@article{reiners2018carmenes,

title = {The CARMENES search for exoplanets around M dwarfs-High-resolution optical and near-infrared spectroscopy of 324 survey stars},

author = {A Reiners and I Ribas and M Zechmeister and J A Caballero and T Trifonov and S Dreizler and JC Morales and L Tal-Or and M Lafarga and A Quirrenbach},

year  = {2018},

date = {2018-01-01},

journal = {Astronomy \& Astrophysics},

volume = {612},

pages = {A49},

publisher = {EDP Sciences},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{reale2018x,

title = {X-Ray Flare Oscillations Track Plasma Sloshing along Star-disk Magnetic Tubes in the Orion Star-forming Region},

author = {F Reale and J L\'{o}pez-Santiago and E Flaccomio and Antonino Petralia and S Sciortino},

year  = {2018},

date = {2018-01-01},

journal = {The Astrophysical Journal},

volume = {856},

number = {1},

pages = {51},

publisher = {IOP Publishing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{reiners2018carmenesb,

title = {The CARMENES search for exoplanets around M dwarfs-HD147379 b: A nearby Neptune in the temperate zone of an early-M dwarf},

author = {A Reiners and I Ribas and M Zechmeister and J A Caballero and T Trifonov and S Dreizler and JC Morales and L Tal-Or and M Lafarga and A Quirrenbach},

year  = {2018},

date = {2018-01-01},

journal = {Astronomy \& Astrophysics},

volume = {609},

pages = {L5},

publisher = {EDP Sciences},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{lopez2018use,

title = {On the use of wavelets to reveal oscillatory patterns in stellar flare emission},

author = {J L\'{o}pez-Santiago},

year  = {2018},

date = {2018-01-01},

journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},

volume = {376},

number = {2126},

pages = {20170253},

publisher = {The Royal Society Publishing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{reiners2018vizier,

title = {VizieR Online Data Catalog: 324 CARMENES M dwarfs velocities (Reiners+, 2018)},

author = {A Reiners and M Zechmeister and J A Caballero and I Ribas and JC Morales and S V Jeffers and A Kaminski},

year  = {2018},

date = {2018-01-01},

journal = {VizieR Online Data Catalog},

volume = {361},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bravi2018gaia,

title = {The Gaia-ESO Survey: a kinematical and dynamical study of four young open clusters},

author = {L Bravi and E Zari and GG Sacco and S Randich and RD Jeffries and RJ Jackson and E Franciosini and E Moraux and J L\'{o}pez-Santiago and E Pancino},

year  = {2018},

date = {2018-01-01},

journal = {Astronomy \& Astrophysics},

volume = {615},

pages = {A37},

publisher = {EDP Sciences},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{,

title = {LMPIT-inspired tests for detecting a cyclostationary signal in noise with spatio-temporal structure},

author = {Aaron Pries and David Ram\'{i}rez and Peter J Schreier},

doi = {10.1109/TWC.2018.2859314},

issn = {1536-1276},

year  = {2018},

date = {2018-00-01},

journal = {IEEE {T}rans. Wireless Comm.},

volume = {17},

number = {9},

pages = {6321--6334},

abstract = {In spectrum sensing for cognitive radio, the presence of a primary user can be detected by making use of the cyclostationarity property of digital communication signals. For the general scenario of a cyclostationary signal in temporally colored and spatially correlated noise, it has previously been shown that an asymptotic generalized likelihood ratio test (GLRT) and locally most powerful invariant test (LMPIT) exist. In this paper, we derive detectors for the presence of a cyclostationary signal in various scenarios with structured noise. In particular, we consider noise that is temporally white and/or spatially uncorrelated. Detectors that make use of this additional information about the noise process have enhanced performance. We have previously derived GLRTs for these specific scenarios; here, we examine the existence of LMPITs. We show that these exist only for detecting the presence of a cyclostationary signal in spatially uncorrelated noise. For white noise, an LMPIT does not exist. Instead, we propose tests that approximate the LMPIT, and they are shown to perform well in simulations. Finally, if the noise structure is not known in advance, we also present hypothesis tests using our framework.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bi,

title = {Combining continuous smartphone native sensors data capture and unsupervised data mining techniques for behavioral changes detection: The feasibility study of the Evidence Based Behavior (eB2) platform},

author = {Sofian Berrouiguet and David Ram\'{i}rez and Mar\'{i}a Luisa Barrig\'{o}n and P Moreno-Mu\~{n}oz and R. Carmona and Enrique Baca-Garc\'{i}a and Antonio Art\'{e}s-Rodr\'{i}guez},

doi = {10.2196/mhealth.9472},

year  = {2018},

date = {2018-00-01},

journal = {JMIR mHealth and uHealth (Special issue on Computing and Mental {H}ealth)},

volume = {6},

number = {12},

pages = {e197},

abstract = {Background: The emergence of smartphones, wearable sensor technologies, and smart homes allows the non-intrusive collection of activity data. Thus, health-related events such as Activities of Daily Living (ADLs, e.g., mobility patterns, feeding, sleeping, ...) can be captured without the patient’s active participation. We designed a system able to detect changes in the mobility patterns based on the smartphone’s native sensors and advanced machine learning and signal processing techniques. Objective: The principal objective of this work was to assess the feasibility of detecting mobility patterns changes in a sample of outpatients suffering from depression using the smartphone’s sensors. The proposed method processed the data acquired by the smartphone using an unsupervised detection technique. Method: Thirty-eight outpatients from the Hospital Fundaci\'{o}n Jim\'{e}nez D\'{i}az Psychiatry Department (Madrid, Spain) participated in the study. The eB2 app was downloaded by patients on the day of recruitment and configured with the assistance of the physician. The app captured the following data: inertial sensors, physical activity, phone calls and message logs, app usage, nearby Bluetooth and Wi-Fi connections, and location. We applied a change-point detection technique to location data on a sample of 9 outpatients recruited between April 6th, 2017 and December 14th, 2017. The change-point detection was based only on location information, but the eB2 platform allowed for an easy integration of additional data. The app remained running in the background on the patient’s smartphone during the study participation. Results: The principal outcome measure was the identification of mobility pattern changes based on an unsupervised detection technique applied to the smartphone’s native sensors data. Results from five patients’ records are presented as a case series . The eB2 system detected specific mobility pattern changes according to the patient’s activity, which may be used as indicators of behavioral and clinical state changes. Discussion: The proposed technique was able to automatically detect changes in the mobility patterns of the outpatients that took part in this study. Assuming these mobility pattern changes correlated with behavioral changes, we have developed a technique that may identify possible relapses or clinical changes. Nevertheless, it is important to point out that the detected changes are not always related to relapses and that some clinical changes cannot be detected by the proposed method.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{b_109b,

title = {Testing equality of multiple power spectral density matrices},

author = {David Ramirez and D Romero and Javier Via and Roberto L\'{o}pez-Valcarce and Ignacio Santamaria},

doi = {10.1109/TSP.2018.2875884},

issn = {1053-587X},

year  = {2018},

date = {2018-00-01},

journal = {IEEE {T}rans. Signal Process.},

volume = {66},

number = {23},

pages = {6268--6280},

abstract = {This paper studies the existence of optimal invariant detectors for determining whether P multivariate processes have the same power spectral density. This problem finds application in multiple fields, including physical layer security and cognitive radio. For Gaussian observations, we prove that the optimal invariant detector, i.e., the uniformly most powerful invariant test, does not exist. Additionally, we consider the challenging case of close hypotheses, where we study the existence of the locally most powerful invariant test (LMPIT). The LMPIT is obtained in the closed form only for univariate signals. In the multivariate case, it is shown that the LMPIT does not exist. However, the corresponding proof naturally suggests an LMPIT-inspired detector that outperforms previously proposed detectors.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{b_110,

title = {Joint detection of almost-cyclostationary signals and estimation of their cycle period},

author = {S Horstmann and David Ram\'{i}rez and Peter J Schreier},

doi = {10.1109/LSP.2018.2871961},

issn = {1070-9908},

year  = {2018},

date = {2018-00-01},

journal = {IEEE Signal Process. Lett.},

volume = {25},

number = {11},

pages = {1695--1699},

abstract = {We propose a technique that jointly detects the presence of almost-cyclostationary (ACS) signals in wide-sense stationary (WSS) noise and provides an estimate of their cycle period. Since the cycle period of an ACS process is not an integer, the approach is based on a combination of a resampling stage and a multiple hypothesis test, which deal separately with the fractional part and the integer part of the cycle period. The approach requires resampling the signal at many different rates, which is computationally expensive. For this reason we propose a filter bank structure that allows us to efficiently resample a signal at many different rates by identifying common interpolation stages among the set of resampling rates.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{8003438,

title = {Continuous Transmission of Spatially Coupled LDPC Code Chains},

author = {Pablo M Olmos and David G M Mitchell and Dimitri Truhachev and Daniel J Costello},

issn = {0090-6778},

year  = {2017},

date = {2017-12-01},

journal = {IEEE Transactions on Communications},

volume = {65},

number = {12},

pages = {5097-5109},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{JMiguez17,

title = {A comparison of Monte Carlo-based Bayesian parameter estimation methods for stochastic models of genetic networks},

author = {In\'{e}s P. Mari\~{n}o and Alexey Zaikin and Joaqu\'{i}n M\'{i}guez},

doi = {https://doi.org/10.1371/journal.pone.0182015},

year  = {2017},

date = {2017-08-10},

urldate = {2017-08-10},

journal = {PLoS ONE},

volume = {12(8)},

number = {e0182015},

keywords = {Bayesian estimation},

pubstate = {published},

tppubtype = {article}

}

@article{7880668,

title = {Probabilistic Equalization With a Smoothing Expectation Propagation Approach},

author = {Irene Santos and Juan Jos\'{e} Murillo-Fuentes and Eva Arias-de-Reyna and Pablo M Olmos},

doi = {10.1109/TWC.2017.2672746},

issn = {1536-1276},

year  = {2017},

date = {2017-05-01},

journal = {IEEE Transactions on Wireless Communications},

volume = {16},

number = {5},

pages = {2950-2962},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{VPGil_2017,

title = {Learning Mobile Communications Standards through Flexible Software Defined Radio Base Stations},

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

doi = {10.1109/MCOM.2017.1601219},

issn = {0163-6804},

year  = {2017},

date = {2017-05-01},

journal = {IEEE Communications Magazine},

volume = {55},

number = {5},

pages = {116-123},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Elvira2017,

title = {Improving Population Monte Carlo: Alternative Weighting and Resampling Schemes},

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

url = {http://www.sciencedirect.com/science/article/pii/S0165168416301633},

doi = {10.1016/j.sigpro.2016.07.012},

issn = {01651684},

year  = {2017},

date = {2017-02-01},

journal = {Signal Processing},

volume = {131},

pages = {77--91},

abstract = {Population Monte Carlo (PMC) sampling methods are powerful tools for approximating distributions of static unknowns given a set of observations. These methods are iterative in nature: at each step they generate samples from a proposal distribution and assign them weights according to the importance sampling principle. Critical issues in applying PMC methods are the choice of the generating functions for the samples and the avoidance of the sample degeneracy. In this paper, we propose three new schemes that considerably improve the performance of the original PMC formulation by allowing for better exploration of the space of unknowns and by selecting more adequately the surviving samples. A theoretical analysis is performed, proving the superiority of the novel schemes in terms of variance of the associated estimators and preservation of the sample diversity. Furthermore, we show that they outperform other state of the art algorithms (both in terms of mean square error and robustness w.r.t. initialization) through extensive numerical simulations.},

keywords = {Adaptive importance sampling, Journal, population Monte Carlo, Proposal distribution, Resampling},

pubstate = {published},

tppubtype = {article}

}

@article{Vazquez2017,

title = {A Robust Scheme for Distributed Particle Filtering in Wireless Sensors Networks},

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

url = {http://www.sciencedirect.com/science/article/pii/S016516841630189X},

doi = {10.1016/j.sigpro.2016.08.003},

issn = {01651684},

year  = {2017},

date = {2017-02-01},

journal = {Signal Processing},

volume = {131},

pages = {190--201},

abstract = {Wireless sensor networks (WSNs) have become a popular technology for a broad range of applications where the goal is to track and forecast the evolution of time-varying physical magnitudes. Several authors have investigated the use of particle filters (PFs) in this scenario. PFs are very flexible, Monte Carlo based algorithms for tracking and prediction in state-space dynamical models. However, to implement a PF in a WSN, the algorithm should run over different nodes in the network to produce estimators based on locally collected data. These local estimators then need to be combined so as to produce a global estimator. Existing approaches to the problem are either heuristic or well-principled but impractical (as they impose stringent conditions on the WSN communication capacity). Here, we introduce a novel distributed PF that relies on the computation of median posterior probability distributions in order to combine local Bayesian estimators (obtained at different nodes) in a way that is efficient, both computation and communication-wise. An extensive simulation study for a target tracking problem shows that the proposed scheme is competitive with existing consensus-based distributed PFs in terms of estimation accuracy, while it clearly outperforms these methods in terms of robustness and communication requirements.},

keywords = {Distributed particle filtering (DPF), Journal, Median posterior, Robust statistics, Sequential Monte Carlo Methods (SMC), Wireless sensors networks (WSNs)},

pubstate = {published},

tppubtype = {article}

}

@article{Yiu2017,

title = {Wireless RSSI Fingerprinting Localization},

author = {Simon Yiu and Marzieh Dashti and Holger Claussen and Fernando Perez-Cruz},

doi = {10.1016/j.sigpro.2016.07.005},

issn = {01651684},

year  = {2017},

date = {2017-02-01},

journal = {Signal Processing},

volume = {131},

pages = {235--244},

abstract = {Localization has attracted a lot of research effort in the last decade due to the explosion of location based service (LBS). In particular, wireless fingerprinting localization has received much attention due to its simplicity and compatibility with existing hardware. In this work, we take a closer look at the underlying aspects of wireless fingerprinting localization. First, we review the various methods to create a radiomap. In particular, we look at the traditional fingerprinting method which is based purely on measurements, the parametric pathloss regression model and the non-parametric Gaussian Process (GP) regression model. Then, based on these three methods and measurements from a real world deployment, the various aspects such as the density of access points (APs) and impact of an outdated signature map which affect the performance of fingerprinting localization are examined. At the end of the paper, the audiences should have a better understanding of what to expect from fingerprinting localization in a real world deployment.},

keywords = {Fingerprinting localization, Gaussian Process, Journal, Location-based service (LBS), Machine learning, Non-parametric model, Pathloss model, Received signal strength indicator (RSSI)},

pubstate = {published},

tppubtype = {article}

}

@article{Santos2017,

title = {Expectation Propagation as Turbo Equalizer in ISI Channels},

author = {Irene Santos and Juan Jose Murillo-Fuentes and Rafael Boloix-Tortosa and Eva Arias-de-Reyna and Pablo M Olmos},

url = {http://ieeexplore.ieee.org/document/7587428/},

doi = {10.1109/TCOMM.2016.2616141},

issn = {0090-6778},

year  = {2017},

date = {2017-01-01},

journal = {IEEE Transactions on Communications},

volume = {65},

number = {1},

pages = {360--370},

abstract = {In probabilistic equalization of channels with inter-symbol interference, the BCJR algorithm and its approximations become intractable for high-order modulations, even for moderate channel dispersions. In this paper, we introduce a novel soft equalizer to approximate the symbol a posteriori probabilities (APP), where the expectation propagation (EP) algorithm is used to provide an accurate estimation. This new soft equalizer is presented as a block solution, denoted as block-EP (BEP), where the structure of the matrices involved is exploited to reduce the complexity order to O(LN2) , i.e., linear in the length of the channel, L , and quadratic in the frame length, N . The solution is presented in complex-valued formulation within a turbo equalization scheme. This algorithm can be cast as a linear minimum-mean-squared-error (LMMSE) turbo equalization with double feedback architecture, where constellations being discrete is a restriction exploited by the EP that provides a first refinement of the APP. In the experiments included, the BEP exhibits a robust performance, regardless of the channel response, with gains in the range 1.5\textendash5 dB compared with the LMMSE equalization.},

keywords = {BCJR, complex-valued, Expectation propagation (EP), ISI, Journal, turbo equalization},

pubstate = {published},

tppubtype = {article}

}

@article{Martino2017,

title = {Cooperative Parallel Particle Filters for Online Model Selection and Applications to Urban Mobility},

author = {Luca Martino and Jesse Read and Victor Elvira and Francisco Louzada},

url = {http://www.sciencedirect.com/science/article/pii/S1051200416301610},

doi = {10.1016/j.dsp.2016.09.011},

issn = {10512004},

year  = {2017},

date = {2017-01-01},

journal = {Digital Signal Processing},

volume = {60},

pages = {172--185},

abstract = {We design a sequential Monte Carlo scheme for the dual purpose of Bayesian inference and model selection. We consider the application context of urban mobility, where several modalities of transport and different measurement devices can be employed. Therefore, we address the joint problem of online tracking and detection of the current modality. For this purpose, we use interacting parallel particle filters, each one addressing a different model. They cooperate for providing a global estimator of the variable of interest and, at the same time, an approximation of the posterior density of each model given the data. The interaction occurs by a parsimonious distribution of the computational effort, with online adaptation for the number of particles of each filter according to the posterior probability of the corresponding model. The resulting scheme is simple and flexible. We have tested the novel technique in different numerical experiments with artificial and real data, which confirm the robustness of the proposed scheme.},

keywords = {Distributed inference, Journal, Marginal likelihood estimation, Modality detection, Parallel particle filters, Sequential model selection, Urban mobility},

pubstate = {published},

tppubtype = {article}

}

@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}

}