@article{Perez-Cruz2010,

title = {Robust and Low Complexity Distributed Kernel Least Squares Learning in Sensor Networks},

author = {Fernando Perez-Cruz and S R Kulkarni},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5395679},

issn = {1070-9908},

year  = {2010},

date = {2010-01-01},

journal = {IEEE Signal Processing Letters},

volume = {17},

number = {4},

pages = {355--358},

abstract = {We present a novel mechanism for consensus building in sensor networks. The proposed algorithm has three main properties that make it suitable for sensor network learning. First, the proposed algorithm is based on robust nonparametric statistics and thereby needs little prior knowledge about the network and the function that needs to be estimated. Second, the algorithm uses only local information about the network and it communicates only with nearby sensors. Third, the algorithm is completely asynchronous and robust. It does not need to coordinate the sensors to estimate the underlying function and it is not affected if other sensors in the network stop working. Therefore, the proposed algorithm is an ideal candidate for sensor networks deployed in remote and inaccessible areas, which might need to change their objective once they have been set up.},

keywords = {communication complexity, Consensus, distributed learning, kernel methods, learning (artificial intelligence), low complexity distributed kernel least squares le, message passing, message-passing algorithms, robust nonparametric statistics, sensor network learning, sensor networks, telecommunication computing, Wireless Sensor Networks},

pubstate = {published},

tppubtype = {article}

}

@article{Carballo2010,

title = {Stability of Childhood Anxiety Disorder Diagnoses: a Follow-Up Naturalistic Study in Psychiatric Care},

author = {Juan J Carballo and Enrique Baca-Garc\'{i}a and Carlos Blanco and Mercedes M Perez-Rodriguez and Miguel A Jimenez-Arriero and Antonio Art\'{e}s-Rodr\'{i}guez and Moira Rynn and David Shaffer and Maria A Oquendo},

url = {http://www.ncbi.nlm.nih.gov/pubmed/19826859},

issn = {1435-165X},

year  = {2010},

date = {2010-01-01},

journal = {European child \& adolescent psychiatry},

volume = {19},

number = {4},

pages = {395--403},

abstract = {Few studies have examined the stability of major psychiatric disorders in pediatric psychiatric clinical populations. The objective of this study was to examine the long-term stability of anxiety diagnoses starting with pre-school age children through adolescence evaluated at multiple time points. Prospective cohort study was conducted of all children and adolescents receiving psychiatric care at all pediatric psychiatric clinics belonging to two catchment areas in Madrid, Spain, between 1 January, 1992 and 30 April, 2006. Patients were selected from among 24,163 children and adolescents who received psychiatric care. Patients had to have a diagnosis of an ICD-10 anxiety disorder during at least one of the consultations and had to have received psychiatric care for the anxiety disorder. We grouped anxiety disorder diagnoses according to the following categories: phobic disorders, social anxiety disorders, obsessive-compulsive disorder (OCD), stress-related disorders, and \"{o}ther" anxiety disorders which, among others, included generalized anxiety disorder, and panic disorder. Complementary indices of diagnostic stability were calculated. As much as 1,869 subjects were included and had 27,945 psychiatric/psychological consultations. The stability of all ICD-10 anxiety disorder categories studied was high regardless of the measure of diagnostic stability used. Phobic and social anxiety disorders showed the highest diagnostic stability, whereas OCD and \"{o}ther" anxiety disorders showed the lowest diagnostic stability. No significant sex differences were observed on the diagnostic stability of the anxiety disorder categories studied. Diagnostic stability measures for phobic, social anxiety, and \"{o}ther" anxiety disorder diagnoses varied depending on the age at first evaluation. In this clinical pediatric outpatient sample it appears that phobic, social anxiety, and stress-related disorder diagnoses in children and adolescents treated in community outpatient services may have high diagnostic stability.},

keywords = {Adolescent, Ambulatory Care, Ambulatory Care: utilization, Anxiety Disorders, Anxiety Disorders: diagnosis, Anxiety Disorders: epidemiology, Catchment Area (Health), Child, Cohort Studies, Female, Follow-Up Studies, Humans, International Classification of Diseases, Male, Mental Health Services, Mental Health Services: utilization, Preschool, Prospective Studies, Severity of Illness Index, Spain, Spain: epidemiology},

pubstate = {published},

tppubtype = {article}

}

@article{Delgado-Gomez2010,

title = {Individual Identification Using Personality Traits},

author = {David Delgado-G\'{o}mez and Federico Sukno and David Aguado and Carlos Santacruz and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://www.sciencedirect.com/science/article/pii/S1084804509001453},

issn = {10848045},

year  = {2010},

date = {2010-01-01},

journal = {Journal of Network and Computer Applications},

volume = {33},

number = {3},

pages = {293--299},

abstract = {In this article, a pioneer study is conducted to evaluate the possibility of identifying people through their personality traits. The study is conducted using the answers of a population of 734 individuals to a collection of 206 items. These items aim at measuring five common different personality traits usually called the big five. These five levels are neuroticism, extraversion, agreeableness, conscientiousness and openness. The traits are estimated using the widely used Samejima's model and then used to discriminate the individuals. Results point biometrics using personality traits as a new promising biometric modality.},

keywords = {Biometrics, Personality traits, Psychometrics, Samejima's model},

pubstate = {published},

tppubtype = {article}

}

@article{Koch2010a,

title = {Gaussian Fading Is the Worst Fading},

author = {Tobias Koch and Amos Lapidoth},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5429105},

issn = {0018-9448},

year  = {2010},

date = {2010-01-01},

journal = {IEEE Transactions on Information Theory},

volume = {56},

number = {3},

pages = {1158--1165},

abstract = {The capacity of peak-power limited, single-antenna, noncoherent, flat-fading channels with memory is considered. The emphasis is on the capacity pre-log, i.e., on the limiting ratio of channel capacity to the logarithm of the signal-to-noise ratio (SNR), as the SNR tends to infinity. It is shown that, among all stationary and ergodic fading processes of a given spectral distribution function and whose law has no mass point at zero, the Gaussian process gives rise to the smallest pre-log. The assumption that the law of the fading process has no mass point at zero is essential in the sense that there exist stationary and ergodic fading processes whose law has a mass point at zero and that give rise to a smaller pre-log than the Gaussian process of equal spectral distribution function. An extension of these results to multiple-input single-output (MISO) fading channels with memory is also presented.},

keywords = {Additive noise, channel capacity, channels with memory, Distribution functions, ergodic fading processes, Fading, fading channels, flat fading, flat-fading channel capacity, Gaussian channels, Gaussian fading, Gaussian processes, H infinity control, high signal-to-noise ratio (SNR), Information technology, information theory, multiple-input single-output fading channels, multiplexing gain, noncoherent, noncoherent channel capacity, peak-power limited channel capacity, Signal to noise ratio, signal-to-noise ratio, single-antenna channel capacity, spectral distribution function, time-selective, Transmitters},

pubstate = {published},

tppubtype = {article}

}

@article{Martino2010a,

title = {Generalized Rejection Sampling Schemes and Applications in Signal Processing},

author = {Luca Martino and Joaquin Miguez},

url = {http://www.sciencedirect.com/science/article/pii/S0165168410001866},

year  = {2010},

date = {2010-01-01},

journal = {Signal Processing},

volume = {90},

number = {11},

pages = {2981--2995},

abstract = {Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques, such as Markov chain Monte Carlo (MCMC) and particle filters, have become very popular in signal processing over the last years. However, in many problems of practical interest these techniques demand procedures for sampling from probability distributions with non-standard forms, hence we are often brought back to the consideration of fundamental simulation algorithms, such as rejection sampling (RS). Unfortunately, the use of RS techniques demands the calculation of tight upper bounds for the ratio of the target probability density function (pdf) over the proposal density from which candidate samples are drawn. Except for the class of log-concave target pdf's, for which an efficient algorithm exists, there are no general methods to analytically determine this bound, which has to be derived from scratch for each specific case. In this paper, we introduce new schemes for (a) obtaining upper bounds for likelihood functions and (b) adaptively computing proposal densities that approximate the target pdf closely. The former class of methods provides the tools to easily sample from a posteriori probability distributions (that appear very often in signal processing problems) by drawing candidates from the prior distribution. However, they are even more useful when they are exploited to derive the generalized adaptive RS (GARS) algorithm introduced in the second part of the paper. The proposed GARS method yields a sequence of proposal densities that converge towards the target pdf and enable a very efficient sampling of a broad class of probability distributions, possibly with multiple modes and non-standard forms. We provide some simple numerical examples to illustrate the use of the proposed techniques, including an example of target localization using range measurements, often encountered in sensor network applications.},

keywords = {Adaptive rejection sampling, Gibbs sampling, Monte Carlo integration, Rejection sampling, sensor networks, Target localization},

pubstate = {published},

tppubtype = {article}

}

@article{Djuric2010a,

title = {Assessment of Nonlinear Dynamic Models by Kolmogorov\textendashSmirnov Statistics},

author = {Petar M Djuric and Joaquin Miguez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5491124},

issn = {1053-587X},

year  = {2010},

date = {2010-01-01},

journal = {IEEE Transactions on Signal Processing},

volume = {58},

number = {10},

pages = {5069--5079},

abstract = {Model assessment is a fundamental problem in science and engineering and it addresses the question of the validity of a model in the light of empirical evidence. In this paper, we propose a method for the assessment of dynamic nonlinear models based on empirical and predictive cumulative distributions of data and the Kolmogorov-Smirnov statistics. The technique is based on the generation of discrete random variables that come from a known discrete distribution if the entertained model is correct. We provide simulation examples that demonstrate the performance of the proposed method.},

keywords = {Cumulative distributions, discrete random variables, dynamic nonlinear models, Electrical capacitance tomography, Filtering, filtering theory, Iron, Kolmogorov-Smirnov statistics, Kolomogorov\textendashSmirnov statistics, model assessment, nonlinear dynamic models, nonlinear dynamical systems, Permission, predictive cumulative distributions, predictive distributions, Predictive models, Random variables, Robots, statistical analysis, statistical distributions, statistics, Telecommunication control},

pubstate = {published},

tppubtype = {article}

}

@article{Perez-Cruz2010a,

title = {MIMO Gaussian Channels With Arbitrary Inputs: Optimal Precoding and Power Allocation},

author = {Fernando Perez-Cruz and Miguel R D Rodrigues and Sergio Verdu},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5429131},

issn = {0018-9448},

year  = {2010},

date = {2010-01-01},

journal = {IEEE Transactions on Information Theory},

volume = {56},

number = {3},

pages = {1070--1084},

abstract = {In this paper, we investigate the linear precoding and power allocation policies that maximize the mutual information for general multiple-input-multiple-output (MIMO) Gaussian channels with arbitrary input distributions, by capitalizing on the relationship between mutual information and minimum mean-square error (MMSE). The optimal linear precoder satisfies a fixed-point equation as a function of the channel and the input constellation. For non-Gaussian inputs, a nondiagonal precoding matrix in general increases the information transmission rate, even for parallel noninteracting channels. Whenever precoding is precluded, the optimal power allocation policy also satisfies a fixed-point equation; we put forth a generalization of the mercury/waterfilling algorithm, previously proposed for parallel noninterfering channels, in which the mercury level accounts not only for the non-Gaussian input distributions, but also for the interference among inputs.},

keywords = {Collaborative work, Equations, fixed-point equation, Gaussian channels, Gaussian noise channels, Gaussian processes, Government, Interference, linear precoding, matrix algebra, mean square error methods, mercury-waterfilling algorithm, MIMO, MIMO communication, MIMO Gaussian channel, minimum mean-square error, minimum mean-square error (MMSE), multiple-input-multiple-output channel, multiple-input\textendashmultiple-output (MIMO) systems, Mutual information, nondiagonal precoding matrix, optimal linear precoder, optimal power allocation policy, optimal precoding, optimum power allocation, Phase shift keying, precoding, Quadrature amplitude modulation, Telecommunications, waterfilling},

pubstate = {published},

tppubtype = {article}

}

@article{Olmos2010a,

title = {Joint Nonlinear Channel Equalization and Soft LDPC Decoding with Gaussian Processes},

author = {Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5290078},

issn = {1053-587X},

year  = {2010},

date = {2010-01-01},

journal = {IEEE Transactions on Signal Processing},

volume = {58},

number = {3},

pages = {1183--1192},

abstract = {In this paper, we introduce a new approach for nonlinear equalization based on Gaussian processes for classification (GPC). We propose to measure the performance of this equalizer after a low-density parity-check channel decoder has detected the received sequence. Typically, most channel equalizers concentrate on reducing the bit error rate, instead of providing accurate posterior probability estimates. We show that the accuracy of these estimates is essential for optimal performance of the channel decoder and that the error rate output by the equalizer might be irrelevant to understand the performance of the overall communication receiver. In this sense, GPC is a Bayesian nonlinear classification tool that provides accurate posterior probability estimates with short training sequences. In the experimental section, we compare the proposed GPC-based equalizer with state-of-the-art solutions to illustrate its improved performance.},

keywords = {Bayesian nonlinear classification tool, Bit error rate, Channel Coding, channel equalizers, Channel estimation, Coding, equalisers, equalization, error statistics, Gaussian processes, GPC, joint nonlinear channel equalization, low-density parity-check (LDPC), low-density parity-check channel decoder, Machine learning, nonlinear channel, nonlinear codes, parity check codes, posterior probability estimates, soft LDPC decoding, soft-decoding, support vector machine (SVM)},

pubstate = {published},

tppubtype = {article}

}

@article{Koch2010b,

title = {On Multipath Fading Channels at High SNR},

author = {Tobias Koch and Amos Lapidoth},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5625630},

issn = {0018-9448},

year  = {2010},

date = {2010-01-01},

journal = {IEEE Transactions on Information Theory},

volume = {56},

number = {12},

pages = {5945--5957},

abstract = {A noncoherent multipath fading channel is considered, where neither the transmitter nor the receiver is cognizant of the realization of the path gains, but both are cognizant of their statistics. It is shown that if the delay spread is large in the sense that the variances of the path gains decay exponentially or slower, then capacity is bounded in the signal-to-noise ratio (SNR). For such channels, capacity does not tend to infinity as the SNR tends to infinity. In contrast, if the variances of the path gains decay faster than exponentially, then capacity is unbounded in the SNR. It is further demonstrated that if the number of paths is finite, then at high SNR capacity grows double-logarithmically with the SNR, and the capacity pre-loglog-defined as the limiting ratio of capacity to loglog(SNR) as the SNR tends to infinity-is 1 irrespective of the number of paths. The results demonstrate that at high SNR multipath fading channels with an infinite number of paths cannot be approximated by multipath fading channels with only a finite number of paths. The number of paths that are needed to approximate a multipath fading channel typically depends on the SNR and may grow to infinity as the SNR tends to infinity.},

keywords = {approximation theory, capacity pre-loglog, capacity to loglog, channel capacity, channels with memory, Delay, Fading, fading channels, frequency-selective fading, high signal-to-noise ratio, high SNR, Limiting, multipath, multipath channels, noncoherent, noncoherent multipath fading channel, Receivers, Signal to noise ratio, signal-to-noise ratio, Transmitters},

pubstate = {published},

tppubtype = {article}

}

@article{Fresia2010,

title = {Joint Source and Channel Coding},

author = {Maria Fresia and Fernando Perez-Cruz and Vincent H Poor and Sergio Verdu},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5563107},

issn = {1053-5888},

year  = {2010},

date = {2010-01-01},

journal = {IEEE Signal Processing Magazine},

volume = {27},

number = {6},

pages = {104--113},

abstract = {The objectives of this article are two-fold: First, to present the problem of joint source and channel (JSC) coding from a graphical model perspective and second, to propose a structure that uses a new graphical model for jointly encoding and decoding a redundant source. In the first part of the article, relevant contributions to JSC coding, ranging from the Slepian-Wolf problem to joint decoding of variable length codes with state-of-the-art source codes, are reviewed and summarized. In the second part, a double low-density parity-check (LDPC) code for JSC coding is proposed. The double LDPC code can be decoded as a single bipartite graph using standard belief propagation (BP) and its limiting performance is analyzed by using extrinsic information transfer (EXIT) chart approximations.},

keywords = {belief propagation, Channel Coding, combined source-channel coding, Decoding, Encoding, graphical model, Hidden Markov models, Iterative decoding, joint source channel coding, JSC coding, LDPC code, low density parity check code, Markov processes, parity check codes, Slepian-Wolf problem, variable length codes},

pubstate = {published},

tppubtype = {article}

}

@article{Martino2010b,

title = {A Generalization of the Adaptive Rejection Sampling Algorithm},

author = {Luca Martino and Joaquin Miguez},

url = {http://link.springer.com/10.1007/s11222-010-9197-9},

issn = {0960-3174},

year  = {2010},

date = {2010-01-01},

journal = {Statistics and Computing},

volume = {21},

number = {4},

pages = {633--647},

abstract = {Rejection sampling is a well-known method to generate random samples from arbitrary target probability distributions. It demands the design of a suitable proposal probability density function (pdf) from which candidate samples can be drawn. These samples are either accepted or rejected depending on a test involving the ratio of the target and proposal densities. The adaptive rejection sampling method is an efficient algorithm to sample from a log-concave target density, that attains high acceptance rates by improving the proposal density whenever a sample is rejected. In this paper we introduce a generalized adaptive rejection sampling procedure that can be applied with a broad class of target probability distributions, possibly non-log-concave and exhibiting multiple modes. The proposed technique yields a sequence of proposal densities that converge toward the target pdf, thus achieving very high acceptance rates. We provide a simple numerical example to illustrate the basic use of the proposed technique, together with a more elaborate positioning application using real data.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Zoubir2010,

title = {Analysis of a Sequential Monte Carlo Method for Optimization in Dynamical Systems},

author = {A Zoubir and M Viberg and B Yang and Joaquin Miguez},

url = {http://www.sciencedirect.com/science/article/pii/S0165168409004708},

year  = {2010},

date = {2010-01-01},

journal = {Signal Processing},

volume = {90},

number = {5},

pages = {1609--1622},

abstract = {We investigate a recently proposed sequential Monte Carlo methodology for recursively tracking the minima of a cost function that evolves with time. These methods, subsequently referred to as sequential Monte Carlo minimization (SMCM) procedures, have an algorithmic structure similar to particle filters: they involve the generation of random paths in the space of the signal of interest (SoI), the stochastic selection of the fittest paths and the ranking of the survivors according to their cost. In this paper, we propose an extension of the original SMCM methodology (that makes it applicable to a broader class of cost functions) and introduce an asymptotic-convergence analysis. Our analytical results are based on simple induction arguments and show how the SoI-estimates computed by a SMCM algorithm converge, in probability, to a sequence of minimizers of the cost function. We illustrate these results by means of two computer simulation examples.},

keywords = {Dynamic optimization, Nonlinear dynamics, Nonlinear tracking, Sequential Monte Carlo, Stochastic optimization},

pubstate = {published},

tppubtype = {article}

}

@article{Murillo-Fuentes2009,

title = {Gaussian Process Regressors for Multiuser Detection in DS-CDMA Systems},

author = {Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5201027},

issn = {0090-6778},

year  = {2009},

date = {2009-01-01},

journal = {IEEE Transactions on Communications},

volume = {57},

number = {8},

pages = {2339--2347},

abstract = {In this paper we present Gaussian processes for Regression (GPR) as a novel detector for CDMA digital communications. Particularly, we propose GPR for constructing analytical nonlinear multiuser detectors in CDMA communication systems. GPR can easily compute the parameters that describe its nonlinearities by maximum likelihood. Thereby, no cross-validation is needed, as it is typically used in nonlinear estimation procedures. The GPR solution is analytical, given its parameters, and it does not need to solve an optimization problem for building the nonlinear estimator. These properties provide fast and accurate learning, two major issues in digital communications. The GPR with a linear decision function can be understood as a regularized MMSE detector, in which the regularization parameter is optimally set. We also show the GPR receiver to be a straightforward nonlinear extension of the linear minimum mean square error (MMSE) criterion, widely used in the design of these receivers. We argue the benefits of this new approach in short codes CDMA systems where little information on the users' codes, users' amplitudes or the channel is available. The paper includes some experiments to show that GPR outperforms linear (MMSE) and nonlinear (SVM) state-ofthe- art solutions.},

keywords = {analytical nonlinear multiuser detectors, code division multiple access, communication systems, Detectors, digital communication, digital communications, DS-CDMA systems, Gaussian process for regressi, Gaussian process regressors, Gaussian processes, GPR, Ground penetrating radar, least mean squares methods, maximum likelihood, maximum likelihood detection, maximum likelihood estimation, mean square error methods, minimum mean square error, MMSE, Multiaccess communication, Multiuser detection, nonlinear estimator, nonlinear state-ofthe- art solutions, radio receivers, Receivers, regression analysis, Support vector machines},

pubstate = {published},

tppubtype = {article}

}

@article{Marino2009,

title = {Monte Carlo Method for Adaptively Estimating the Unknown Parameters and the Dynamic State of Chaotic Systems},

author = {In\'{e}s P. Mari\~{n}o and Joaquin Miguez and Riccardo Meucci},

url = {http://link.aps.org/doi/10.1103/PhysRevE.79.056218},

issn = {1539-3755},

year  = {2009},

date = {2009-01-01},

journal = {Physical Review E},

volume = {79},

number = {5},

pages = {056218},

publisher = {American Physical Society},

abstract = {We propose a Monte Carlo methodology for the joint estimation of unobserved dynamic variables and unknown static parameters in chaotic systems. The technique is sequential, i.e., it updates the variable and parameter estimates recursively as new observations become available, and, hence, suitable for online implementation. We demonstrate the validity of the method by way of two examples. In the first one, we tackle the estimation of all the dynamic variables and one unknown parameter of a five-dimensional nonlinear model using a time series of scalar observations experimentally collected from a chaotic CO2\<math display="inline"\>\<mrow\>\<msub\>\<mrow\>\<mtext\>CO\<mn\>2 laser. In the second example, we address the estimation of the two dynamic variables and the phase parameter of a numerical model commonly employed to represent the dynamics of optoelectronic feedback loops designed for chaotic communications over fiber-optic links.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Vazquez2009,

title = {Maximum-Likelihood Sequence Detection in Time- and Frequency-Selective MIMO Channels With Unknown Order},

author = {Manuel A Vazquez and Joaquin Miguez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4510724},

issn = {0018-9545},

year  = {2009},

date = {2009-01-01},

journal = {IEEE Transactions on Vehicular Technology},

volume = {58},

number = {1},

pages = {499--504},

abstract = {In the equalization of frequency-selective multiple-input-multiple-output (MIMO) channels, it is usually assumed that the length of the channel impulse response (CIR), which is also referred to as the channel order, is known. However, this is not true in most practical situations, and it is a common approach to overestimate the channel order to avoid the serious performance degradation that occurs when the CIR length is underestimated. Unfortunately, the computational complexity of maximum-likelihood sequence detection (MLSD) in frequency-selective channels exponentially grows with the channel order; hence, overestimation can actually be undesirable because it leads to more expensive and inefficient receivers. In this paper, we introduce an algorithm for MLSD that incorporates the full estimation of the MIMO CIR parameters, including its order. The proposed technique is based on the per-survivor processing (PSP) methodology; it admits both blind and semiblind implementations, depending on the availability of pilot data, and is designed to work with time-selective channels. In addition to the analytical derivation of the algorithm, we provide computer simulation results that illustrate the effectiveness of the resulting receiver.},

keywords = {channel impulse response, channel order estimation, CIR, frequency-selective multiple-input-multiple-output, joint channel and data estimation, maximum likelihood detection, maximum-likelihood sequence detection, MIMO channels, MIMO communication, MLSD, Multiple Input Multiple Output (MIMO), multiple-input\textendashmultiple-output (MIMO), per-survivor processing, per-survivor processing (PSP), telecommunication channels, time-selective multiple-input-multiple-output chan},

pubstate = {published},

tppubtype = {article}

}

@article{Koch2009,

title = {Channels That Heat Up},

author = {Tobias Koch and Amos Lapidoth and Paul P Sotiriadis},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5165190},

issn = {0018-9448},

year  = {2009},

date = {2009-01-01},

journal = {IEEE Transactions on Information Theory},

volume = {55},

number = {8},

pages = {3594--3612},

abstract = {This paper considers an additive noise channel where the time-A; noise variance is a weighted sum of the squared magnitudes of the previous channel inputs plus a constant. This channel model accounts for the dependence of the intrinsic thermal noise on the data due to the heat dissipation associated with the transmission of data in electronic circuits: the data determine the transmitted signal, which in turn heats up the circuit and thus influences the power of the thermal noise. The capacity of this channel (both with and without feedback) is studied at low transmit powers and at high transmit powers. At low transmit powers, the slope of the capacity-versus-power curve at zero is computed and it is shown that the heating-up effect is beneficial. At high transmit powers, conditions are determined under which the capacity is bounded, i.e., under which the capacity does not grow to infinity as the allowed average power tends to infinity.},

keywords = {additive noise channel, Capacity per unit cost, channel capacity, channels with memory, cooling, electronic circuits, heat dissipation, heat sinks, high signal-to-noise ratio, high signal-to-noise ratio (SNR), intrinsic thermal noise, low transmit power, network analysis, noise variance, on-chip communication, thermal noise},

pubstate = {published},

tppubtype = {article}

}

@article{Lazaro2009b,

title = {Blind Equalization Using the IRWLS Formulation of the Support Vector Machine},

author = {Marcelino L\'{a}zaro and Jonathan Gonz\'{a}lez-Olasola},

url = {http://www.sciencedirect.com/science/article/pii/S0165168409000383},

issn = {01651684},

year  = {2009},

date = {2009-01-01},

journal = {Signal Processing},

volume = {89},

number = {7},

pages = {1436--1445},

abstract = {In this paper, using a common framework, we propose, analyze, and evaluate several variants of batch algorithms for blind equalization of SISO channels. They are based on the iterative re-weighted least square (IRWLS) solution for the support vector machine (SVM). The proposed methods combine the conventional cost function of the SVM with classical error functions applied to blind equalization: Sato's and Godard's error functions are included in the penalty term of the SVM. The relationship of these batch algorithms with conventional equalization and regularization techniques is analyzed in the paper. Simulation experiments performed over a relevant set of channels show that the proposed equalization methods perform better than traditional cumulant-based methods: they require a lower number of data samples to achieve the same equalization level and convergence ratio.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lazaro2009bb,

title = {Optimal Sensor Selection in Binary Heterogeneous Sensor Networks},

author = {Marcelino Lazaro and Matilde Sanchez-Fernandez and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4749309},

issn = {1053-587X},

year  = {2009},

date = {2009-01-01},

journal = {IEEE Transactions on Signal Processing},

volume = {57},

number = {4},

pages = {1577--1587},

abstract = {We consider the problem of sensor selection in a heterogeneous sensor network when several types of binary sensors with different discrimination performance and costs are available. We want to analyze what is the optimal proportion of sensors of each class in a target detection problem when a total cost constraint is specified. We obtain the conditional distributions of the observations at the fusion center given the hypotheses, necessary to perform an optimal hypothesis test in this heterogeneous scenario. We characterize the performance of the tests by means of the symmetric Kullback-Leibler divergence, or J -divergence, applied to the conditional distributions under each hypothesis. By formulating the sensor selection as a constrained maximization problem, and showing the linearity of the J-divergence with the number of sensors of each class, we found that the optimal proportion of sensors is ldquowinner takes allrdquo like. The sensor class with the best performance/cost ratio is selected.},

keywords = {binary heterogeneous sensor networks, discrimination performance, Energy scaling, object detection, optimal sensor selection, performance-cost ratio, sensor networks, sensor selection, symmetric Kullback-Leibler divergence, target detection problem, Wireless Sensor Networks},

pubstate = {published},

tppubtype = {article}

}

@article{Perez-Cruz2008c,

title = {Nonlinear Channel Equalization With Gaussian Processes for Regression},

author = {Fernando Perez-Cruz and Juan Jose Murillo-Fuentes and S Caro},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4563433},

issn = {1053-587X},

year  = {2008},

date = {2008-01-01},

journal = {IEEE Transactions on Signal Processing},

volume = {56},

number = {10},

pages = {5283--5286},

abstract = {We propose Gaussian processes for regression (GPR) as a novel nonlinear equalizer for digital communications receivers. GPR's main advantage, compared to previous nonlinear estimation approaches, lies on their capability to optimize the kernel hyperparameters by maximum likelihood, which improves its performance significantly for short training sequences. Besides, GPR can be understood as a nonlinear minimum mean square error estimator, a standard criterion for training equalizers that trades off the inversion of the channel and the amplification of the noise. In the experiment section, we show that the GPR-based equalizer clearly outperforms support vector machine and kernel adaline approaches, exhibiting outstanding results for short training sequences.},

keywords = {Channel estimation, digital communications receivers, equalisers, equalization, Gaussian processes, kernel adaline, least mean squares methods, maximum likelihood estimation, nonlinear channel equalization, nonlinear equalization, nonlinear minimum mean square error estimator, regression, regression analysis, short training sequences, Support vector machines},

pubstate = {published},

tppubtype = {article}

}

@article{Baca-Garcia2008,

title = {Patterns of Mental Health Service Utilization in a General Hospital and Outpatient Mental Health Facilities: Analysis of 365,262 Psychiatric Consultations},

author = {Enrique Baca-Garc\'{i}a and Mercedes M Perez-Rodriguez and Ignacio Basurte-Villamor and Javier F Quintero-Gutierrez and Juncal Sevilla-Vicente and Maria Martinez-Vigo and Antonio Art\'{e}s-Rodr\'{i}guez and Antonio Fernandez L del Moral and Miguel A Jimenez-Arriero and Jose Gonzalez L de Rivera},

url = {http://www.ncbi.nlm.nih.gov/pubmed/17990050},

issn = {0940-1334},

year  = {2008},

date = {2008-01-01},

journal = {European archives of psychiatry and clinical neuroscience},

volume = {258},

number = {2},

pages = {117--123},

abstract = {PURPOSE: Mental health is one of the priorities of the European Commission. Studies of the use and cost of mental health facilities are needed in order to improve the planning and efficiey of mental health resources. We analyze the patterns of mental health service use in multiple clinical settings to identify factors associated with high cost. SUBJECTS AND METHODS: 22,859 patients received psychiatric care in the catchment area of a Spanish hospital (2000-2004). They had 365,262 psychiatric consultations in multiple settings. Two groups were selected that generated 80% of total costs: the medium cost group (N = 4,212; 50% of costs), and the high cost group (N = 236; 30% of costs). Statistical analyses were performed using univariate and multivariate techniques. Significant variables in univariate analyses were introduced as independent variables in a logistic regression analysis using "high cost" (\>7,263$) as dependent variable. RESULTS: Costs were not evenly distributed throughout the sample. 19.4% of patients generated 80% of costs. The variables associated with high cost were: age group 1 (0-14 years) at the first evaluation, permanent disability, and ICD-10 diagnoses: Organic, including symptomatic, mental disorders; Mental and behavioural disorders due to psychoactive substance use; Schizophrenia, schizotypal and delusional disorders; Behavioural syndromes associated with physiological disturbances and physical factors; External causes of morbidity and mortality; and Factors influencing health status and contact with health services. DISCUSSION: Mental healthcare costs were not evenly distributed throughout the patient population. The highest costs are associated with early onset of the mental disorder, permanent disability, organic mental disorders, substance-related disorders, psychotic disorders, and external factors that influence the health status and contact with health services or cause morbidity and mortality. CONCLUSION: Variables related to psychiatric diagnoses and sociodemographic factors have influence on the cost of mental healthcare.},

keywords = {80 and over, Adolescent, Adult, Age Distribution, Aged, Ambulatory Care, Ambulatory Care: statistics \& numerical data, Ambulatory Care: utilization, Child, Diagnosis-Related Groups, Female, General, General: statistics \& numerical data, General: utilization, Health Care Costs, Health Care Costs: statistics \& numerical data, Health Services Accessibility, Health Services Accessibility: statistics \& numeri, Health Services Needs and Demand, Health Services Needs and Demand: statistics \& num, Hospitals, Humans, Male, Mental Disorders, Mental Disorders: classification, Mental Disorders: diagnosis, Mental Disorders: epidemiology, Mental Disorders: therapy, Mental Health Services, Mental Health Services: economics, Mental Health Services: utilization, Middle Aged, Outcome and Process Assessment (Health Care), Preschool, Psychiatry, Psychiatry: economics, Psychiatry: statistics \& numerical data, Sex Distribution, Spain, Spain: epidemiology, Utilization Review, Utilization Review: statistics \& numerical data},

pubstate = {published},

tppubtype = {article}

}

@article{Perez-Cruz2008d,

title = {Digital Communication Receivers Using Gaussian Processes for Machine Learning},

author = {Fernando Perez-Cruz and Juan Jose Murillo-Fuentes},

url = {http://asp.eurasipjournals.com/content/2008/1/491503},

issn = {1687-6172},

year  = {2008},

date = {2008-01-01},

journal = {EURASIP Journal on Advances in Signal Processing},

volume = {2008},

number = {1},

pages = {1--13},

publisher = {Springer},

abstract = {We propose Gaussian processes (GPs) as a novel nonlinear receiver for digital communication systems. The GPs framework can be used to solve both classification (GPC) and regression (GPR) problems. The minimum mean squared error solution is the expectation of the transmitted symbol given the information at the receiver, which is a nonlinear function of the received symbols for discrete inputs. GPR can be presented as a nonlinear MMSE estimator and thus capable of achieving optimal performance from MMSE viewpoint. Also, the design of digital communication receivers can be viewed as a detection problem, for which GPC is specially suited as it assigns posterior probabilities to each transmitted symbol. We explore the suitability of GPs as nonlinear digital communication receivers. GPs are Bayesian machine learning tools that formulates a likelihood function for its hyperparameters, which can then be set optimally. GPs outperform state-of-the-art nonlinear machine learning approaches that prespecify their hyperparameters or rely on cross validation. We illustrate the advantages of GPs as digital communication receivers for linear and nonlinear channel models for short training sequences and compare them to state-of-the-art nonlinear machine learning tools, such as support vector machines.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Leiva-Murillo2008c,

title = {A Simulated Annealing Approach to Speaker Segmentation in Audio Databases},

author = {Jose M Leiva-Murillo and Sancho Salcedo-Sanz and Ascensi\'{o}n Gallardo-Antol\'{i}n and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://www.sciencedirect.com/science/article/pii/S0952197607000954},

year  = {2008},

date = {2008-01-01},

journal = {Engineering Applications of Artificial Intelligence},

volume = {21},

number = {4},

pages = {499--508},

abstract = {In this paper we present a novel approach to the problem of speaker segmentation, which is an unavoidable previous step to audio indexing. Mutual information is used for evaluating the accuracy of the segmentation, as a function to be maximized by a simulated annealing (SA) algorithm. We introduce a novel mutation operator for the SA, the Consecutive Bits Mutation operator, which improves the performance of the SA in this problem. We also use the so-called Compaction Factor, which allows the SA to operate in a reduced search space. Our algorithm has been tested in the segmentation of real audio databases, and it has been compared to several existing algorithms for speaker segmentation, obtaining very good results in the test problems considered.},

keywords = {Audio indexing, information theory, Simulated annealing, Speaker segmentation},

pubstate = {published},

tppubtype = {article}

}

@article{Vazquez2008b,

title = {Sequential Monte Carlo Methods for Complexity-Constrained MAP Equalization of Dispersive MIMO Channels},

author = {Manuel A Vazquez and Monica F Bugallo and Joaquin Miguez},

url = {http://www.sciencedirect.com/science/article/pii/S0165168407003763},

year  = {2008},

date = {2008-01-01},

journal = {Signal Processing},

volume = {88},

number = {4},

pages = {1017--1034},

abstract = {The ability to perform nearly optimal equalization of multiple input multiple output (MIMO) wireless channels using sequential Monte Carlo (SMC) techniques has recently been demonstrated. SMC methods allow to recursively approximate the a posteriori probabilities of the transmitted symbols, as observations are sequentially collected, using samples from adequate probability distributions. Hence, they are a class of online (adaptive) algorithms, suitable to handle the time-varying channels typical of high speed mobile communication applications. The main drawback of the SMC-based MIMO-channel equalizers so far proposed is that their computational complexity grows exponentially with the number of input data streams and the length of the channel impulse response, rendering these methods impractical. In this paper, we introduce novel SMC schemes that overcome this limitation by the adequate design of proposal probability distribution functions that can be sampled with a lesser computational burden, yet provide a close-to-optimal performance in terms of the resulting equalizer bit error rate and channel estimation error. We show that the complexity of the resulting receivers grows polynomially with the number of input data streams and the length of the channel response, and present computer simulation results that illustrate their performance in some typical scenarios.},

keywords = {joint channel and data estimation, Multiple Input Multiple Output (MIMO), Sequential Monte Carlo (SMC)},

pubstate = {published},

tppubtype = {article}

}

@article{Leiva-Murillo2007,

title = {Maximization of Mutual Information for Supervised Linear Feature Extraction},

author = {Jose M Leiva-Murillo and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=4298118},

issn = {1045-9227},

year  = {2007},

date = {2007-01-01},

journal = {IEEE Transactions on Neural Networks},

volume = {18},

number = {5},

pages = {1433--1441},

publisher = {IEEE},

abstract = {In this paper, we present a novel scheme for linear feature extraction in classification. The method is based on the maximization of the mutual information (MI) between the features extracted and the classes. The sum of the MI corresponding to each of the features is taken as an heuristic that approximates the MI of the whole output vector. Then, a component-by-component gradient-ascent method is proposed for the maximization of the MI, similar to the gradient-based entropy optimization used in independent component analysis (ICA). The simulation results show that not only is the method competitive when compared to existing supervised feature extraction methods in all cases studied, but it also remarkably outperform them when the data are characterized by strongly nonlinear boundaries between classes.},

keywords = {Algorithms, Artificial Intelligence, Automated, component-by-component gradient-ascent method, Computer Simulation, Data Mining, Entropy, Feature extraction, gradient methods, gradient-based entropy, Independent component analysis, Information Storage and Retrieval, information theory, Iron, learning (artificial intelligence), Linear discriminant analysis, Linear Models, Mutual information, Optimization methods, Pattern recognition, Reproducibility of Results, Sensitivity and Specificity, supervised linear feature extraction, Vectors},

pubstate = {published},

tppubtype = {article}

}

@article{FPerez18c,

title = {A Bayesian Nonparametric Approach to Discover Clinico-Genetic Associations across Cancer Types},

author = {Melanie F. Pradier and Stephanie L. Hyland and Stefan G. Stark and Kjong Lehmann and Julia E. Vogt and Fernando Perez-Cruz and Gunnar R\"{a}tsch},

doi = {https://doi.org/10.1101/623215},

keywords = {Bayesian non parametrics},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{RamirezSantamariaScharf-2024-Passivedetectionofrandomsignal,

title = {Passive detection of a random signal common to multi-sensor reference and surveillance arrays},

author = {D. Ram\'{i}rez and I. Santamar\'{i}a and L. L. Scharf},

doi = {10.1109/TVT.2024.3366757},

issn = {0018-9545},

journal = {IEEE Trans. Vehicular Techn.},

volume = {73},

number = {7},

pages = {10106\textendash10117},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bonilla-EscribanoRamirezBaca-Garcia-2023-Multidimensionalvariabilityinecologicalassessments,

title = {Multidimensional variability in ecological assessments predicts two clusters of suicidal patients},

author = {P. Bonilla-Escribano and D. Ram\'{i}rez and Enrique Baca-Garc\'{i}a and P. Courtet and A. Art\'{e}s-Rodr\'{i}guez and Jorge L\'{o}pez-Castrom\'{a}n},

doi = {10.1038/s41598-023-30085-1},

issn = {2045-2322},

journal = {Scientific Reports},

volume = {13},

number = {3546},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{XiaoHuangRamirez-2023-Covariancematrixrecoveryfromone-bitb,

title = {Covariance matrix recovery from one-bit data with non-zero quantization thresholds: Algorithm and performance analysis},

author = {Y. -H. Xiao and L. Huang and D. Ram\'{i}rez and C. Qian and H. C. So},

doi = {10.1109/TSP.2023.3325664},

issn = {1053-587X},

journal = {IEEE Trans. Signal Process.},

volume = {71},

pages = {4060\textendash4076},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PerezViaVielva-2022-OnlinedetectionandSNRestimationb,

title = {Online detection and SNR estimation in cooperative spectrum sensing},

author = {J. P\'{e}rez and J. V\'{i}a and L. Vielva and D. Ram\'{i}rez},

doi = {10.1109/TWC.2021.3113089},

issn = {1536-1276},

journal = {IEEE Trans. Wireless Comm.},

volume = {21},

number = {4},

pages = {2521\textendash2533},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{XiaoRamirezSchreier-2022-One-bittargetdetectionincollocatedb,

title = {One-bit target detection in collocated MIMO Radar and performance degradation analysis},

author = {Y. -H. Xiao and D. Ram\'{i}rez and P. J. Schreier and C. Qian and L. Huang},

doi = {10.1109/TVT.2022.3178285},

issn = {0018-9545},

journal = {IEEE Trans. Vehicular Techn.},

volume = {71},

number = {9},

pages = {9363\textendash9374},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{RamirezMarquesSegarra-2021-Graph-signalreconstructionandblinddeconvolution,

title = {Graph-signal reconstruction and blind deconvolution for structured inputs},

author = {D. Ram\'{i}rez and A. G. Marques and S. Segarra},

doi = {10.1016/j.sigpro.2021.108180},

issn = {0165-1684},

journal = {Signal Process. (Special issue on Processing and Learning over Graphs)},

volume = {188},

pages = {108180},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Moreno-MunozRamirezArtes-Rodriguez-2021-Change-pointdetectioninhierarchicalcircadian,

title = {Change-point detection in hierarchical circadian models},

author = {P. Moreno-Mu\~{n}oz and D. Ram\'{i}rez and A. Art\'{e}s-Rodr\'{i}guez},

doi = {10.1016/j.patcog.2021.107820},

issn = {0031-3203},

journal = {Pattern Recognition},

volume = {113},

pages = {107820},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{GargSantamariaRamirez-2019-Subspaceaveragingandorderdetermination,

title = {Subspace averaging and order determination for source enumeration},

author = {V. Garg and I. Santamar\'{i}a and D. Ram\'{i}rez and L. L. Scharf},

doi = {10.1109/TSP.2019.2912151},

issn = {1053-587X},

journal = {IEEE Trans. Signal Process.},

volume = {67},

number = {11},

pages = {3028\textendash3041},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{EguizabalLameiroRamirez-2019-Sourceenumerationinpresenceof,

title = {Source enumeration in the presence of colored noise},

author = {A. Eguizabal and C. Lameiro and D. Ram\'{i}rez and P. J. Schreier},

doi = {10.1109/LSP.2019.2895548},

issn = {1070-9908},

journal = {IEEE Signal Process. Lett.},

volume = {26},

number = {3},

pages = {475\textendash479},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Guzman2015b,

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 P. Gil Jimenez},

doi = {10.1109/tce.2015.7389772},

issn = {1558-4127},

journal = {IEEE Trans. Consumer Electron.},

volume = {61},

number = {4},

pages = {393--401},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Jimenez2017,

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

author = {Victor P. Gil Jimenez and Alejandro Lancho Serrano and Borja Genoves Guzman and Ana Garcia Armada},

doi = {10.1109/mcom.2017.1601219},

issn = {0163-6804},

journal = {IEEE Commun. Mag.},

volume = {55},

number = {5},

pages = {116--123},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}