@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{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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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 \& 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}

}

@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}

}