of upper boundsthat can be used with a broad class of target pdf’s, includingscenarios with correlated observations, multimodal and/or mixturemeasurement noises. We provide some simple numerical examplesto illustrate the application of the proposed techniques},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Perez-Cruz2009,
title = {Distributed Least Square for Consensus Building in Sensor Networks},
author = {Fernando Perez-Cruz and S R Kulkarni},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5205336},
isbn = {978-1-4244-4312-3},
year  = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE International Symposium on Information Theory},
pages = {2877--2881},
publisher = {IEEE},
address = {Seoul},
abstract = {We present a novel mechanism for consensus building in sensor networks. The proposed algorithm has three main properties that make it suitable for general sensor-network learning. First, the proposed algorithm is based on robust nonparametric statistics and thereby needs little prior knowledge about the network and the function that needs to be estimated. Second, the algorithm uses only local information about the network and it communicates only with nearby sensors. Third, the algorithm is completely asynchronous and robust. It does not need to coordinate the sensors to estimate the underlying function and it is not affected if other sensors in the network stop working. Therefore, the proposed algorithm is an ideal candidate for sensor networks deployed in remote and inaccessible areas, which might need to change their objective once they have been set up.},
keywords = {Change detection algorithms, Channel Coding, Distributed computing, distributed least square method, graphical models, Inference algorithms, Kernel, Least squares methods, nonparametric statistics, Parametric statistics, robustness, sensor-network learning, statistical analysis, Telecommunication network reliability, Wireless sensor network, Wireless Sensor Networks},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Fresia2009,
title = {Optimized Concatenated LDPC Codes for Joint Source-Channel Coding},
author = {Maria Fresia and Fernando Perez-Cruz and Vincent H Poor},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5205766},
isbn = {978-1-4244-4312-3},
year  = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE International Symposium on Information Theory},
pages = {2131--2135},
publisher = {IEEE},
address = {Seoul},
abstract = {In this paper a scheme for joint source-channel coding based on low-density-parity-check (LDPC) codes is investigated. Two concatenated independent LDPC codes are used in the transmitter: one for source coding and the other for channel coding, with a joint belief propagation decoder. The asymptotic behavior is analyzed using EXtrinsic Information Transfer (EXIT) charts and this approximation is corroborated with illustrative experiments. The optimization of the degree distributions for our sparse code to maximize the information transmission rate is also considered.},
keywords = {approximation theory, asymptotic behavior analysis, Channel Coding, combined source-channel coding, Concatenated codes, Decoding, Entropy, EXIT chart, extrinsic information transfer, H infinity control, Information analysis, joint belief propagation decoder, joint source-channel coding, low-density-parity-check code, optimized concatenated independent LDPC codes, parity check codes, Redundancy, source coding, transmitter, Transmitters},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Martino2009b,
title = {An Adaptive Accept/Reject Sampling Algorithm for Posterior Probability Distributions},
author = {Luca Martino and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5278644},
isbn = {978-1-4244-2709-3},
year  = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE/SP 15th Workshop on Statistical Signal Processing},
pages = {45--48},
publisher = {IEEE},
address = {Cardiff},
abstract = {Accept/reject sampling is a well-known method to generate random samples from arbitrary target probability distributions. It demands the design of a suitable proposal probability density function (pdf) from which candidate samples can be drawn. These samples are either accepted or rejected depending on a test involving the ratio of the target and proposal densities. In this paper we introduce an adaptive method to build a sequence of proposal pdf's that approximate the target density and hence can ensure a high acceptance rate. In order to illustrate the application of the method we design an accept/reject particle filter and then assess its performance and sampling efficiency numerically, by means of computer simulations.},
keywords = {adaptive accept/reject sampling, Adaptive rejection sampling, arbitrary target probability distributions, Computer Simulation, Filtering, Monte Carlo integration, Monte Carlo methods, posterior probability distributions, Probability, Probability density function, Probability distribution, Proposals, Rejection sampling, Sampling methods, sensor networks, Signal processing algorithms, signal sampling, Testing},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Vinuelas-Peris2009,
title = {Sensing Matrix Optimization in Distributed Compressed Sensing},
author = {Pablo Vinuelas-Peris and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5278496},
isbn = {978-1-4244-2709-3},
year  = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE/SP 15th Workshop on Statistical Signal Processing},
pages = {638--641},
publisher = {IEEE},
address = {Cardiff},
abstract = {Distributed compressed sensing (DCS) seeks to simultaneously measure signals that are each individually sparse in some domain(s) and also mutually correlated. In this paper we consider the scenario in which the (overcomplete) bases for common component and innovations are different. We propose and analyze a distributed coding strategy for the common component, and also the use of efficient projection (EP) method for optimizing the sensing matrices in this setting. We show the effectiveness of our approach by computer simulations using the orthogonal matching pursuit (OMP) as joint recovery method, and we discuss the configuration of the distribution strategy.},
keywords = {Compressed sensing, Computer Simulation, computer simulations, correlated signal, Correlated signals, correlation theory, Dictionaries, distributed coding strategy, distributed compressed sensing, Distributed control, efficient projection method, Encoding, joint recovery method, Matching pursuit algorithms, Optimization methods, orthogonal matching pursuit, Projection Matrix Optimization, sensing matrix optimization, Sensor Network, Sensor phenomena and characterization, Sensor systems, Signal processing, Sparse matrices, Technological innovation},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Perez-Cruz2009a,
title = {Optimal Precoding for Multiple-Input Multiple-Output Gaussian Channels},
author = {Fernando Perez-Cruz and Miguel R D Rodrigues and Sergio Verdu},
url = {http://eprints.pascal-network.org/archive/00006754/},
year  = {2009},
date = {2009-01-01},
booktitle = {Seminar PIIRS},
address = {Princeton},
abstract = {We investigate the linear precoding and power allocation policies that maximize the mutual information for general multiple-input multiple-output (MIMO) Gaussian channels with arbitrary input distributions, by capitalizing on the relationship between mutual information and minimum mean-square error. The optimal linear precoder satisfies a fixed-point equation as a function of the channel and the input constellation. For nonGaussian inputs, a nondiagonal precoding matrix in general increases the information transmission rate, even for parallel noninteracting channels. Whenever precoding is precluded, the optimal power allocation policy also satisfies a fixed-point equation; we put forth a generalization of the mercury/waterfilling algorithm, previously proposed for parallel noninterfering channels, in which the mercury level accounts not only for the nonGaussian input distributions, but also for the interference among inputs.},
keywords = {Theory \&amp;amp;amp; Algorithms},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Miguez2009,
title = {Sequential Monte Carlo Optimization Using Artificial State-Space Models},
author = {Joaquin Miguez and Cristina S Maiz and Petar M Djuric and Dan Crisan},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4785933},
year  = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop},
pages = {268--273},
publisher = {IEEE},
address = {Marco Island, FL},
abstract = {We introduce a method for sequential minimization of a certain class of (possibly non-convex) cost functions with respect to a high dimensional signal of interest. The proposed approach involves the transformation of the optimization problem into one of estimation in a discrete-time dynamical system. In particular, we describe a methodology for constructing an artificial state-space model which has the signal of interest as its unobserved dynamic state. The model is \"{a}dapted" to the cost function in the sense that the maximum a posteriori (MAP) estimate of the system state is also a global minimizer of the cost function. The advantage of the estimation framework is that we can draw from a pool of sequential Monte Carlo methods, for particle approximation of probability measures in dynamic systems, that enable the numerical computation of MAP estimates. We provide examples of how to apply the proposed methodology, including some illustrative simulation results.},
keywords = {Acceleration, Cost function, Design optimization, discrete-time dynamical system, Educational institutions, Mathematics, maximum a posteriori estimate, maximum likelihood estimation, minimisation, Monte Carlo methods, Optimization methods, Probability distribution, sequential Monte Carlo optimization, Sequential optimization, Signal design, State-space methods, state-space model, Stochastic optimization},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Fresia2009a,
title = {Joint Source-Channel Coding with Concatenated LDPC Codes},
author = {Maria Fresia and Fernando Perez-Cruz and Vincent H Poor and Sergio Verdu},
url = {http://eprints.pascal-network.org/archive/00004905/},
year  = {2009},
date = {2009-01-01},
booktitle = {Information Theory and Applications (ITA)},
address = {San Diego},
abstract = {The separation principle, a milestone in information theory, establishes that for stationary sources and channels there is no loss of optimality when a channel-independent source encoder followed by a source-independent channel encoder are used to transmit the data, as the code length tends to infinity. Thereby, the source and channel encoding have been typically treated as independent problems. For finite-length codes, the separation principle does not hold and a joint encoder and decoder can potentially increase the achieved information transmission rate. In this paper, a scheme for joint source-channel coding based on low-density parity-check (LDPC) codes is presented. The source is compressed and protected with two concatenated LDPC codes and a joint belief propagation decoder is implemented. EXIT chart performance of the proposed schemes is studied. The results are verified with some illustrative experiments.},
keywords = {Learning/Statistics \&amp;amp;amp; Optimisation},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Murillo-Fuentes2009,
title = {Gaussian Process Regressors for Multiuser Detection in DS-CDMA Systems},
author = {Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5201027},
issn = {0090-6778},
year  = {2009},
date = {2009-01-01},
journal = {IEEE Transactions on Communications},
volume = {57},
number = {8},
pages = {2339--2347},
abstract = {In this paper we present Gaussian processes for Regression (GPR) as a novel detector for CDMA digital communications. Particularly, we propose GPR for constructing analytical nonlinear multiuser detectors in CDMA communication systems. GPR can easily compute the parameters that describe its nonlinearities by maximum likelihood. Thereby, no cross-validation is needed, as it is typically used in nonlinear estimation procedures. The GPR solution is analytical, given its parameters, and it does not need to solve an optimization problem for building the nonlinear estimator. These properties provide fast and accurate learning, two major issues in digital communications. The GPR with a linear decision function can be understood as a regularized MMSE detector, in which the regularization parameter is optimally set. We also show the GPR receiver to be a straightforward nonlinear extension of the linear minimum mean square error (MMSE) criterion, widely used in the design of these receivers. We argue the benefits of this new approach in short codes CDMA systems where little information on the users' codes, users' amplitudes or the channel is available. The paper includes some experiments to show that GPR outperforms linear (MMSE) and nonlinear (SVM) state-ofthe- art solutions.},
keywords = {analytical nonlinear multiuser detectors, code division multiple access, communication systems, Detectors, digital communication, digital communications, DS-CDMA systems, Gaussian process for regressi, Gaussian process regressors, Gaussian processes, GPR, Ground penetrating radar, least mean squares methods, maximum likelihood, maximum likelihood detection, maximum likelihood estimation, mean square error methods, minimum mean square error, MMSE, Multiaccess communication, Multiuser detection, nonlinear estimator, nonlinear state-ofthe- art solutions, radio receivers, Receivers, regression analysis, Support vector machines},
pubstate = {published},
tppubtype = {article}
}
@article{Marino2009,
title = {Monte Carlo Method for Adaptively Estimating the Unknown Parameters and the Dynamic State of Chaotic Systems},
author = {In\'{e}s P. Mari\~{n}o and Joaquin Miguez and Riccardo Meucci},
url = {http://link.aps.org/doi/10.1103/PhysRevE.79.056218},
issn = {1539-3755},
year  = {2009},
date = {2009-01-01},
journal = {Physical Review E},
volume = {79},
number = {5},
pages = {056218},
publisher = {American Physical Society},
abstract = {We propose a Monte Carlo methodology for the joint estimation of unobserved dynamic variables and unknown static parameters in chaotic systems. The technique is sequential, i.e., it updates the variable and parameter estimates recursively as new observations become available, and, hence, suitable for online implementation. We demonstrate the validity of the method by way of two examples. In the first one, we tackle the estimation of all the dynamic variables and one unknown parameter of a five-dimensional nonlinear model using a time series of scalar observations experimentally collected from a chaotic CO2\&lt;math display="inline"\&gt;\&lt;mrow\&gt;\&lt;msub\&gt;\&lt;mrow\&gt;\&lt;mtext\&gt;CO\&lt;mn\&gt;2 laser. In the second example, we address the estimation of the two dynamic variables and the phase parameter of a numerical model commonly employed to represent the dynamics of optoelectronic feedback loops designed for chaotic communications over fiber-optic links.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Vazquez2009,
title = {Maximum-Likelihood Sequence Detection in Time- and Frequency-Selective MIMO Channels With Unknown Order},
author = {Manuel A Vazquez and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4510724},
issn = {0018-9545},
year  = {2009},
date = {2009-01-01},
journal = {IEEE Transactions on Vehicular Technology},
volume = {58},
number = {1},
pages = {499--504},
abstract = {In the equalization of frequency-selective multiple-input-multiple-output (MIMO) channels, it is usually assumed that the length of the channel impulse response (CIR), which is also referred to as the channel order, is known. However, this is not true in most practical situations, and it is a common approach to overestimate the channel order to avoid the serious performance degradation that occurs when the CIR length is underestimated. Unfortunately, the computational complexity of maximum-likelihood sequence detection (MLSD) in frequency-selective channels exponentially grows with the channel order; hence, overestimation can actually be undesirable because it leads to more expensive and inefficient receivers. In this paper, we introduce an algorithm for MLSD that incorporates the full estimation of the MIMO CIR parameters, including its order. The proposed technique is based on the per-survivor processing (PSP) methodology; it admits both blind and semiblind implementations, depending on the availability of pilot data, and is designed to work with time-selective channels. In addition to the analytical derivation of the algorithm, we provide computer simulation results that illustrate the effectiveness of the resulting receiver.},
keywords = {channel impulse response, channel order estimation, CIR, frequency-selective multiple-input-multiple-output, joint channel and data estimation, maximum likelihood detection, maximum-likelihood sequence detection, MIMO channels, MIMO communication, MLSD, Multiple Input Multiple Output (MIMO), multiple-input\textendashmultiple-output (MIMO), per-survivor processing, per-survivor processing (PSP), telecommunication channels, time-selective multiple-input-multiple-output chan},
pubstate = {published},
tppubtype = {article}
}
@article{Koch2009,
title = {Channels That Heat Up},
author = {Tobias Koch and Amos Lapidoth and Paul P Sotiriadis},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5165190},
issn = {0018-9448},
year  = {2009},
date = {2009-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {55},
number = {8},
pages = {3594--3612},
abstract = {This paper considers an additive noise channel where the time-A; noise variance is a weighted sum of the squared magnitudes of the previous channel inputs plus a constant. This channel model accounts for the dependence of the intrinsic thermal noise on the data due to the heat dissipation associated with the transmission of data in electronic circuits: the data determine the transmitted signal, which in turn heats up the circuit and thus influences the power of the thermal noise. The capacity of this channel (both with and without feedback) is studied at low transmit powers and at high transmit powers. At low transmit powers, the slope of the capacity-versus-power curve at zero is computed and it is shown that the heating-up effect is beneficial. At high transmit powers, conditions are determined under which the capacity is bounded, i.e., under which the capacity does not grow to infinity as the allowed average power tends to infinity.},
keywords = {additive noise channel, Capacity per unit cost, channel capacity, channels with memory, cooling, electronic circuits, heat dissipation, heat sinks, high signal-to-noise ratio, high signal-to-noise ratio (SNR), intrinsic thermal noise, low transmit power, network analysis, noise variance, on-chip communication, thermal noise},
pubstate = {published},
tppubtype = {article}
}
@article{Lazaro2009b,
title = {Blind Equalization Using the IRWLS Formulation of the Support Vector Machine},
author = {Marcelino L\'{a}zaro and Jonathan Gonz\'{a}lez-Olasola},
url = {http://www.sciencedirect.com/science/article/pii/S0165168409000383},
issn = {01651684},
year  = {2009},
date = {2009-01-01},
journal = {Signal Processing},
volume = {89},
number = {7},
pages = {1436--1445},
abstract = {In this paper, using a common framework, we propose, analyze, and evaluate several variants of batch algorithms for blind equalization of SISO channels. They are based on the iterative re-weighted least square (IRWLS) solution for the support vector machine (SVM). The proposed methods combine the conventional cost function of the SVM with classical error functions applied to blind equalization: Sato's and Godard's error functions are included in the penalty term of the SVM. The relationship of these batch algorithms with conventional equalization and regularization techniques is analyzed in the paper. Simulation experiments performed over a relevant set of channels show that the proposed equalization methods perform better than traditional cumulant-based methods: they require a lower number of data samples to achieve the same equalization level and convergence ratio.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{Goez2009,
title = {Training of Neural Classifiers by Separating Distributions at the Hidden Layer},
author = {Roger Goez and Marcelino Lazaro},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5306240},
isbn = {978-1-4244-4947-7},
year  = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE International Workshop on Machine Learning for Signal Processing},
pages = {1--6},
publisher = {IEEE},
address = {Grenoble},
abstract = {A new cost function for training of binary classifiers based on neural networks is proposed. This cost function aims at separating the distributions for patterns of each class at the output of the hidden layer of the network. It has been implemented in a Generalized Radial Basis Function (GRBF) network and its performance has been evaluated under three different databases, showing advantages with respect to the conventional Mean Squared Error (MSE) cost function. With respect to the Support Vector Machine (SVM) classifier, the proposed method has also advantages both in terms of performance and complexity.},
keywords = {Artificial neural networks, Bayesian methods, Cost function, Curve fitting, Databases, Function approximation, Neural networks, Speech recognition, Support vector machine classification, Support vector machines},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Plata-Chaves2009,
title = {Closed-Form Error Exponent for the Neyman-Pearson Fusion of Markov Local Decisions},
author = {Jorge Plata-Chaves and Marcelino Lazaro},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=5278522},
isbn = {978-1-4244-2709-3},
year  = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE/SP 15th Workshop on Statistical Signal Processing},
pages = {533--536},
publisher = {IEEE},
address = {Cardiff},
abstract = {In this correspondence, we derive a closed-form expression of the error exponent associated with the binary Neyman-Pearson test performed at the fusion center of a distributed detection system where a large number of local detectors take dependent binary decisions regarding a specific phenomenon. We assume that the sensors are equally spaced along a straight line, that their local decisions are taken with no kind of cooperation, and that they are transmitted to the fusion center over an error free parallel access channel. Under each one of the two possible hypothesis, H0 and H1 the correlation structure of the local binary decisions is modelled with a first-order binary Markov chain whose transition probabilities are linked with different physical parameters of the network. Through different simulations based on the error exponent and a deterministic physical model of the aforementioned transition probabilities we study the effect of network density on the overall detection performance.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Alvarez2009,
title = {Latent Force Models},
author = {Mauricio Alvarez and David Luengo and Neil D Lawrence},
year  = {2009},
date = {2009-01-01},
booktitle = {Conf. on Artificial Intelligence and Statistics},
address = {Clearwater Beach},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Lazaro2009bb,
title = {Optimal Sensor Selection in Binary Heterogeneous Sensor Networks},
author = {Marcelino Lazaro and Matilde Sanchez-Fernandez and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4749309},
issn = {1053-587X},
year  = {2009},
date = {2009-01-01},
journal = {IEEE Transactions on Signal Processing},
volume = {57},
number = {4},
pages = {1577--1587},
abstract = {We consider the problem of sensor selection in a heterogeneous sensor network when several types of binary sensors with different discrimination performance and costs are available. We want to analyze what is the optimal proportion of sensors of each class in a target detection problem when a total cost constraint is specified. We obtain the conditional distributions of the observations at the fusion center given the hypotheses, necessary to perform an optimal hypothesis test in this heterogeneous scenario. We characterize the performance of the tests by means of the symmetric Kullback-Leibler divergence, or J -divergence, applied to the conditional distributions under each hypothesis. By formulating the sensor selection as a constrained maximization problem, and showing the linearity of the J-divergence with the number of sensors of each class, we found that the optimal proportion of sensors is ldquowinner takes allrdquo like. The sensor class with the best performance/cost ratio is selected.},
keywords = {binary heterogeneous sensor networks, discrimination performance, Energy scaling, object detection, optimal sensor selection, performance-cost ratio, sensor networks, sensor selection, symmetric Kullback-Leibler divergence, target detection problem, Wireless Sensor Networks},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{5281057,
title = {Using Doppler radar and MEMS gyro to augment DGPS for land vehicle navigation},
author = {Jussi Parviainen and Manuel A V\'{a}zquez and Olli Pekkalin and Jani Hautamaki and Jussi Collin and Pavel Davidson},
doi = {10.1109/CCA.2009.5281057},
year  = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
booktitle = {2009 IEEE Control Applications, (CCA) \& Intelligent Control, (ISIC)},
pages = {1690-1695},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{5156849,
title = {Uninterrupted portable car navigation system using GPS, map and inertial sensors data},
author = {Pavel Davidson and Manuel A V\'{a}zquez and Robert Piche},
doi = {10.1109/ISCE.2009.5156849},
year  = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
booktitle = {2009 IEEE 13th International Symposium on Consumer Electronics},
pages = {836-840},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{asilomar2008,
title = {Mobility Dependent Feedback Scheme for point-to-point MIMO Systems},
author = {Gonzalo Vazquez-Vilar and Vinay Majjigi and Aydin Sezgin and Arogyaswami Paulraj},
year  = {2008},
date = {2008-10-01},
booktitle = {Asilomar Conference on Signals, Systems, and Computers (Asilomar SSC 2008)},
address = {Pacific Grove, CA, U.S.A.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Koch2008,
title = {On Multipath Fading Channels at High SNR},
author = {Tobias Koch and Amos Lapidoth},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=4595252},
isbn = {978-1-4244-2256-2},
year  = {2008},
date = {2008-01-01},
booktitle = {2008 IEEE International Symposium on Information Theory},
pages = {1572--1576},
publisher = {IEEE},
address = {Toronto},
abstract = {This paper studies the capacity of discrete-time multipath fading channels. It is assumed that the number of paths is finite, i.e., that the channel output is influenced by the present and by the L previous channel inputs. A noncoherent channel model is considered where neither transmitter nor receiver are cognizant of the fading's realization, but both are aware of its statistic. The focus is on capacity at high signal-to-noise ratios (SNR). In particular, the capacity pre-loglog-defined as the limiting ratio of the capacity to loglog(SNR) as SNR tends to infinity-is studied. It is shown that, irrespective of the number of paths L, the capacity pre-loglog is 1.},
keywords = {channel capacity, Delay, discrete time systems, discrete-time channels, Entropy, Fading, fading channels, Frequency, Mathematical model, multipath channels, multipath fading channels, noncoherent channel model, Random variables, Signal to noise ratio, signal-to-noise ratios, SNR, statistics, Transmitters},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Vazquez2008,
title = {A Per-Survivor Processing Algorithm for Maximum Likelihood Equalization of MIMO Channels with Unknown Order},
author = {Manuel A Vazquez and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=4475587},
isbn = {978-1-4244-1756-8},
year  = {2008},
date = {2008-01-01},
booktitle = {2008 International ITG Workshop on Smart Antennas},
pages = {387--391},
publisher = {IEEE},
address = {Vienna},
abstract = {In the equalization of frequency-selective multiple-input multiple-output (MIMO) channels it is usually assumed that the length of the channel impulse response (CIR), also referred to as the channel order, is known. However, this is not true in most practical situations and, in order to avoid the serious performance degradation that occurs when the CIR length is underestimated, a channel with "more than enough" taps is usually considered. This possibly means overestimating the channel order, and is not desirable since the computational complexity of maximum likelihood sequence detection (MLSD) in frequency-selective channels grows exponentially with the channel order. In addition to that, the higher the channel order considered, the more the number of channel coefficients that need to be estimated from the same set of observations. In this paper, we introduce an algorithm for MLSD that incorporates the full estimation of the MIMO CIR parameters, including its order. The proposed technique is based on the per survivor processing (PSP) methodology, it admits both blind and semiblind implementations, depending on the availability of pilot data, and is designed to work with time-selective channels. Besides the analytical derivation of the algorithm, we provide computer simulation results that illustrate the effectiveness of the resulting receiver},
keywords = {Channel estimation, channel impulse response, computational complexity, Computer science education, Computer Simulation, Degradation, Frequency, frequency-selective multiple-input multiple-output, maximum likelihood detection, maximum likelihood equalization, maximum likelihood estimation, maximum likelihood sequence detection, maximum likelihood sequence estimation, MIMO, MIMO channels, MIMO communication, per-survivor processing algorithm, time-selective channels, Transmitting antennas},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Miguez2008,
title = {Analysis of a Sequential Monte Carlo Optimization Methodology},
author = {Joaquin Miguez},
url = {http://www.eurasip.org/Proceedings/Eusipco/Eusipco2008/papers/1569105254.pdf},
year  = {2008},
date = {2008-01-01},
booktitle = {16th European Signal Processing Conference (EUSIPCO 2008},
address = {Lausanne},
abstract = {We investigate a family of stochastic exploration methods that has been recently proposed to carry out estimation and prediction in discrete-time random dynamical systems. The key of the novel approach is to identify a cost function whose minima provide valid estimates of the system state at successive time instants. This function is recursively optimized using a sequential Monte Carlo minimization (SMCM) procedure which is similar to standard particle filtering algorithms but does not require a explicit probabilistic model to be imposed on the system. In this paper, we analyze the asymptotic convergence of SMCM methods and show that a properly designed algorithm produces a sequence of system-state estimates with individually minimal contributions to the cost function. We apply the SMCM method to a target tracking problem in order to illustrate how convergence is achieved in the way predicted by the theory.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Perez-Cruz2008,
title = {Kullback-Leibler Divergence Estimation of Continuous Distributions},
author = {Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4595271},
isbn = {978-1-4244-2256-2},
year  = {2008},
date = {2008-01-01},
booktitle = {2008 IEEE International Symposium on Information Theory},
pages = {1666--1670},
publisher = {IEEE},
address = {Toronto},
abstract = {We present a method for estimating the KL divergence between continuous densities and we prove it converges almost surely. Divergence estimation is typically solved estimating the densities first. Our main result shows this intermediate step is unnecessary and that the divergence can be either estimated using the empirical cdf or k-nearest-neighbour density estimation, which does not converge to the true measure for finite k. The convergence proof is based on describing the statistics of our estimator using waiting-times distributions, as the exponential or Erlang. We illustrate the proposed estimators and show how they compare to existing methods based on density estimation, and we also outline how our divergence estimators can be used for solving the two-sample problem.},
keywords = {Convergence, density estimation, Density measurement, Entropy, Frequency estimation, H infinity control, information theory, k-nearest-neighbour density estimation, Kullback-Leibler divergence estimation, Machine learning, Mutual information, neuroscience, Random variables, statistical distributions, waiting-times distributions},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Perez-Cruz2008a,
title = {Optimal Precoding for Digital Subscriber Lines},
author = {Fernando Perez-Cruz and Miguel R D Rodrigues and Sergio Verdu},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4533270},
isbn = {978-1-4244-2075-9},
year  = {2008},
date = {2008-01-01},
booktitle = {2008 IEEE International Conference on Communications},
pages = {1200--1204},
publisher = {IEEE},
address = {Beijing},
abstract = {We determine the linear precoding policy that maximizes the mutual information for general multiple-input multiple-output (MIMO) Gaussian channels with arbitrary input distributions, by capitalizing on the relationship between mutual information and minimum mean squared error (MMSE). The optimal linear precoder can be computed by means of a fixed- point equation as a function of the channel and the input constellation. We show that diagonalizing the channel matrix does not maximize the information transmission rate for nonGaussian inputs. A full precoding matrix may significantly increase the information transmission rate, even for parallel non-interacting channels. We illustrate the application of our results to typical Gigabit DSL systems.},
keywords = {Bit error rate, channel matrix diagonalization, Communications Society, Computer science, digital subscriber lines, DSL, Equations, fixed-point equation, Gaussian channels, least mean squares methods, linear codes, matrix algebra, MIMO, MIMO communication, MIMO Gaussian channel, minimum mean squared error method, MMSE, multiple-input multiple-output communication, Mutual information, optimal linear precoder, precoding, Telecommunications, Telephony},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Koch2008a,
title = {Multipath Channels of Bounded Capacity},
author = {Tobias Koch and Amos Lapidoth},
url = {http://www.researchgate.net/publication/4353168_Multipath_channels_of_bounded_capacity},
isbn = {978-1-4244-2269-2},
year  = {2008},
date = {2008-01-01},
booktitle = {2008 IEEE Information Theory Workshop},
pages = {6--10},
publisher = {IEEE},
address = {Oporto},
abstract = {The capacity of discrete-time, non-coherent, multi-path fading channels is considered. It is shown that if the delay spread is large in the sense that the variances of the path gains do not decay faster than geometrically, then capacity is bounded in the signal-to-noise ratio.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{JoseM.Leiva-murillo2008,
title = {Linear Dimensionality Reduction With Gausian Mixture Models},
author = {Jose M Leiva-murillo and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.167.798},
year  = {2008},
date = {2008-01-01},
booktitle = {Cognitive Information Processing, (CIP) 2008},
address = {Santorini},
abstract = {In this paper, we explore the application of several informationtheoretic criteria to the problem of reducing the dimension in pattern recognition. We consider the use of Gaussian mixture models for estimating the distribution of the data. Three algorithms are proposed for linear feature extraction by the maximization of the mutual information, the likelihood or the hypotheses test, respectively. The experiments show that the proposed methods outperform the classical methods based on parametric Gaussian models, and avoid the intense computational complexity of nonparametric kernel density estimators.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Koch2008b,
title = {Multipath Channels of Unbounded Capacity},
author = {Tobias Koch and Amos Lapidoth},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=4736611},
isbn = {978-1-4244-2481-8},
year  = {2008},
date = {2008-01-01},
booktitle = {2008 IEEE 25th Convention of Electrical and Electronics Engineers in Israel},
pages = {640--644},
publisher = {IEEE},
address = {Eilat},
abstract = {The capacity of discrete-time, noncoherent, multipath fading channels is considered. It is shown that if the variances of the path gains decay faster than exponentially, then capacity is unbounded in the transmit power.},
keywords = {channel capacity, discrete-time capacity, Entropy, Fading, fading channels, Frequency, H infinity control, Information rates, multipath channels, multipath fading channels, noncoherent, noncoherent capacity, path gains decay, Signal to noise ratio, statistics, Transmitters, unbounded capacity},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Rodrigues2008,
title = {Multiple-Input Multiple-Output Gaussian Channels: Optimal Covariance for Non-Gaussian Inputs},
author = {Miguel R D Rodrigues and Fernando Perez-Cruz and Sergio Verdu},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4578704},
isbn = {978-1-4244-2269-2},
year  = {2008},
date = {2008-01-01},
booktitle = {2008 IEEE Information Theory Workshop},
pages = {445--449},
publisher = {IEEE},
address = {Porto},
abstract = {We investigate the input covariance that maximizes the mutual information of deterministic multiple-input multipleo-utput (MIMO) Gaussian channels with arbitrary (not necessarily Gaussian) input distributions, by capitalizing on the relationship between the gradient of the mutual information and the minimum mean-squared error (MMSE) matrix. We show that the optimal input covariance satisfies a simple fixed-point equation involving key system quantities, including the MMSE matrix. We also specialize the form of the optimal input covariance to the asymptotic regimes of low and high snr. We demonstrate that in the low-snr regime the optimal covariance fully correlates the inputs to better combat noise. In contrast, in the high-snr regime the optimal covariance is diagonal with diagonal elements obeying the generalized mercury/waterfilling power allocation policy. Numerical results illustrate that covariance optimization may lead to significant gains with respect to conventional strategies based on channel diagonalization followed by mercury/waterfilling or waterfilling power allocation, particularly in the regimes of medium and high snr.},
keywords = {Binary phase shift keying, covariance matrices, Covariance matrix, deterministic MIMO Gaussian channel, fixed-point equation, Gaussian channels, Gaussian noise, Information rates, intersymbol interference, least mean squares methods, Magnetic recording, mercury-waterfilling power allocation policy, MIMO, MIMO communication, minimum mean-squared error, MMSE, MMSE matrix, multiple-input multiple-output system, Multiple-Input Multiple-Output Systems, Mutual information, Optimal Input Covariance, Optimization, Telecommunications},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Vazquez2008a,
title = {A Per-Survivor Processing Algorithm for Maximum Likelihood Equalization of MIMO Channels with Unknown Order},
author = {Manuel A Vazquez and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4475587},
isbn = {978-1-4244-1756-8},
year  = {2008},
date = {2008-01-01},
booktitle = {2008 International ITG Workshop on Smart Antennas},
pages = {387--391},
publisher = {IEEE},
address = {Vienna},
abstract = {In the equalization of frequency-selective multiple-input multiple-output (MIMO) channels it is usually assumed that the length of the channel impulse response (CIR), also referred to as the channel order, is known. However, this is not true in most practical situations and, in order to avoid the serious performance degradation that occurs when the CIR length is underestimated, a channel with "more than enough" taps is usually considered. This possibly means overestimating the channel order, and is not desirable since the computational complexity of maximum likelihood sequence detection (MLSD) in frequency-selective channels grows exponentially with the channel order. In addition to that, the higher the channel order considered, the more the number of channel coefficients that need to be estimated from the same set of observations. In this paper, we introduce an algorithm for MLSD that incorporates the full estimation of the MIMO CIR parameters, including its order. The proposed technique is based on the per survivor processing (PSP) methodology, it admits both blind and semiblind implementations, depending on the availability of pilot data, and is designed to work with time-selective channels. Besides the analytical derivation of the algorithm, we provide computer simulation results that illustrate the effectiveness of the resulting receiver.},
keywords = {Channel estimation, channel impulse response, computational complexity, Computer science education, Computer Simulation, Degradation, Frequency, frequency-selective multiple-input multiple-output, maximum likelihood detection, maximum likelihood equalization, maximum likelihood estimation, maximum likelihood sequence detection, maximum likelihood sequence estimation, MIMO, MIMO channels, MIMO communication, per-survivor processing algorithm, time-selective channels, Transmitting antennas},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Leiva-Murillo2008a,
title = {Algorithms for Gaussian Bandwidth Selection in Kernel Density Estimators},
author = {Jose M Leiva-Murillo and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.researchgate.net/publication/228859873_Algorithms_for_gaussian_bandwidth_selection_in_kernel_density_estimators},
year  = {2008},
date = {2008-01-01},
booktitle = {NIPS 2008, Workshop on Optimization for Machine Learning Vancouver},
address = {Vancouver},
abstract = {In this paper we study the classical statistical problem of choos-ing an appropriate bandwidth for Kernel Density Estimators. For the special case of Gaussian kernel, two algorithms are proposed for the spherical covariance matrix and for the general case, respec-tively. These methods avoid the unsatisfactory procedure of tuning the bandwidth while evaluating the likelihood, which is impractical with multivariate data in the general case. The convergence con-ditions are provided together with the algorithms proposed. We measure the accuracy of the models obtained by a set of classifica-tion experiments.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Mariode-Prado-Cumplido2008,
title = {SVM Discovery of Causation Direction by Machine Learning Techniques},
author = {Mario Mario de-Prado-Cumplido and Antonio Art\'{e}s-Rodr\'{i}guez},
year  = {2008},
date = {2008-01-01},
booktitle = {NIPS’08, Workshop on Causality},
address = {Vancouver},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{MartinezRuiz2008,
title = {Progressive Still Image Transmission over a Tactical Data Link Network},
author = {Manuel Martinez Ruiz and Antonio Art\'{e}s-Rodr\'{i}guez and R Sabatini},
year  = {2008},
date = {2008-01-01},
booktitle = {RTO 2008 Information Systems Technology Panel (IST) Symposium},
address = {Praga},
abstract = {Future military communications will be required to provide higher data capacity and wideband in real time, greater flexibility, reliability, robustness and seamless networking capabilities. The next generation of communication systems and standards should be able to outperform in a littoral combat environment with a high density of civilian emissions and “ad-hoc” spot jammers. In this operational context it is extremely important to ensure the proper performance of the information grid and to provide not all the available but only the required information in real time either by broadcasting or upon demand, with the best possible “quality of service”. Existing tactical data link systems and standards have being designed to convey mainly textual information such as surveillance and identification data, electronic warfare parameters, aircraft control information, coded voice. The future tactical data link systems and standards should take into consideration the multimedia nature of most of the dispersed and “fuzzy” information available in the battlefield to correlate the ISR components in a way to better contribute to the Network Centric Operations. For this to be accomplished new wideband coalition waveforms should be developed and new coding and image compression standards should be taken into account, such as MPEG-7 (Multimedia Content Description Interface), MPEG-21, JPEG2000 and many others. In the meantime it is important to find new applications for the current tactical data links in order to better exploit their capabilities and to overcome or minimize their limitations.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Bravo-Santos2008,
title = {Multireception Systems in Mobile Environments},
author = {\'{A}ngel M Bravo-Santos},
year  = {2008},
date = {2008-01-01},
booktitle = {2008 International Workshop on Advances in Communications},
address = {Victoria BC},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Plata-Chaves2008,
title = {Decentralized Detection in a Dense Wireless Sensor Network with Correlated Observations},
author = {Jorge Plata-Chaves and Marcelino L\'{a}zaro and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.dcc.fc.up.pt/wits08/wits-advance-program.pdf},
year  = {2008},
date = {2008-01-01},
booktitle = {International Workshop on Information Theory for Sensor Networks (WITS 2008)},
address = {Santorini},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Santiago-Mozos2008,
title = {On the Uncertainty in Sequential Hypothesis Testing},
author = {Ricardo Santiago-Mozos and R Fernandez-Lorenzana and Fernando Perez-Cruz and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=4541223},
isbn = {978-1-4244-2002-5},
year  = {2008},
date = {2008-01-01},
booktitle = {2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro},
pages = {1223--1226},
publisher = {IEEE},
address = {Paris},
abstract = {We consider the problem of sequential hypothesis testing when the exact pdfs are not known but instead a set of iid samples are used to describe the hypotheses. We modify the classical test by introducing a likelihood ratio interval which accommodates the uncertainty in the pdfs. The test finishes when the whole likelihood ratio interval crosses one of the thresholds and reduces to the classical test as the number of samples to describe the hypotheses tend to infinity. We illustrate the performance of this test in a medical image application related to tuberculosis diagnosis. We show in this example how the test confidence level can be accurately determined.},
keywords = {binary hypothesis test, Biomedical imaging, Detectors, H infinity control, likelihood ratio, Medical diagnostic imaging, medical image application, medical image processing, Medical tests, patient diagnosis, Probability, Random variables, Sequential analysis, sequential hypothesis testing, sequential probability ratio test, Signal processing, Testing, tuberculosis diagnosis, Uncertainty},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Vila-Forcen2008,
title = {Compressive Sensing Detection of Stochastic Signals},
author = {J E Vila-Forcen and Antonio Art\'{e}s-Rodr\'{i}guez and J Garcia-Frias},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4558656},
isbn = {978-1-4244-2246-3},
year  = {2008},
date = {2008-01-01},
booktitle = {2008 42nd Annual Conference on Information Sciences and Systems},
pages = {956--960},
publisher = {IEEE},
address = {Princeton},
abstract = {Inspired by recent work in compressive sensing, we propose a framework for the detection of stochastic signals from optimized projections. In order to generate a good projection matrix, we use dimensionality reduction techniques based on the maximization of the mutual information between the projected signals and their corresponding class labels. In addition, classification techniques based on support vector machines (SVMs) are applied for the final decision process. Simulation results show that the realizations of the stochastic process are detected with higher accuracy and lower complexity than a scheme performing signal reconstruction first, followed by detection based on the reconstructed signal.},
keywords = {Additive white noise, AWGN, compressive sensing detection, dimensionality reduction techniques, Distortion measurement, Gaussian noise, matrix algebra, Mutual information, optimized projections, projection matrix, signal detection, Signal processing, signal reconstruction, Stochastic processes, stochastic signals, Support vector machine classification, Support vector machines, SVM},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Perez-Cruz2008b,
title = {Estimation of Information Theoretic Measures for Continuous Random Variables},
author = {Fernando Perez-Cruz},
url = {http://papers.nips.cc/paper/3417-estimation-of-information-theoretic-measures-for-continuous-random-variables},
year  = {2008},
date = {2008-01-01},
booktitle = {Advances in Neural Information Processing Systems},
pages = {1257--1264},
address = {Vancouver},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{Perez-Cruz2008c,
title = {Nonlinear Channel Equalization With Gaussian Processes for Regression},
author = {Fernando Perez-Cruz and Juan Jose Murillo-Fuentes and S Caro},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4563433},
issn = {1053-587X},
year  = {2008},
date = {2008-01-01},
journal = {IEEE Transactions on Signal Processing},
volume = {56},
number = {10},
pages = {5283--5286},
abstract = {We propose Gaussian processes for regression (GPR) as a novel nonlinear equalizer for digital communications receivers. GPR's main advantage, compared to previous nonlinear estimation approaches, lies on their capability to optimize the kernel hyperparameters by maximum likelihood, which improves its performance significantly for short training sequences. Besides, GPR can be understood as a nonlinear minimum mean square error estimator, a standard criterion for training equalizers that trades off the inversion of the channel and the amplification of the noise. In the experiment section, we show that the GPR-based equalizer clearly outperforms support vector machine and kernel adaline approaches, exhibiting outstanding results for short training sequences.},
keywords = {Channel estimation, digital communications receivers, equalisers, equalization, Gaussian processes, kernel adaline, least mean squares methods, maximum likelihood estimation, nonlinear channel equalization, nonlinear equalization, nonlinear minimum mean square error estimator, regression, regression analysis, short training sequences, Support vector machines},
pubstate = {published},
tppubtype = {article}
}
@article{Baca-Garcia2008,
title = {Patterns of Mental Health Service Utilization in a General Hospital and Outpatient Mental Health Facilities: Analysis of 365,262 Psychiatric Consultations},
author = {Enrique Baca-Garc\'{i}a and Mercedes M Perez-Rodriguez and Ignacio Basurte-Villamor and Javier F Quintero-Gutierrez and Juncal Sevilla-Vicente and Maria Martinez-Vigo and Antonio Art\'{e}s-Rodr\'{i}guez and Antonio Fernandez L del Moral and Miguel A Jimenez-Arriero and Jose Gonzalez L de Rivera},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17990050},
issn = {0940-1334},
year  = {2008},
date = {2008-01-01},
journal = {European archives of psychiatry and clinical neuroscience},
volume = {258},
number = {2},
pages = {117--123},
abstract = {PURPOSE: Mental health is one of the priorities of the European Commission. Studies of the use and cost of mental health facilities are needed in order to improve the planning and efficiey of mental health resources. We analyze the patterns of mental health service use in multiple clinical settings to identify factors associated with high cost. SUBJECTS AND METHODS: 22,859 patients received psychiatric care in the catchment area of a Spanish hospital (2000-2004). They had 365,262 psychiatric consultations in multiple settings. Two groups were selected that generated 80% of total costs: the medium cost group (N = 4,212; 50% of costs), and the high cost group (N = 236; 30% of costs). Statistical analyses were performed using univariate and multivariate techniques. Significant variables in univariate analyses were introduced as independent variables in a logistic regression analysis using "high cost" (\&gt;7,263$) as dependent variable. RESULTS: Costs were not evenly distributed throughout the sample. 19.4% of patients generated 80% of costs. The variables associated with high cost were: age group 1 (0-14 years) at the first evaluation, permanent disability, and ICD-10 diagnoses: Organic, including symptomatic, mental disorders; Mental and behavioural disorders due to psychoactive substance use; Schizophrenia, schizotypal and delusional disorders; Behavioural syndromes associated with physiological disturbances and physical factors; External causes of morbidity and mortality; and Factors influencing health status and contact with health services. DISCUSSION: Mental healthcare costs were not evenly distributed throughout the patient population. The highest costs are associated with early onset of the mental disorder, permanent disability, organic mental disorders, substance-related disorders, psychotic disorders, and external factors that influence the health status and contact with health services or cause morbidity and mortality. CONCLUSION: Variables related to psychiatric diagnoses and sociodemographic factors have influence on the cost of mental healthcare.},
keywords = {80 and over, Adolescent, Adult, Age Distribution, Aged, Ambulatory Care, Ambulatory Care: statistics \&amp;amp;amp; numerical data, Ambulatory Care: utilization, Child, Diagnosis-Related Groups, Female, General, General: statistics \&amp;amp;amp; numerical data, General: utilization, Health Care Costs, Health Care Costs: statistics \&amp;amp;amp; numerical data, Health Services Accessibility, Health Services Accessibility: statistics \&amp;amp;amp; numeri, Health Services Needs and Demand, Health Services Needs and Demand: statistics \&amp;amp;amp; num, Hospitals, Humans, Male, Mental Disorders, Mental Disorders: classification, Mental Disorders: diagnosis, Mental Disorders: epidemiology, Mental Disorders: therapy, Mental Health Services, Mental Health Services: economics, Mental Health Services: utilization, Middle Aged, Outcome and Process Assessment (Health Care), Preschool, Psychiatry, Psychiatry: economics, Psychiatry: statistics \&amp;amp;amp; numerical data, Sex Distribution, Spain, Spain: epidemiology, Utilization Review, Utilization Review: statistics \&amp;amp;amp; numerical data},
pubstate = {published},
tppubtype = {article}
}
@article{Perez-Cruz2008d,
title = {Digital Communication Receivers Using Gaussian Processes for Machine Learning},
author = {Fernando Perez-Cruz and Juan Jose Murillo-Fuentes},
url = {http://asp.eurasipjournals.com/content/2008/1/491503},
issn = {1687-6172},
year  = {2008},
date = {2008-01-01},
journal = {EURASIP Journal on Advances in Signal Processing},
volume = {2008},
number = {1},
pages = {1--13},
publisher = {Springer},
abstract = {We propose Gaussian processes (GPs) as a novel nonlinear receiver for digital communication systems. The GPs framework can be used to solve both classification (GPC) and regression (GPR) problems. The minimum mean squared error solution is the expectation of the transmitted symbol given the information at the receiver, which is a nonlinear function of the received symbols for discrete inputs. GPR can be presented as a nonlinear MMSE estimator and thus capable of achieving optimal performance from MMSE viewpoint. Also, the design of digital communication receivers can be viewed as a detection problem, for which GPC is specially suited as it assigns posterior probabilities to each transmitted symbol. We explore the suitability of GPs as nonlinear digital communication receivers. GPs are Bayesian machine learning tools that formulates a likelihood function for its hyperparameters, which can then be set optimally. GPs outperform state-of-the-art nonlinear machine learning approaches that prespecify their hyperparameters or rely on cross validation. We illustrate the advantages of GPs as digital communication receivers for linear and nonlinear channel models for short training sequences and compare them to state-of-the-art nonlinear machine learning tools, such as support vector machines.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Leiva-Murillo2008c,
title = {A Simulated Annealing Approach to Speaker Segmentation in Audio Databases},
author = {Jose M Leiva-Murillo and Sancho Salcedo-Sanz and Ascensi\'{o}n Gallardo-Antol\'{i}n and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.sciencedirect.com/science/article/pii/S0952197607000954},
year  = {2008},
date = {2008-01-01},
journal = {Engineering Applications of Artificial Intelligence},
volume = {21},
number = {4},
pages = {499--508},
abstract = {In this paper we present a novel approach to the problem of speaker segmentation, which is an unavoidable previous step to audio indexing. Mutual information is used for evaluating the accuracy of the segmentation, as a function to be maximized by a simulated annealing (SA) algorithm. We introduce a novel mutation operator for the SA, the Consecutive Bits Mutation operator, which improves the performance of the SA in this problem. We also use the so-called Compaction Factor, which allows the SA to operate in a reduced search space. Our algorithm has been tested in the segmentation of real audio databases, and it has been compared to several existing algorithms for speaker segmentation, obtaining very good results in the test problems considered.},
keywords = {Audio indexing, information theory, Simulated annealing, Speaker segmentation},
pubstate = {published},
tppubtype = {article}
}
@article{Vazquez2008b,
title = {Sequential Monte Carlo Methods for Complexity-Constrained MAP Equalization of Dispersive MIMO Channels},
author = {Manuel A Vazquez and Monica F Bugallo and Joaquin Miguez},
url = {http://www.sciencedirect.com/science/article/pii/S0165168407003763},
year  = {2008},
date = {2008-01-01},
journal = {Signal Processing},
volume = {88},
number = {4},
pages = {1017--1034},
abstract = {The ability to perform nearly optimal equalization of multiple input multiple output (MIMO) wireless channels using sequential Monte Carlo (SMC) techniques has recently been demonstrated. SMC methods allow to recursively approximate the a posteriori probabilities of the transmitted symbols, as observations are sequentially collected, using samples from adequate probability distributions. Hence, they are a class of online (adaptive) algorithms, suitable to handle the time-varying channels typical of high speed mobile communication applications. The main drawback of the SMC-based MIMO-channel equalizers so far proposed is that their computational complexity grows exponentially with the number of input data streams and the length of the channel impulse response, rendering these methods impractical. In this paper, we introduce novel SMC schemes that overcome this limitation by the adequate design of proposal probability distribution functions that can be sampled with a lesser computational burden, yet provide a close-to-optimal performance in terms of the resulting equalizer bit error rate and channel estimation error. We show that the complexity of the resulting receivers grows polynomially with the number of input data streams and the length of the channel response, and present computer simulation results that illustrate their performance in some typical scenarios.},
keywords = {joint channel and data estimation, Multiple Input Multiple Output (MIMO), Sequential Monte Carlo (SMC)},
pubstate = {published},
tppubtype = {article}
}
@article{Leiva-Murillo2007,
title = {Maximization of Mutual Information for Supervised Linear Feature Extraction},
author = {Jose M Leiva-Murillo and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=4298118},
issn = {1045-9227},
year  = {2007},
date = {2007-01-01},
journal = {IEEE Transactions on Neural Networks},
volume = {18},
number = {5},
pages = {1433--1441},
publisher = {IEEE},
abstract = {In this paper, we present a novel scheme for linear feature extraction in classification. The method is based on the maximization of the mutual information (MI) between the features extracted and the classes. The sum of the MI corresponding to each of the features is taken as an heuristic that approximates the MI of the whole output vector. Then, a component-by-component gradient-ascent method is proposed for the maximization of the MI, similar to the gradient-based entropy optimization used in independent component analysis (ICA). The simulation results show that not only is the method competitive when compared to existing supervised feature extraction methods in all cases studied, but it also remarkably outperform them when the data are characterized by strongly nonlinear boundaries between classes.},
keywords = {Algorithms, Artificial Intelligence, Automated, component-by-component gradient-ascent method, Computer Simulation, Data Mining, Entropy, Feature extraction, gradient methods, gradient-based entropy, Independent component analysis, Information Storage and Retrieval, information theory, Iron, learning (artificial intelligence), Linear discriminant analysis, Linear Models, Mutual information, Optimization methods, Pattern recognition, Reproducibility of Results, Sensitivity and Specificity, supervised linear feature extraction, Vectors},
pubstate = {published},
tppubtype = {article}
}
@article{FPerez18c,
title = {A Bayesian Nonparametric Approach to Discover Clinico-Genetic Associations across Cancer Types},
author = {Melanie F. Pradier and Stephanie L. Hyland and Stefan G. Stark and Kjong Lehmann and Julia E. Vogt and Fernando Perez-Cruz and Gunnar R\"{a}tsch},
doi = {https://doi.org/10.1101/623215},
keywords = {Bayesian non parametrics},
pubstate = {forthcoming},
tppubtype = {article}
}
@conference{nokey,
title = {Parameter Estimation and State Forecasting in Meteorological Models},
author = {In\'{e}s P. Mari\~{n}o and Sara P\'{e}rez-Vieites and Joaqu\'{i}n M\'{i}guez},
booktitle = {BOOK OF ABSTRACTS},
pages = {178},
note = {The 6th International Conference on Complex Networks \&
Their Applications. Nov. 29 - Dec. 01, 2017, Lyon (France)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}