@article{MartinoRG2017,

title = {The Recycling Gibbs sampler for efficient learning},

author = {Luca Martino and Victor Elvira and Gustavo Camps-Valls},

year  = {2017},

date = {2017-01-01},

journal = {Digital Signal Processing},

volume = {74},

pages = {1 -13},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{7974876,

title = {Adaptive Importance Sampling: The past, the present, and the future},

author = {Monica F Bugallo and Victor Elvira and Luca Martino and David Luengo and Joaquin Miguez and Petar M Djuric},

year  = {2017},

date = {2017-01-01},

journal = {IEEE Signal Processing Magazine},

volume = {34},

number = {4},

pages = {60-79},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{8234658,

title = {Probabilistic MIMO Symbol Detection with Expectation Consistency Approximate Inference},

author = {Javier C\'{e}spedes and Pablo M Olmos and Matilde Sanchez-Fernandez and Fernando Perez-Cruz},

doi = {10.1109/TVT.2017.2786638},

issn = {0018-9545},

year  = {2017},

date = {2017-01-01},

journal = {IEEE Transactions on Vehicular Technology},

volume = {PP},

number = {99},

pages = {1-1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{crisan_m\'{i}guez_2017,

title = {Uniform convergence over time of a nested particle filtering scheme for recursive parameter estimation in state-space Markov models},

author = {Dan Crisan and Joaqu\'{i}n M\'{i}guez},

doi = {10.1017/apr.2017.38},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {Advances in Applied Probability},

volume = {49},

number = {4},

pages = {1170\textendash1200},

publisher = {Cambridge University Press},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{articleb,

title = {Importance sampling with transformed weights},

author = {Manuel A V\'{a}zquez and Joaqu\'{i}n M\'{i}guez},

doi = {10.1049/el.2016.3462},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {Electronics Letters},

volume = {53},

number = {12},

pages = {783-785},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pradier2016,

title = {Entropy-Constrained Scalar Quantization with a Lossy-Compressed Bit},

author = {Melanie F. Pradier and Pablo M Olmos and Fernando Perez-Cruz},

url = {http://www.mdpi.com/1099-4300/18/12/449},

doi = {10.3390/e18120449},

issn = {1099-4300},

year  = {2016},

date = {2016-12-01},

journal = {Entropy},

volume = {18},

number = {12},

pages = {449},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {We consider the compression of a continuous real-valued source X using scalar quantizers and average squared error distortion D. Using lossless compression of the quantizer's output, Gish and Pierce showed that uniform quantizing yields the smallest output entropy in the limit D → 0 , resulting in a rate penalty of 0.255 bits/sample above the Shannon Lower Bound (SLB). We present a scalar quantization scheme named lossy-bit entropy-constrained scalar quantization (Lb-ECSQ) that is able to reduce the D → 0 gap to SLB to 0.251 bits/sample by combining both lossless and binary lossy compression of the quantizer's output. We also study the low-resolution regime and show that Lb-ECSQ significantly outperforms ECSQ in the case of 1-bit quantization.},

keywords = {Journal, scalar quantization, source coding},

pubstate = {published},

tppubtype = {article}

}

@article{Vazquez2016,

title = {On the Use of the Channel Second-Order Statistics in MMSE Receivers for Time- and Frequency-Selective MIMO Transmission Systems},

author = {Manuel A V\'{a}zquez and Joaqu\'{i}n M\'{i}guez},

url = {http://jwcn.eurasipjournals.springeropen.com/articles/10.1186/s13638-016-0768-0},

doi = {10.1186/s13638-016-0768-0},

year  = {2016},

date = {2016-12-01},

journal = {EURASIP Journal on Wireless Communications and Networking},

volume = {2016},

number = {1},

publisher = {Springer International Publishing},

abstract = {Equalization of unknown frequency- and time-selective multiple input multiple output (MIMO) channels is often carried out by means of decision feedback receivers. These consist of a channel estimator and a linear filter (for the estimation of the transmitted symbols), interconnected by a feedback loop through a symbol-wise threshold detector. The linear filter is often a minimum mean square error (MMSE) filter, and its mathematical expression involves second-order statistics (SOS) of the channel, which are usually ignored by simply assuming that the channel is a known (deterministic) parameter given by an estimate thereof. This appears to be suboptimal and in this work we investigate the kind of performance gains that can be expected when the MMSE equalizer is obtained using SOS of the channel process. As a result, we demonstrate that improvements of several dBs in the signal-to-noise ratio needed to achieve a prescribed symbol error rate are possible.},

keywords = {data estimation, Joint channel, Journal, MIMO, MMSE, Second-order statistics},

pubstate = {published},

tppubtype = {article}

}

@article{gvazquez16,

title = {Multiantenna GLR Detection of Rank-One Signals with a Known Power Spectral Shape under Spatially Uncorrelated Noise},

author = {Josep Sala and Gonzalo Vazquez-Vilar and Roberto L\'{o}pez-Valcarce and Saeid Sedighi and Abbas Taherpour},

doi = {10.1109/TSP.2016.2601290},

issn = {1053-587X},

year  = {2016},

date = {2016-12-01},

journal = {IEEE Transactions on Signal Processing},

volume = {64},

number = {23},

pages = {6269-6283},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Martino2016b,

title = {Orthogonal Parallel MCMC Methods for Sampling and Optimization},

author = {Luca Martino and Victor Elvira and David Luengo and Jukka Corander and Francisco Louzada},

url = {http://www.sciencedirect.com/science/article/pii/S1051200416300987},

doi = {10.1016/j.dsp.2016.07.013},

issn = {10512004},

year  = {2016},

date = {2016-11-01},

journal = {Digital Signal Processing},

volume = {58},

pages = {64--84},

abstract = {Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In order to foster better exploration of the state space, specially in high-dimensional applications, several schemes employing multiple parallel MCMC chains have been recently introduced. In this work, we describe a novel parallel interacting MCMC scheme, called orthogonal MCMC (O-MCMC), where a set of “vertical” parallel MCMC chains share information using some “horizontal” MCMC techniques working on the entire population of current states. More specifically, the vertical chains are led by random-walk proposals, whereas the horizontal MCMC techniques employ independent proposals, thus allowing an efficient combination of global exploration and local approximation. The interaction is contained in these horizontal iterations. Within the analysis of different implementations of O-MCMC, novel schemes in order to reduce the overall computational cost of parallel multiple try Metropolis (MTM) chains are also presented. Furthermore, a modified version of O-MCMC for optimization is provided by considering parallel simulated annealing (SA) algorithms. Numerical results show the advantages of the proposed sampling scheme in terms of efficiency in the estimation, as well as robustness in terms of independence with respect to initial values and the choice of the parameters.},

keywords = {Bayesian inference, Block Independent Metropolis, Journal, Optimization, Parallel Markov Chain Monte Carlo, Parallel Multiple Try Metropolis, Parallel Simulated Annealing, Recycling samples},

pubstate = {published},

tppubtype = {article}

}

@article{Koch2016b,

title = {The Shannon Lower Bound Is Asymptotically Tight},

author = {Tobias Koch},

url = {http://ieeexplore.ieee.org/document/7556344/},

doi = {10.1109/TIT.2016.2604254},

issn = {0018-9448},

year  = {2016},

date = {2016-11-01},

journal = {IEEE Transactions on Information Theory},

volume = {62},

number = {11},

pages = {6155--6161},

abstract = {The Shannon lower bound is one of the few lower bounds on the rate-distortion function that holds for a large class of sources. In this paper, which considers exclusively norm-based difference distortion measures, it is demonstrated that its gap to the rate-distortion function vanishes as the allowed distortion tends to zero for all sources having finite differential entropy and whose integer part has finite entropy. Conversely, it is demonstrated that if the integer part of the source has infinite entropy, then its rate-distortion function is infinite for every finite distortion level. Thus, the Shannon lower bound provides an asymptotically tight bound on the rate-distortion function if, and only if, the integer part of the source has finite entropy.},

keywords = {Journal, R{\'{e}}nyi information dimension, Rate-distortion theory, Shannon lower bound},

pubstate = {published},

tppubtype = {article}

}

@article{Song2016,

title = {Canonical Correlation Analysis of High-Dimensional Data With Very Small Sample Support},

author = {Yang Song and Peter J Schreier and David Ram\'{i}rez and Tanuj Hasija},

url = {http://www.sciencedirect.com/science/article/pii/S0165168416300834},

doi = {10.1016/j.sigpro.2016.05.020},

issn = {01651684},

year  = {2016},

date = {2016-11-01},

journal = {Signal Processing},

volume = {128},

pages = {449--458},

abstract = {This paper is concerned with the analysis of correlation between two high-dimensional data sets when there are only few correlated signal components but the number of samples is very small, possibly much smaller than the dimensions of the data. In such a scenario, a principal component analysis (PCA) rank-reduction preprocessing step is commonly performed before applying canonical correlation analysis (CCA). We present simple, yet very effective, approaches to the joint model-order selection of the number of dimensions that should be retained through the PCA step and the number of correlated signals. These approaches are based on reduced-rank versions of the Bartlett\textendashLawley hypothesis test and the minimum description length information-theoretic criterion. Simulation results show that the techniques perform well for very small sample sizes even in colored noise.},

keywords = {Bartlett-Lawley statistic, Canonical correlation analysis, Journal, Model-order selection, Principal component analysis, Small sample support},

pubstate = {published},

tppubtype = {article}

}

@article{Bocharova2016,

title = {Multi-Class Source-Channel Coding},

author = {Irina E Bocharova and Albert Guill\'{e}n i F\`{a}bregas and Boris D Kudryashov and Alfonso Martinez and Adria Tauste Campo and Gonzalo Vazquez-Vilar},

url = {http://arxiv.org/abs/1410.8714},

year  = {2016},

date = {2016-09-01},

journal = {IEEE Transactions on Information Theory},

volume = {62},

number = {9},

pages = {5093 -- 5104},

abstract = {This paper studies an almost-lossless source-channel coding scheme in which source messages are assigned to different classes and encoded with a channel code that depends on the class index. The code performance is analyzed by means of random-coding error exponents and validated by simulation of a low-complexity implementation using existing source and channel codes. While each class code can be seen as a concatenation of a source code and a channel code, the overall performance improves on that of separate source-channel coding and approaches that of joint source-channel coding when the number of classes increases.},

keywords = {Channel Coding, Complexity theory, error probability, Indexes, Journal, Maximum likelihood decoding},

pubstate = {published},

tppubtype = {article}

}

@article{Durisi2016a,

title = {Towards Massive, Ultra-Reliable, and Low-Latency Wireless Communication with Short Packets},

author = {Giuseppe Durisi and Tobias Koch and Petar Popovski},

url = {http://arxiv.org/abs/1504.06526},

year  = {2016},

date = {2016-09-01},

journal = {Proceedings of the IEEE},

volume = {104},

number = {9},

pages = {1711 -- 1726},

abstract = {Most of the recent advances in the design of high-speed wireless systems are based on information-theoretic principles that demonstrate how to efficiently transmit long data packets. However, the upcoming wireless systems, notably the 5G system, will need to support novel traffic types that use short packets. For example, short packets represent the most common form of traffic generated by sensors and other devices involved in Machine-to-Machine (M2M) communications. Furthermore, there are emerging applications in which small packets are expected to carry critical information that should be received with low latency and ultra-high reliability. Current wireless systems are not designed to support short-packet transmissions. For example, the design of current systems relies on the assumption that the metadata (control information) is of negligible size compared to the actual information payload. Hence, transmitting metadata using heuristic methods does not affect the overall system performance. However, when the packets are short, metadata may be of the same size as the payload, and the conventional methods to transmit it may be highly suboptimal. In this article, we review recent advances in information theory, which provide the theoretical principles that govern the transmission of short packets. We then apply these principles to three exemplary scenarios (the two-way channel, the downlink broadcast channel, and the uplink random access channel), thereby illustrating how the transmission of control information can be optimized when the packets are short. The insights brought by these examples suggest that new principles are needed for the design of wireless protocols supporting short packets. These principles will have a direct impact on the system design.},

keywords = {finite blocklength, Journal, massive M2M communication, short packets, ultrareliable communication (URC), Wireless 5G systems},

pubstate = {published},

tppubtype = {article}

}

@article{Nazabal2016b,

title = {Human Activity Recognition by Combining a Small Number of Classifiers.},

author = {Alfredo Naz\'{a}bal and Pablo Garcia-Moreno and Antonio Art\'{e}s-Rodr\'{i}guez and Zoubin Ghahramani},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7161292},

doi = {10.1109/JBHI.2015.2458274},

issn = {2168-2208},

year  = {2016},

date = {2016-09-01},

journal = {IEEE journal of biomedical and health informatics},

volume = {20},

number = {5},

pages = {1342 -- 1351},

publisher = {IEEE},

abstract = {We consider the problem of daily Human Activity Recognition (HAR) using multiple wireless inertial sensors and, specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semi-supervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and a Markovian structure of the human activities.},

keywords = {Bayes methods, Bayesian inference, Biological system modeling, Classifier combination, Databases, Estimation, Hidden Markov models, Journal, Sensor systems},

pubstate = {published},

tppubtype = {article}

}

@article{Valera2016b,

title = {Infinite Factorial Unbounded-State Hidden Markov Model},

author = {Isabel Valera and Francisco J R Ruiz and Fernando Perez-Cruz},

url = {http://www.ncbi.nlm.nih.gov/pubmed/26571511 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true\&arnumber=7322279},

doi = {10.1109/TPAMI.2015.2498931},

issn = {1939-3539},

year  = {2016},

date = {2016-09-01},

journal = {IEEE transactions on pattern analysis and machine intelligence},

volume = {38},

number = {9},

pages = {1816 -- 1828},

abstract = {There are many scenarios in artificial intelligence, signal processing or medicine, in which a temporal sequence consists of several unknown overlapping independent causes, and we are interested in accurately recovering those canonical causes. Factorial hidden Markov models (FHMMs) present the versatility to provide a good fit to these scenarios. However, in some scenarios, the number of causes or the number of states of the FHMM cannot be known or limited a priori. In this paper, we propose an infinite factorial unbounded-state hidden Markov model (IFUHMM), in which the number of parallel hidden Markov models (HMMs) and states in each HMM are potentially unbounded. We rely on a Bayesian nonparametric (BNP) prior over integer-valued matrices, in which the columns represent the Markov chains, the rows the time indexes, and the integers the state for each chain and time instant. First, we extend the existent infinite factorial binary-state HMM to allow for any number of states. Then, we modify this model to allow for an unbounded number of states and derive an MCMC-based inference algorithm that properly deals with the trade-off between the unbounded number of states and chains. We illustrate the performance of our proposed models in the power disaggregation problem.},

keywords = {Bayes methods, Bayesian nonparametrics, CASI CAM CM, Computational modeling, GAMMA-L+ UC3M, Gibbs sampling, Hidden Markov models, Inference algorithms, Journal, Markov processes, Probability distribution, reversible jump Markov chain Monte Carlo, slice sampling, Time series, variational inference, Yttrium},

pubstate = {published},

tppubtype = {article}

}

@article{Vazquez-Vilar2016,

title = {Bayesian M-Ary Hypothesis Testing: The Meta-Converse and Verd\'{u}-Han Bounds Are Tight},

author = {Gonzalo Vazquez-Vilar and Adria Tauste Campo and Albert Guillen i Fabregas and Alfonso Martinez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7434042},

doi = {10.1109/TIT.2016.2542080},

issn = {0018-9448},

year  = {2016},

date = {2016-05-01},

journal = {IEEE Transactions on Information Theory},

volume = {62},

number = {5},

pages = {2324--2333},

abstract = {Two alternative exact characterizations of the minimum error probability of Bayesian M-ary hypothesis testing are derived. The first expression corresponds to the error probability of an induced binary hypothesis test and implies the tightness of the meta-converse bound by Polyanskiy et al.; the second expression is a function of an information-spectrum measure and implies the tightness of a generalized Verd\'{u}-Han lower bound. The formulas characterize the minimum error probability of several problems in information theory and help to identify the steps where existing converse bounds are loose.},

keywords = {Bayes methods, Channel Coding, Electronic mail, error probability, Journal, Random variables, Testing},

pubstate = {published},

tppubtype = {article}

}

@article{Miguez2016,

title = {A Proof of Uniform Convergence Over Time for a Distributed Particle Filter},

author = {Joaqu\'{i}n M\'{i}guez and Manuel A V\'{a}zquez},

url = {http://www.sciencedirect.com/science/article/pii/S0165168415004077},

doi = {10.1016/j.sigpro.2015.11.015},

issn = {01651684},

year  = {2016},

date = {2016-05-01},

journal = {Signal Processing},

volume = {122},

pages = {152--163},

abstract = {Distributed signal processing algorithms have become a hot topic during the past years. One class of algorithms that have received special attention are particles filters (PFs). However, most distributed PFs involve various heuristic or simplifying approximations and, as a consequence, classical convergence theorems for standard PFs do not hold for their distributed counterparts. In this paper, we analyze a distributed PF based on the non-proportional weight-allocation scheme of Bolic et al (2005) and prove rigorously that, under certain stability assumptions, its asymptotic convergence is guaranteed uniformly over time, in such a way that approximation errors can be kept bounded with a fixed computational budget. To illustrate the theoretical findings, we carry out computer simulations for a target tracking problem. The numerical results show that the distributed PF has a negligible performance loss (compared to a centralized filter) for this problem and enable us to empirically validate the key assumptions of the analysis.},

keywords = {Convergence analysis, Distributed algorithms, Journal, Parallelization, Particle filtering, Wireless Sensor Networks},

pubstate = {published},

tppubtype = {article}

}

@article{Koblents2016,

title = {A Nonlinear Population Monte Carlo Scheme for the Bayesian Estimation of Parameters of α-stable Distributions},

author = {Eugenia Koblents and Joaqu\'{i}n M\'{i}guez and Marco A Rodr\'{i}guez and Alexandra M Schmidt},

url = {http://www.sciencedirect.com/science/article/pii/S0167947315002340},

doi = {10.1016/j.csda.2015.09.007},

issn = {01679473},

year  = {2016},

date = {2016-03-01},

journal = {Computational Statistics \& Data Analysis},

volume = {95},

pages = {57--74},

abstract = {The class of $alpha$-stable distributions enjoys multiple practical applications in signal processing, finance, biology and other areas because it allows to describe interesting and complex data patterns, such as asymmetry or heavy tails, in contrast with the simpler and widely used Gaussian distribution. The density associated with a general $alpha$-stable distribution cannot be obtained in closed form, which hinders the process of estimating its parameters. A nonlinear population Monte Carlo (NPMC) scheme is applied in order to approximate the posterior probability distribution of the parameters of an $alpha$-stable random variable given a set of random realizations of the latter. The approximate posterior distribution is computed by way of an iterative algorithm and it consists of a collection of samples in the parameter space with associated nonlinearly-transformed importance weights. A numerical comparison of the main existing methods to estimate the $alpha$-stable parameters is provided, including the traditional frequentist techniques as well as a Markov chain Monte Carlo (MCMC) and a likelihood-free Bayesian approach. It is shown by means of computer simulations that the NPMC method outperforms the existing techniques in terms of parameter estimation error and failure rate for the whole range of values of $alpha$, including the smaller values for which most existing methods fail to work properly. Furthermore, it is shown that accurate parameter estimates can often be computed based on a low number of observations. Additionally, numerical results based on a set of real fish displacement data are provided.},

keywords = {Animal movement, Bayesian inference, Importance sampling, L{\'{e}}vy process, α-stable distributions},

pubstate = {published},

tppubtype = {article}

}

@article{Martin-Fernandez2015b,

title = {A Bayesian Method for Model Selection in Environmental Noise Prediction},

author = {L Mart\'{i}n-Fern\'{a}ndez and D P Ruiz and A J Torija and Joaquin Miguez},

url = {http://www.researchgate.net/publication/268213140_A_Bayesian_method_for_model_selection_in_environmental_noise_prediction},

issn = {1726-2135},

year  = {2016},

date = {2016-03-01},

journal = {Journal of Environmental Informatics},

volume = {27},

number = {1},

pages = {31--42},

abstract = {Environmental noise prediction and modeling are key factors for addressing a proper planning and management of urban sound environments. In this paper we propose a maximum a posteriori (MAP) method to compare nonlinear state-space models that describe the problem of predicting environmental sound levels. The numerical implementation of this method is based on particle filtering and we use a Markov chain Monte Carlo technique to improve the resampling step. In order to demonstrate the validity of the proposed approach for this particular problem, we have conducted a set of experiments where two prediction models are quantitatively compared using real noise measurement data collected in different urban areas.},

keywords = {Journal},

pubstate = {published},

tppubtype = {article}

}

@article{Martino2016a,

title = {Layered adaptive importance sampling},

author = {Luca Martino and Victor Elvira and David Luengo and Jukka Corander},

url = {http://link.springer.com/10.1007/s11222-016-9642-5},

doi = {10.1007/s11222-016-9642-5},

issn = {0960-3174},

year  = {2016},

date = {2016-03-01},

journal = {Statistics and Computing},

pages = {1--25},

publisher = {Springer US},

abstract = {Monte Carlo methods represent the de facto standard for approximating complicated integrals involving multidimensional target distributions. In order to generate random realizations from the target distribution, Monte Carlo techniques use simpler proposal probability densities to draw candidate samples. The performance of any such method is strictly related to the specification of the proposal distribution, such that unfortunate choices easily wreak havoc on the resulting estimators. In this work, we introduce a layered (i.e., hierarchical) procedure to generate samples employed within a Monte Carlo scheme. This approach ensures that an appropriate equivalent proposal density is always obtained automatically (thus eliminating the risk of a catastrophic performance), although at the expense of a moderate increase in the complexity. Furthermore, we provide a general unified importance sampling (IS) framework, where multiple proposal densities are employed and several IS schemes are introduced by applying the so-called deterministic mixture approach. Finally, given these schemes, we also propose a novel class of adaptive importance samplers using a population of proposals, where the adaptation is driven by independent parallel or interacting Markov chain Monte Carlo (MCMC) chains. The resulting algorithms efficiently combine the benefits of both IS and MCMC methods.},

keywords = {Bayesian inference Adaptive importance sampling Po, Journal},

pubstate = {published},

tppubtype = {article}

}

@article{Stinner2016,

title = {On the Waterfall Performance of Finite-Length SC-LDPC Codes Constructed From Protographs},

author = {Markus Stinner and Pablo M Olmos},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7339427},

doi = {10.1109/JSAC.2015.2504279},

issn = {0733-8716},

year  = {2016},

date = {2016-02-01},

journal = {IEEE Journal on Selected Areas in Communications},

volume = {34},

number = {2},

pages = {345--361},

abstract = {An analysis of spatially coupled low-density parity-check (SC-LDPC) codes constructed from protographs is proposed. Given the protograph used to generate the SC-LDPC code ensemble, a set of scaling parameters to characterize the average finite-length performance in the waterfall region is computed. The error performance of structured SC-LDPC code ensembles is shown to follow a scaling law similar to that of unstructured randomly constructed SC-LDPC codes. Under a finite-length perspective, some of the most relevant SC-LDPC protograph structures proposed to date are compared. The analysis reveals significant differences in their finite-length scaling behavior, which is corroborated by simulation. Spatially coupled repeat-accumulate codes present excellent finite-length performance, as they outperform in the waterfall region SC-LDPC codes of the same rate and better asymptotic thresholds.},

keywords = {Analytical models, capacity-achieving codes, Complexity theory, Couplings, Decoding, Encoding, finite-length analysis, Iterative decoding, Low-density parity-check (LDPC) codes, spatially coupled LDPC codes constructed from prot},

pubstate = {published},

tppubtype = {article}

}

@article{Valera2016ab,

title = {Infinite Continuous Feature Model for Psychiatric Comorbidity Analysis.},

author = {Isabel Valera and Francisco J R Ruiz and Pablo M Olmos and Carlos Blanco and Fernando Perez-Cruz},

url = {http://www.ncbi.nlm.nih.gov/pubmed/26654208},

doi = {10.1162/NECO_a_00805},

issn = {1530-888X},

year  = {2016},

date = {2016-02-01},

journal = {Neural computation},

volume = {28},

number = {2},

pages = {354--381},

abstract = {We aim at finding the comorbidity patterns of substance abuse, mood and personality disorders using the diagnoses from the National Epidemiologic Survey on Alcohol and Related Conditions database. To this end, we propose a novel Bayesian nonparametric latent feature model for categorical observations, based on the Indian buffet process, in which the latent variables can take values between 0 and 1. The proposed model has several interesting features for modeling psychiatric disorders. First, the latent features might be off, which allows distinguishing between the subjects who suffer a condition and those who do not. Second, the active latent features take positive values, which allows modeling the extent to which the patient has that condition. We also develop a new Markov chain Monte Carlo inference algorithm for our model that makes use of a nested expectation propagation procedure.},

keywords = {Journal},

pubstate = {published},

tppubtype = {article}

}

@article{Durisi2016b,

title = {Short-Packet Communications Over Multiple-Antenna Rayleigh-Fading Channels},

author = {Giuseppe Durisi and Tobias Koch and Johan Ostman and Yury Polyanskiy and Wei Yang},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7362178},

doi = {10.1109/TCOMM.2015.2511087},

issn = {0090-6778},

year  = {2016},

date = {2016-02-01},

journal = {IEEE Transactions on Communications},

volume = {64},

number = {2},

pages = {618--629},

publisher = {IEEE},

abstract = {Motivated by the current interest in ultra-reliable, low-latency, machine-type communication systems, we investigate the tradeoff between reliability, throughput, and latency in the transmission of information over multiple-antenna Rayleigh block-fading channels. Specifically, we obtain finite-blocklength, finite-SNR upper and lower bounds on the maximum coding rate achievable over such channels for a given constraint on the packet error probability. Numerical evidence suggests that our bounds delimit tightly the maximum coding rate already for short blocklengths (packets of about 100 symbols). Furthermore, our bounds reveal the existence of a tradeoff between the rate gain obtainable by spreading each codeword over all available time-frequency-spatial degrees of freedom, and the rate loss caused by the need of estimating the fading coefficients over these degrees of freedom. In particular, our bounds allow us to determine the optimal number of transmit antennas and the optimal number of time-frequency diversity branches that maximize the rate. Finally, we show that infinite-blocklength performance metrics such as the ergodic capacity and the outage capacity yield inaccurate throughput estimates},

keywords = {diversity branches, Encoding, ergodic capacity, Fading, fading channels, finite-blocklength information theory, finiteblocklength information theory, infinite-blocklength performance metrics, Journal, machine-type communication systems, maximum coding rate, Mission critical systems, mission-critical machine-type communications, multiple antennas, multiple-antenna Rayleigh block-fading channels, Multiplexing, optimal number, outage capacity, rate gain, Rayleigh channels, Receivers, Reliability, short-packet communications, spatial multiplexing, Throughput, Time-frequency analysis, time-frequency-spatial degrees of freedom, transmit antennas, transmit diversity, Transmitting antennas, Ultra-reliable low-latency communications},

pubstate = {published},

tppubtype = {article}

}

@article{Valera2016c,

title = {Infinite Factorial Unbounded-State Hidden Markov Model},

author = {Isabel Valera and Francisco J R Ruiz and Fernando Perez-Cruz},

url = {http://www.ncbi.nlm.nih.gov/pubmed/26571511 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true\&arnumber=7322279},

doi = {10.1109/TPAMI.2015.2498931},

issn = {1939-3539},

year  = {2016},

date = {2016-01-01},

journal = {IEEE transactions on pattern analysis and machine intelligence},

volume = {To appear},

number = {99},

pages = {1},

abstract = {There are many scenarios in artificial intelligence, signal processing or medicine, in which a temporal sequence consists of several unknown overlapping independent causes, and we are interested in accurately recovering those canonical causes. Factorial hidden Markov models (FHMMs) present the versatility to provide a good fit to these scenarios. However, in some scenarios, the number of causes or the number of states of the FHMM cannot be known or limited a priori. In this paper, we propose an infinite factorial unbounded-state hidden Markov model (IFUHMM), in which the number of parallel hidden Markov models (HMMs) and states in each HMM are potentially unbounded. We rely on a Bayesian nonparametric (BNP) prior over integer-valued matrices, in which the columns represent the Markov chains, the rows the time indexes, and the integers the state for each chain and time instant. First, we extend the existent infinite factorial binary-state HMM to allow for any number of states. Then, we modify this model to allow for an unbounded number of states and derive an MCMC-based inference algorithm that properly deals with the trade-off between the unbounded number of states and chains. We illustrate the performance of our proposed models in the power disaggregation problem.},

keywords = {Bayes methods, Bayesian nonparametrics, CASI CAM CM, Computational modeling, GAMMA-L+ UC3M, Gibbs sampling, Hidden Markov models, Inference algorithms, Markov processes, Probability distribution, reversible jump Markov chain Monte Carlo, slice sampling, Time series, variational inference, Yttrium},

pubstate = {published},

tppubtype = {article}

}

@article{Nazabal2016bb,

title = {Human Activity Recognition by Combining a Small Number of Classifiers},

author = {Alfredo Nazabal and Pablo Garcia-Moreno and Antonio Artes-Rodriguez and Zoubin Ghahramani},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7161292},

doi = {10.1109/JBHI.2015.2458274},

issn = {2168-2208},

year  = {2016},

date = {2016-01-01},

journal = {IEEE journal of biomedical and health informatics},

volume = {To appear},

publisher = {IEEE},

abstract = {We consider the problem of daily Human Activity Recognition (HAR) using multiple wireless inertial sensors and, specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semi-supervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and a Markovian structure of the human activities.},

keywords = {Bayes methods, Bayesian inference, Biological system modeling, Classifier combination, Databases, Estimation, Hidden Markov models, Sensor systems},

pubstate = {published},

tppubtype = {article}

}

@article{Borchani2016,

title = {Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers},

author = {Hanen Borchani and Pedro Larra\~{n}aga and J Gama and Concha Bielza},

url = {http://cig.fi.upm.es/node/879},

year  = {2016},

date = {2016-01-01},

journal = {Intelligent Data Analysis},

volume = {20},

keywords = {CASI CAM CM, CIG UPM, Journal},

pubstate = {published},

tppubtype = {article}

}

@article{Asheghan2016,

title = {Stability Analysis and Robust Control of Heart Beat Rate During Treadmill Exercise},

author = {Mohammad Mostafa Asheghan and Joaqu\'{i}n M\'{i}guez},

doi = {10.1016/j.automatica.2015.10.027},

issn = {00051098},

year  = {2016},

date = {2016-01-01},

journal = {Automatica},

volume = {63},

pages = {311--320},

abstract = {We investigate a nonlinear dynamical model of a human's heart beat rate (HBR) during a treadmill exercise. We begin with a rigorous analysis of the stability of the model that extends significantly the results available in the literature. In particular, we first identify a simple set of necessary and sufficient conditions for both input-state stability and Lyapunov stability of the system, and then prove that the same conditions also hold when the model parameters are subject to unknown but bounded perturbations. The second part of the paper is devoted to the design and analysis of a control structure for this model, where the treadmill speed plays the role of the control input and the output is the subject's HBR, which is intended to follow a prescribed pattern. We propose a simple control scheme, suitable for a practical implementation, and then analyze its performance. Specifically, we prove (i) that the same conditions that guarantee the stability of the system also ensure that the controller attains a desired level of performance (quantified in terms of the admissible deviation of the HBR from the prescribed profile) and (ii) that the controller is robust to bounded perturbations both in the system parameters and the control input. Numerical simulations are also presented in order to illustrate some of the theoretical results.},

keywords = {Cardiovascular system, Journal, Nonlinear systems, Robust control},

pubstate = {published},

tppubtype = {article}

}

@article{F.Pradier2016,

title = {Prior Design for Dependent Dirichlet Processes: An Application to Marathon Modeling},

author = {Melanie F. Pradier and Francisco J R Ruiz and Fernando Perez-Cruz},

editor = {Guy Brock},

url = {http://dx.plos.org/10.1371/journal.pone.0147402},

doi = {10.1371/journal.pone.0147402},

issn = {1932-6203},

year  = {2016},

date = {2016-01-01},

journal = {PLOS ONE},

volume = {11},

number = {1},

pages = {e0147402},

publisher = {Public Library of Science},

abstract = {This paper presents a novel application of Bayesian nonparametrics (BNP) for marathon data modeling. We make use of two well-known BNP priors, the single-p dependent Dirichlet process and the hierarchical Dirichlet process, in order to address two different problems. First, we study the impact of age, gender and environment on the runners' performance. We derive a fair grading method that allows direct comparison of runners regardless of their age and gender. Unlike current grading systems, our approach is based not only on top world records, but on the performances of all runners. The presented methodology for comparison of densities can be adopted in many other applications straightforwardly, providing an interesting perspective to build dependent Dirichlet processes. Second, we analyze the running patterns of the marathoners in time, obtaining information that can be valuable for training purposes. We also show that these running patterns can be used to predict finishing time given intermediate interval measurements. We apply our models to New York City, Boston and London marathons.},

keywords = {Journal},

pubstate = {published},

tppubtype = {article}

}

@article{Guzman2015,

title = {Cooperative Optical Wireless Transmission for Improving Performance in Indoor Scenarios for Visible Light Communications},

author = {Borja Genoves Guzman and Alejandro Lancho Serrano and V\'{i}ctor Gil P Jimenez},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7389772},

doi = {10.1109/TCE.2015.7389772},

issn = {0098-3063},

year  = {2015},

date = {2015-11-01},

journal = {IEEE Transactions on Consumer Electronics},

volume = {61},

number = {4},

pages = {393--401},

publisher = {IEEE},

abstract = {In this paper, a novel cooperative transmission and reception scheme in Visible Light Communications (VLC) is proposed and evaluated. This new scheme provides improvements and reliability in large indoor scenarios, such as corridors, laboratories, shops or conference rooms, where the coverage needs to be obtained by using different access points when VLC is used. The main idea behind the proposal is a simple cooperative transmission scheme where the receiver terminal will obtain the signal from different access points at the same time. This proposal outperforms traditional VLC schemes, especially in Non-Line-of-Sight reception where around 3 dB of gain, with respect to traditional schemes, can be obtained for unoptimized parameters, and larger than 3 dB could easily be achieved. The cooperation is studied in terms of the percentage of light coming from the main access point and a parameter called sidelobes??? amplitude level. The performance is evaluated according to the location within the atto-cell.},

keywords = {CoMP, Cooperative transmission andreception, Interference, Journal, Nonlinear optics, Optical receivers, Proposals, Pulse Position Division Multiplexing, Radio frequency, VLC, Wireless communication},

pubstate = {published},

tppubtype = {article}

}

@article{Ramirez2015,

title = {Detection of Multivariate Cyclostationarity},

author = {David Ram\'{i}rez and Peter J Schreier and Javier Via and Ignacio Santamaria and L L Scharf},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7134806},

doi = {10.1109/TSP.2015.2450201},

issn = {1053-587X},

year  = {2015},

date = {2015-10-01},

journal = {IEEE Transactions on Signal Processing},

volume = {63},

number = {20},

pages = {5395--5408},

publisher = {IEEE},

abstract = {This paper derives an asymptotic generalized likelihood ratio test (GLRT) and an asymptotic locally most powerful invariant test (LMPIT) for two hypothesis testing problems: 1) Is a vector-valued random process cyclostationary (CS) or is it wide-sense stationary (WSS)? 2) Is a vector-valued random process CS or is it nonstationary? Our approach uses the relationship between a scalar-valued CS time series and a vector-valued WSS time series for which the knowledge of the cycle period is required. This relationship allows us to formulate the problem as a test for the covariance structure of the observations. The covariance matrix of the observations has a block-Toeplitz structure for CS and WSS processes. By considering the asymptotic case where the covariance matrix becomes block-circulant we are able to derive its maximum likelihood (ML) estimate and thus an asymptotic GLRT. Moreover, using Wijsman's theorem, we also obtain an asymptotic LMPIT. These detectors may be expressed in terms of the Loève spectrum, the cyclic spectrum, and the power spectral density, establishing how to fuse the information in these spectra for an asymptotic GLRT and LMPIT. This goes beyond the state-of-the-art, where it is common practice to build detectors of cyclostationarity from ad-hoc functions of these spectra.},

keywords = {ad hoc function, asymptotic GLRT, asymptotic LMPIT, block circulant, block-Toeplitz structure, Correlation, covariance matrices, Covariance matrix, covariance structure, cycle period, cyclic spectrum, Cyclostationarity, Detectors, Frequency-domain analysis, generalized likelihood ratio test, generalized likelihood ratio test (GLRT), hypothesis testing problem, locally most powerful invariant test, locally most powerful invariant test (LMPIT), Loe{\&}{#}x0300, maximum likelihood estimation, multivariate cyclostationarity detection, power spectral density, random processes, s theorem, scalar valued CS time series, signal detection, spectral analysis, statistical testing, Testing, Time series, Time series analysis, Toeplitz matrices, Toeplitz matrix, ve spectrum, vector valued random process cyclostationary, vector valued WSS time series, wide sense stationary, Wijsman theorem, Wijsman{\&}{#}x2019},

pubstate = {published},

tppubtype = {article}

}

@article{Elvira2015bb,

title = {Efficient Multiple Importance Sampling Estimators},

author = {Victor Elvira and Luca Martino and David Luengo and Monica F Bugallo},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7105865},

doi = {10.1109/LSP.2015.2432078},

issn = {1070-9908},

year  = {2015},

date = {2015-10-01},

journal = {IEEE Signal Processing Letters},

volume = {22},

number = {10},

pages = {1757--1761},

publisher = {IEEE},

abstract = {Multiple importance sampling (MIS) methods use a set of proposal distributions from which samples are drawn. Each sample is then assigned an importance weight that can be obtained according to different strategies. This work is motivated by the trade-off between variance reduction and computational complexity of the different approaches (classical vs. deterministic mixture) available for the weight calculation. A new method that achieves an efficient compromise between both factors is introduced in this letter. It is based on forming a partition of the set of proposal distributions and computing the weights accordingly. Computer simulations show the excellent performance of the associated partial deterministic mixture MIS estimator.},

keywords = {Adaptive importance sampling, classical mixture approach, computational complexity, Computational efficiency, Computer Simulation, deterministic mixture, estimation theory, Journal, Monte Carlo methods, multiple importance sampling, multiple importance sampling estimator, partial deterministic mixture MIS estimator, Proposals, signal sampling, Sociology, Standards, variance reduction, weight calculation},

pubstate = {published},

tppubtype = {article}

}