@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}

}

@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{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{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}

}

@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},

booktitle = {Proc. IEEE Int. Conf. Acoustics, Speech and Signal Process.},

address = {Barcelona, Spain},

keywords = {},

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},

booktitle = {Proc. IEEE Int. Work. Machine Learning for Signal Process.},

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{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{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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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{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{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{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{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{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{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\&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{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 \& Algorithms},

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{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{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{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{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{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{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{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{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{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{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}

}