2010
Delgado-Gómez, David; Sukno, Federico; Aguado, David; Santacruz, Carlos; Artés-Rodríguez, Antonio
Individual Identification Using Personality Traits Artículo de revista
En: Journal of Network and Computer Applications, vol. 33, no. 3, pp. 293–299, 2010, ISSN: 10848045.
Resumen | Enlaces | BibTeX | Etiquetas: Biometrics, Personality traits, Psychometrics, Samejima's model
@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}
}
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.
Koch, Tobias; Lapidoth, Amos
Gaussian Fading Is the Worst Fading Artículo de revista
En: IEEE Transactions on Information Theory, vol. 56, no. 3, pp. 1158–1165, 2010, ISSN: 0018-9448.
Resumen | Enlaces | BibTeX | Etiquetas: 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
@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}
}
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.
Martino, Luca; Miguez, Joaquin
Generalized Rejection Sampling Schemes and Applications in Signal Processing Artículo de revista
En: Signal Processing, vol. 90, no. 11, pp. 2981–2995, 2010.
Resumen | Enlaces | BibTeX | Etiquetas: Adaptive rejection sampling, Gibbs sampling, Monte Carlo integration, Rejection sampling, sensor networks, Target localization
@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}
}
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.
Djuric, Petar M; Miguez, Joaquin
Assessment of Nonlinear Dynamic Models by Kolmogorov–Smirnov Statistics Artículo de revista
En: IEEE Transactions on Signal Processing, vol. 58, no. 10, pp. 5069–5079, 2010, ISSN: 1053-587X.
Resumen | Enlaces | BibTeX | Etiquetas: Cumulative distributions, discrete random variables, dynamic nonlinear models, Electrical capacitance tomography, Filtering, filtering theory, Iron, Kolmogorov-Smirnov statistics, Kolomogorov–Smirnov 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
@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}
}
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.
Perez-Cruz, Fernando; Rodrigues, Miguel R D; Verdu, Sergio
MIMO Gaussian Channels With Arbitrary Inputs: Optimal Precoding and Power Allocation Artículo de revista
En: IEEE Transactions on Information Theory, vol. 56, no. 3, pp. 1070–1084, 2010, ISSN: 0018-9448.
Resumen | Enlaces | BibTeX | Etiquetas: 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–multiple-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
@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}
}
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.
Olmos, Pablo M; Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando
Joint Nonlinear Channel Equalization and Soft LDPC Decoding with Gaussian Processes Artículo de revista
En: IEEE Transactions on Signal Processing, vol. 58, no. 3, pp. 1183–1192, 2010, ISSN: 1053-587X.
Resumen | Enlaces | BibTeX | Etiquetas: 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)
@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}
}
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.
Koch, Tobias; Lapidoth, Amos
On Multipath Fading Channels at High SNR Artículo de revista
En: IEEE Transactions on Information Theory, vol. 56, no. 12, pp. 5945–5957, 2010, ISSN: 0018-9448.
Resumen | Enlaces | BibTeX | Etiquetas: 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
@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}
}
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.
Fresia, Maria; Perez-Cruz, Fernando; Poor, Vincent H; Verdu, Sergio
Joint Source and Channel Coding Artículo de revista
En: IEEE Signal Processing Magazine, vol. 27, no. 6, pp. 104–113, 2010, ISSN: 1053-5888.
Resumen | Enlaces | BibTeX | Etiquetas: 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
@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}
}
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.
Martino, Luca; Miguez, Joaquin
A Generalization of the Adaptive Rejection Sampling Algorithm Artículo de revista
En: Statistics and Computing, vol. 21, no. 4, pp. 633–647, 2010, ISSN: 0960-3174.
Resumen | Enlaces | BibTeX | Etiquetas:
@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}
}
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.
Zoubir, A; Viberg, M; Yang, B; Miguez, Joaquin
Analysis of a Sequential Monte Carlo Method for Optimization in Dynamical Systems Artículo de revista
En: Signal Processing, vol. 90, no. 5, pp. 1609–1622, 2010.
Resumen | Enlaces | BibTeX | Etiquetas: Dynamic optimization, Nonlinear dynamics, Nonlinear tracking, Sequential Monte Carlo, Stochastic optimization
@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}
}
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.
Alvarez, Mauricio; Luengo, David; Titsias, Michalis; Lawrence, Neil D
Efficient Multioutput Gaussian Processes Through Variational Inducing Kernels Artículo en actas
En: AISTATS 2010, Sardinia, 2010.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Alvarez2010,
title = {Efficient Multioutput Gaussian Processes Through Variational Inducing Kernels},
author = {Mauricio Alvarez and David Luengo and Michalis Titsias and Neil D Lawrence},
url = {http://eprints.pascal-network.org/archive/00006397/},
year = {2010},
date = {2010-01-01},
booktitle = {AISTATS 2010},
address = {Sardinia},
abstract = {Interest in multioutput kernel methods is increasing, whether under the guise of multitask learning, multisensor networks or structured output data. From the Gaussian process perspective a multioutput Mercer kernel is a covariance function over correlated output functions. One way of constructing such kernels is based on convolution processes (CP). A key problem for this approach is efficient inference. Alvarez and Lawrence recently presented a sparse approximation for CPs that enabled efficient inference. In this paper, we extend this work in two directions: we introduce the concept of variational inducing functions to handle potential non-smooth functions involved in the kernel CP construction and we consider an alternative approach to approximate inference based on variational methods, extending the work by Titsias (2009) to the multiple output case. We demonstrate our approaches on prediction of school marks, compiler performance and financial time series.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Interest in multioutput kernel methods is increasing, whether under the guise of multitask learning, multisensor networks or structured output data. From the Gaussian process perspective a multioutput Mercer kernel is a covariance function over correlated output functions. One way of constructing such kernels is based on convolution processes (CP). A key problem for this approach is efficient inference. Alvarez and Lawrence recently presented a sparse approximation for CPs that enabled efficient inference. In this paper, we extend this work in two directions: we introduce the concept of variational inducing functions to handle potential non-smooth functions involved in the kernel CP construction and we consider an alternative approach to approximate inference based on variational methods, extending the work by Titsias (2009) to the multiple output case. We demonstrate our approaches on prediction of school marks, compiler performance and financial time series.
Plata-Chaves, Jorge; Lazaro, Marcelino
Closed-Form Error Exponent for the Neyman-Pearson Fusion of Two-Dimensional Markov Local Decisions Artículo en actas
En: European Signal Processing Conference (EUSIPCO 2010), Aalborg, 2010.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Plata-Chaves2010,
title = {Closed-Form Error Exponent for the Neyman-Pearson Fusion of Two-Dimensional Markov Local Decisions},
author = {Jorge Plata-Chaves and Marcelino Lazaro},
url = {http://www.eurasip.org/Proceedings/Eusipco/Eusipco2010/Contents/papers/1569292447.pdf},
year = {2010},
date = {2010-01-01},
booktitle = {European Signal Processing Conference (EUSIPCO 2010)},
address = {Aalborg},
abstract = {We consider a distributed detection system formed by a large num- ber of local detectors and a fusion center that performs a Neyman- Pearson fusion of the binary quantizations of the sensor observa- tions. The aforementioned local decisions are taken with no kind of cooperation and transmitted to the fusion center over error free parallel access channels. Furthermore, the devices are located on a rectangular lattice so that sensors belonging to a specific row or column are equally spaced. For each hypothesis H 0 and H 1 , the correlation structure of the local decisions is modelled with a two- dimensional causal field where the rows and columns are outcomes of the same first-order binary Markov chain. Under this scenario, we derive a closed-form error exponent for the Neyman-Pearson fusion of the local decisions. Afterwards, using the derived error exponent we study the effect of different design parameters of the network on its overall detection performance},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider a distributed detection system formed by a large num- ber of local detectors and a fusion center that performs a Neyman- Pearson fusion of the binary quantizations of the sensor observa- tions. The aforementioned local decisions are taken with no kind of cooperation and transmitted to the fusion center over error free parallel access channels. Furthermore, the devices are located on a rectangular lattice so that sensors belonging to a specific row or column are equally spaced. For each hypothesis H 0 and H 1 , the correlation structure of the local decisions is modelled with a two- dimensional causal field where the rows and columns are outcomes of the same first-order binary Markov chain. Under this scenario, we derive a closed-form error exponent for the Neyman-Pearson fusion of the local decisions. Afterwards, using the derived error exponent we study the effect of different design parameters of the network on its overall detection performance
2009
Czink, Nicolai; Bandemer, Bernd; Vazquez-Vilar, Gonzalo; Jalloul, Louay; Oestges, Claude; Paulraj, Arogyaswami
Spatial Separation of Multi-User MIMO Channels Artículo en actas
En: 20th Personal, Indoor and Mobile Radio Communications Symposium 2009 (PIMRC 09), Tokyo, Japan, 2009.
BibTeX | Etiquetas:
@inproceedings{nczink2009,
title = {Spatial Separation of Multi-User MIMO Channels},
author = {Nicolai Czink and Bernd Bandemer and Gonzalo Vazquez-Vilar and Louay Jalloul and Claude Oestges and Arogyaswami Paulraj},
year = {2009},
date = {2009-09-01},
booktitle = {20th Personal, Indoor and Mobile Radio Communications Symposium 2009 (PIMRC 09)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Bandemer, Bernd; Vazquez-Vilar, Gonzalo; Gamal, Abbas El
On the Sum Capacity of A Class of Cyclically Symmetric Deterministic Interference Channels Artículo en actas
En: 2009 IEEE International Symposium on Information Theory (ISIT 2009), Coex, Seoul, Korea, 2009.
BibTeX | Etiquetas:
@inproceedings{bbandemer2009,
title = {On the Sum Capacity of A Class of Cyclically Symmetric Deterministic Interference Channels},
author = {Bernd Bandemer and Gonzalo Vazquez-Vilar and Abbas El Gamal},
year = {2009},
date = {2009-06-01},
booktitle = {2009 IEEE International Symposium on Information Theory (ISIT 2009)},
address = {Coex, Seoul, Korea},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
López-Valcarce, Roberto; Vazquez-Vilar, Gonzalo; Álvarez-Díaz, Marcos
Multiantenna detection of multicarrier primary signals exploiting spectral a priori information Artículo en actas
En: 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications (Crowncom 2009), Hannover, Germany, 2009.
BibTeX | Etiquetas:
@inproceedings{crowncom2009,
title = {Multiantenna detection of multicarrier primary signals exploiting spectral a priori information},
author = {Roberto L\'{o}pez-Valcarce and Gonzalo Vazquez-Vilar and Marcos \'{A}lvarez-D\'{i}az},
year = {2009},
date = {2009-06-01},
booktitle = {4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications (Crowncom 2009)},
address = {Hannover, Germany},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
López-Valcarce, Roberto; Vazquez-Vilar, Gonzalo
Wideband Spectrum Sensing in Cognitive Radio: Joint Estimation of Noise Variance and Multiple Signal Levels Artículo en actas
En: 2009 IEEE International Workshop on Signal Processing Advances for Wireless Communications (Spawc 2009), Perugia, Italy, 2009.
BibTeX | Etiquetas:
@inproceedings{spawc2009,
title = {Wideband Spectrum Sensing in Cognitive Radio: Joint Estimation of Noise Variance and Multiple Signal Levels},
author = {Roberto L\'{o}pez-Valcarce and Gonzalo Vazquez-Vilar},
year = {2009},
date = {2009-06-01},
booktitle = {2009 IEEE International Workshop on Signal Processing Advances for Wireless Communications (Spawc 2009)},
address = {Perugia, Italy},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Olmos, Pablo M; Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando
Soft LDPC Decoding in Nonlinear Channels with Gaussian Processes for Classification Artículo en actas
En: European Signal Processing Conference (EUSIPCO), Glasgow, 2009.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Olmos2009,
title = {Soft LDPC Decoding in Nonlinear Channels with Gaussian Processes for Classification},
author = {Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://www.eurasip.org/Proceedings/Eusipco/Eusipco2009/contents/papers/1569186781.pdf},
year = {2009},
date = {2009-01-01},
booktitle = {European Signal Processing Conference (EUSIPCO)},
address = {Glasgow},
abstract = {In this paper, we propose a new approach for nonlinear equalization based on Gaussian processes for classification (GPC).We also measure the performance of the 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. GPC is a Bayesian nonlinear classification tool that provides accurate posterior probability estimates with short training sequences. We show that the accuracy of these estimates is essential for optimal performance of the channel decoder and that the error rate outputted by the equalizer might be irrelevant to understand the performance of the overall communication receiver. We compare the proposed equalizers with state-ofthe- art solutions.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose a new approach for nonlinear equalization based on Gaussian processes for classification (GPC).We also measure the performance of the 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. GPC is a Bayesian nonlinear classification tool that provides accurate posterior probability estimates with short training sequences. We show that the accuracy of these estimates is essential for optimal performance of the channel decoder and that the error rate outputted by the equalizer might be irrelevant to understand the performance of the overall communication receiver. We compare the proposed equalizers with state-ofthe- art solutions.
Bravo-Santos, Ángel M; Djuric, Petar M
Cooperative Relay Communications in Mesh Networks Artículo en actas
En: 2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications, pp. 499–503, IEEE, Perugia, 2009, ISBN: 978-1-4244-3695-8.
Resumen | Enlaces | BibTeX | Etiquetas: binary transmission, bit error probability, Bit error rate, cooperative relay communications, decode-and-forward relays, Detectors, error statistics, Maximum likelihood decoding, maximum likelihood detection, Mesh networks, mesh wireless networks, multi-hop networks, Network topology, optimal node decision rules, Peer to peer computing, radio networks, Relays, spread spectrum communication, telecommunication network topology, Wireless Sensor Networks
@inproceedings{Bravo-Santos2009,
title = {Cooperative Relay Communications in Mesh Networks},
author = {\'{A}ngel M Bravo-Santos and Petar M Djuric},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5161835},
isbn = {978-1-4244-3695-8},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications},
pages = {499--503},
publisher = {IEEE},
address = {Perugia},
abstract = {In previous literature on cooperative relay communications, the emphasis has been on the study of multi-hop networks. In this paper we address mesh wireless networks that use decode-and-forward relays for which we derive the optimal node decision rules in case of binary transmission. We also obtain the expression for the overall bit error probability. We compare the mesh networks with multi-hop networks and show the improvement in performance that can be achieved with them when both networks have the same number of nodes and equal number of hops.},
keywords = {binary transmission, bit error probability, Bit error rate, cooperative relay communications, decode-and-forward relays, Detectors, error statistics, Maximum likelihood decoding, maximum likelihood detection, Mesh networks, mesh wireless networks, multi-hop networks, Network topology, optimal node decision rules, Peer to peer computing, radio networks, Relays, spread spectrum communication, telecommunication network topology, Wireless Sensor Networks},
pubstate = {published},
tppubtype = {inproceedings}
}
In previous literature on cooperative relay communications, the emphasis has been on the study of multi-hop networks. In this paper we address mesh wireless networks that use decode-and-forward relays for which we derive the optimal node decision rules in case of binary transmission. We also obtain the expression for the overall bit error probability. We compare the mesh networks with multi-hop networks and show the improvement in performance that can be achieved with them when both networks have the same number of nodes and equal number of hops.
Bugallo, Monica F; Maiz, Cristina S; Miguez, Joaquin; Djuric, Petar M
Cost-Reference Particle Filters and Fusion of Information Artículo en actas
En: 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, pp. 286–291, IEEE, Marco Island, FL, 2009.
Resumen | Enlaces | BibTeX | Etiquetas: costs, distributed processing, Electronic mail, fusion, Information filtering, Information filters, information fusion, Measurement standards, probabilistic information, random measures, sensor fusion, smoothing methods, Weight measurement
@inproceedings{Bugallo2009,
title = {Cost-Reference Particle Filters and Fusion of Information},
author = {Monica F Bugallo and Cristina S Maiz and Joaquin Miguez and Petar M Djuric},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4785936},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop},
pages = {286--291},
publisher = {IEEE},
address = {Marco Island, FL},
abstract = {Cost-reference particle filtering is a methodology for tracking unknowns in a system without reliance on probabilistic information about the noises in the system. The methodology is based on analogous principles as the ones of standard particle filtering. Unlike the random measures of standard particle filters that are composed of particles and weights, the random measures of cost-reference particle filters contain particles and user-defined costs. In this paper, we discuss a few scenarios where we need to meld random measures of two or more cost-reference particle filters. The objective is to obtain a fused random measure that combines the information from the individual cost-reference particle filters.},
keywords = {costs, distributed processing, Electronic mail, fusion, Information filtering, Information filters, information fusion, Measurement standards, probabilistic information, random measures, sensor fusion, smoothing methods, Weight measurement},
pubstate = {published},
tppubtype = {inproceedings}
}
Cost-reference particle filtering is a methodology for tracking unknowns in a system without reliance on probabilistic information about the noises in the system. The methodology is based on analogous principles as the ones of standard particle filtering. Unlike the random measures of standard particle filters that are composed of particles and weights, the random measures of cost-reference particle filters contain particles and user-defined costs. In this paper, we discuss a few scenarios where we need to meld random measures of two or more cost-reference particle filters. The objective is to obtain a fused random measure that combines the information from the individual cost-reference particle filters.
Djuric, Petar M; Miguez, Joaquin
Model Assessment with Kolmogorov-Smirnov Statistics Artículo en actas
En: 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2973–2976, IEEE, Taipei, 2009, ISSN: 1520-6149.
Resumen | Enlaces | BibTeX | Etiquetas: Bayesian methods, Computer Simulation, Context modeling, Electronic mail, Filtering, ill-conditioned problem, Kolmogorov-Smirnov statistics, model assessment, modelling, Predictive models, Probability, statistical analysis, statistics, Testing
@inproceedings{Djuric2009,
title = {Model Assessment with Kolmogorov-Smirnov Statistics},
author = {Petar M Djuric and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4960248},
issn = {1520-6149},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE International Conference on Acoustics, Speech and Signal Processing},
pages = {2973--2976},
publisher = {IEEE},
address = {Taipei},
abstract = {One of the most basic problems in science and engineering is the assessment of a considered model. The model should describe a set of observed data and the objective is to find ways of deciding if the model should be rejected. It seems that this is an ill-conditioned problem because we have to test the model against all the possible alternative models. In this paper we use the Kolmogorov-Smirnov statistic to develop a test that shows if the model should be kept or it should be rejected. We explain how this testing can be implemented in the context of particle filtering. We demonstrate the performance of the proposed method by computer simulations.},
keywords = {Bayesian methods, Computer Simulation, Context modeling, Electronic mail, Filtering, ill-conditioned problem, Kolmogorov-Smirnov statistics, model assessment, modelling, Predictive models, Probability, statistical analysis, statistics, Testing},
pubstate = {published},
tppubtype = {inproceedings}
}
One of the most basic problems in science and engineering is the assessment of a considered model. The model should describe a set of observed data and the objective is to find ways of deciding if the model should be rejected. It seems that this is an ill-conditioned problem because we have to test the model against all the possible alternative models. In this paper we use the Kolmogorov-Smirnov statistic to develop a test that shows if the model should be kept or it should be rejected. We explain how this testing can be implemented in the context of particle filtering. We demonstrate the performance of the proposed method by computer simulations.
Maiz, Cristina S; Miguez, Joaquin; Djuric, Petar M
Particle Filtering in the Presence of Outliers Artículo en actas
En: 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, pp. 33–36, IEEE, Cardiff, 2009, ISBN: 978-1-4244-2709-3.
Resumen | Enlaces | BibTeX | Etiquetas: computer simulations, Degradation, Filtering, multidimensional random variates, Multidimensional signal processing, Multidimensional systems, Nonlinear tracking, Outlier detection, predictive distributions, Signal processing, signal processing tools, signal-power observations, spatial depth, statistical analysis, statistical distributions, statistics, Target tracking, Testing
@inproceedings{Maiz2009,
title = {Particle Filtering in the Presence of Outliers},
author = {Cristina S Maiz and Joaquin Miguez and Petar M Djuric},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5278645},
isbn = {978-1-4244-2709-3},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE/SP 15th Workshop on Statistical Signal Processing},
pages = {33--36},
publisher = {IEEE},
address = {Cardiff},
abstract = {Particle filters have become very popular signal processing tools for problems that involve nonlinear tracking of an unobserved signal of interest given a series of related observations. In this paper we propose a new scheme for particle filtering when the observed data are possibly contaminated with outliers. An outlier is an observation that has been generated by some (unknown) mechanism different from the assumed model of the data. Therefore, when handled in the same way as regular observations, outliers may drastically degrade the performance of the particle filter. To address this problem, we introduce an auxiliary particle filtering scheme that incorporates an outlier detection step. We propose to implement it by means of a test involving statistics of the predictive distributions of the observations. Specifically, we investigate the use of a proposed statistic called spatial depth that can easily be applied to multidimensional random variates. The performance of the resulting algorithm is assessed by computer simulations of target tracking based on signal-power observations.},
keywords = {computer simulations, Degradation, Filtering, multidimensional random variates, Multidimensional signal processing, Multidimensional systems, Nonlinear tracking, Outlier detection, predictive distributions, Signal processing, signal processing tools, signal-power observations, spatial depth, statistical analysis, statistical distributions, statistics, Target tracking, Testing},
pubstate = {published},
tppubtype = {inproceedings}
}
Particle filters have become very popular signal processing tools for problems that involve nonlinear tracking of an unobserved signal of interest given a series of related observations. In this paper we propose a new scheme for particle filtering when the observed data are possibly contaminated with outliers. An outlier is an observation that has been generated by some (unknown) mechanism different from the assumed model of the data. Therefore, when handled in the same way as regular observations, outliers may drastically degrade the performance of the particle filter. To address this problem, we introduce an auxiliary particle filtering scheme that incorporates an outlier detection step. We propose to implement it by means of a test involving statistics of the predictive distributions of the observations. Specifically, we investigate the use of a proposed statistic called spatial depth that can easily be applied to multidimensional random variates. The performance of the resulting algorithm is assessed by computer simulations of target tracking based on signal-power observations.
Martino, Luca; Miguez, Joaquin
A Novel Rejection Sampling Scheme for Posterior Probability Distributions Artículo en actas
En: 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2921–2924, IEEE, Taipei, 2009, ISSN: 1520-6149.
Resumen | Enlaces | BibTeX | Etiquetas: Additive noise, arbitrary target probability distributions, Bayes methods, Bayesian methods, Monte Carlo integration, Monte Carlo methods, Monte Carlo techniques, Overbounding, posterior probability distributions, Probability density function, Probability distribution, Proposals, Rejection sampling, rejection sampling scheme, Sampling methods, Signal processing algorithms, signal sampling, Upper bound
@inproceedings{Martino2009,
title = {A Novel Rejection Sampling Scheme for Posterior Probability Distributions},
author = {Luca Martino and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4960235},
issn = {1520-6149},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE International Conference on Acoustics, Speech and Signal Processing},
pages = {2921--2924},
publisher = {IEEE},
address = {Taipei},
abstract = {Rejection sampling (RS) is a well-known method to draw from arbitrary target probability distributions, which has important applications by itself or as a building block for more sophisticated Monte Carlo techniques. The main limitation to the use of RS is the need to find an adequate upper bound for the ratio of the target probability density function (pdf) over the proposal pdf from which the samples are generated. There are no general methods to analytically find this bound, except in the particular case in which the target pdf is log-concave. In this paper we adopt a Bayesian view of the problem and propose a general RS scheme to draw from the posterior pdf of a signal of interest using its prior density as a proposal function. The method enables the analytical calculation of the bound and can be applied to a large class of target densities. We illustrate its use with a simple numerical example.},
keywords = {Additive noise, arbitrary target probability distributions, Bayes methods, Bayesian methods, Monte Carlo integration, Monte Carlo methods, Monte Carlo techniques, Overbounding, posterior probability distributions, Probability density function, Probability distribution, Proposals, Rejection sampling, rejection sampling scheme, Sampling methods, Signal processing algorithms, signal sampling, Upper bound},
pubstate = {published},
tppubtype = {inproceedings}
}
Rejection sampling (RS) is a well-known method to draw from arbitrary target probability distributions, which has important applications by itself or as a building block for more sophisticated Monte Carlo techniques. The main limitation to the use of RS is the need to find an adequate upper bound for the ratio of the target probability density function (pdf) over the proposal pdf from which the samples are generated. There are no general methods to analytically find this bound, except in the particular case in which the target pdf is log-concave. In this paper we adopt a Bayesian view of the problem and propose a general RS scheme to draw from the posterior pdf of a signal of interest using its prior density as a proposal function. The method enables the analytical calculation of the bound and can be applied to a large class of target densities. We illustrate its use with a simple numerical example.
Achutegui, Katrin; Martino, Luca; Rodas, Javier; Escudero, Carlos J; Miguez, Joaquin
A Multi-Model Particle Filtering Algorithm for Indoor Tracking of Mobile Terminals Using RSS Data Artículo en actas
En: 2009 IEEE International Conference on Control Applications, pp. 1702–1707, IEEE, Saint Petersburg, 2009, ISBN: 978-1-4244-4601-8.
Resumen | Enlaces | BibTeX | Etiquetas: Bayesian methods, Control systems, Filtering algorithms, generalized interacting multiple model, GIMM, indoor radio, Indoor tracking, mobile radio, mobile terminal, Monte Carlo methods, multipath propagation, position-dependent data measurement, random process, random processes, Rao-Blackwellized sequential Monte Carlo tracking, received signal strength, RSS data, Sliding mode control, State-space methods, state-space model, Target tracking, tracking, transmitter-to-receiver distance, wireless network, wireless technology
@inproceedings{Achutegui2009,
title = {A Multi-Model Particle Filtering Algorithm for Indoor Tracking of Mobile Terminals Using RSS Data},
author = {Katrin Achutegui and Luca Martino and Javier Rodas and Carlos J Escudero and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5280960},
isbn = {978-1-4244-4601-8},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE International Conference on Control Applications},
pages = {1702--1707},
publisher = {IEEE},
address = {Saint Petersburg},
abstract = {In this paper we address the problem of indoor tracking using received signal strength (RSS) as a position-dependent data measurement. This type of measurements is very appealing because they can be easily obtained with a variety of wireless technologies which are relatively inexpensive. The extraction of accurate location information from RSS in indoor scenarios is not an easy task, though. Since RSS is highly influenced by multipath propagation, it turns out very hard to adequately model the correspondence between the received power and the transmitter-to-receiver distance. The measurement models proposed in the literature are site-specific and require a great deal of information regarding the structure of the building where the tracking will be performed and therefore are not useful for a general application. For that reason we propose the use of a compound model that combines several sub-models, whose parameters are adjusted to specific and different propagation environments. This methodology, is called interacting multiple models (IMM), has been used in the past for modeling the motion of maneuvering targets. Here, we extend its application to handle also the uncertainty in the RSS observations and we refer to the resulting state-space model as a generalized IMM (GIMM) system. The flexibility of the GIMM approach is attained at the expense of an increase in the number of random processes that must be accurately tracked. To overcome this difficulty, we introduce a Rao-Blackwellized sequential Monte Carlo tracking algorithm that exhibits good performance both with synthetic and experimental data.},
keywords = {Bayesian methods, Control systems, Filtering algorithms, generalized interacting multiple model, GIMM, indoor radio, Indoor tracking, mobile radio, mobile terminal, Monte Carlo methods, multipath propagation, position-dependent data measurement, random process, random processes, Rao-Blackwellized sequential Monte Carlo tracking, received signal strength, RSS data, Sliding mode control, State-space methods, state-space model, Target tracking, tracking, transmitter-to-receiver distance, wireless network, wireless technology},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we address the problem of indoor tracking using received signal strength (RSS) as a position-dependent data measurement. This type of measurements is very appealing because they can be easily obtained with a variety of wireless technologies which are relatively inexpensive. The extraction of accurate location information from RSS in indoor scenarios is not an easy task, though. Since RSS is highly influenced by multipath propagation, it turns out very hard to adequately model the correspondence between the received power and the transmitter-to-receiver distance. The measurement models proposed in the literature are site-specific and require a great deal of information regarding the structure of the building where the tracking will be performed and therefore are not useful for a general application. For that reason we propose the use of a compound model that combines several sub-models, whose parameters are adjusted to specific and different propagation environments. This methodology, is called interacting multiple models (IMM), has been used in the past for modeling the motion of maneuvering targets. Here, we extend its application to handle also the uncertainty in the RSS observations and we refer to the resulting state-space model as a generalized IMM (GIMM) system. The flexibility of the GIMM approach is attained at the expense of an increase in the number of random processes that must be accurately tracked. To overcome this difficulty, we introduce a Rao-Blackwellized sequential Monte Carlo tracking algorithm that exhibits good performance both with synthetic and experimental data.
Djuric, Petar M; Bugallo, Monica F; Closas, Pau; Miguez, Joaquin
Measuring the Robustness of Sequential Methods Artículo en actas
En: 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, pp. 29–32, IEEE, Aruba, Dutch Antilles, 2009, ISBN: 978-1-4244-5179-1.
Resumen | Enlaces | BibTeX | Etiquetas: Additive noise, cumulative distribution functions, data processing method, extended Kalman filtering, Extraterrestrial measurements, Filtering, Gaussian distribution, Gaussian noise, Kalman filters, Kolmogorov-Smirnov distance, Least squares approximation, Noise robustness, nonlinear filters, robustness, sequential methods, statistical distributions, telecommunication computing
@inproceedings{Djuric2009a,
title = {Measuring the Robustness of Sequential Methods},
author = {Petar M Djuric and Monica F Bugallo and Pau Closas and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5413275},
isbn = {978-1-4244-5179-1},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop},
pages = {29--32},
publisher = {IEEE},
address = {Aruba, Dutch Antilles},
abstract = {Whenever we apply methods for processing data, we make a number of model assumptions. In reality, these assumptions are not always correct. Robust methods can withstand model inaccuracies, that is, despite some incorrect assumptions they can still produce good results. We often want to know how robust employed methods are. To that end we need to have a yardstick for measuring robustness. In this paper, we propose an approach for constructing such metrics for sequential methods. These metrics are derived from the Kolmogorov-Smirnov distance between the cumulative distribution functions of the actual observations and the ones based on the assumed model. The use of the proposed metrics is demonstrated with simulation examples.},
keywords = {Additive noise, cumulative distribution functions, data processing method, extended Kalman filtering, Extraterrestrial measurements, Filtering, Gaussian distribution, Gaussian noise, Kalman filters, Kolmogorov-Smirnov distance, Least squares approximation, Noise robustness, nonlinear filters, robustness, sequential methods, statistical distributions, telecommunication computing},
pubstate = {published},
tppubtype = {inproceedings}
}
Whenever we apply methods for processing data, we make a number of model assumptions. In reality, these assumptions are not always correct. Robust methods can withstand model inaccuracies, that is, despite some incorrect assumptions they can still produce good results. We often want to know how robust employed methods are. To that end we need to have a yardstick for measuring robustness. In this paper, we propose an approach for constructing such metrics for sequential methods. These metrics are derived from the Kolmogorov-Smirnov distance between the cumulative distribution functions of the actual observations and the ones based on the assumed model. The use of the proposed metrics is demonstrated with simulation examples.
Martino, Luca; Miguez, Joaquin
New Accept/Reject Methods for Independent Sampling from Posterior Probability Distributions Artículo en actas
En: 17th European Signal Processing Conference (EUSIPCO 2009), Glasgow, 2009.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Martino2009a,
title = {New Accept/Reject Methods for Independent Sampling from Posterior Probability Distributions},
author = {Luca Martino and Joaquin Miguez},
url = {http://www.academia.edu/2355641/NEW_ACCEPT_REJECT_METHODS_FOR_INDEPENDENT_SAMPLING_FROM_POSTERIOR_PROBABILITY_DISTRIBUTIONS},
year = {2009},
date = {2009-01-01},
booktitle = {17th European Signal Processing Conference (EUSIPCO 2009)},
address = {Glasgow},
abstract = {Rejection sampling (RS) is a well-known method to generate(pseudo-)random samples from arbitrary probability distributionsthat enjoys important applications, either by itself or as a tool inmore sophisticated Monte Carlo techniques. Unfortunately, the useof RS techniques demands the calculation of tight upper bounds forthe ratio of the target probability density function (pdf) over theproposal density from which candidate samples are drawn. Exceptfor the class of log-concave target pdf’s, for which an efficientalgorithm exists, there are no general methods to analyticallydetermine this bound, which has to be derived from scratch foreach specific case. In this paper, we tackle the general problemof applying RS to draw from an arbitrary posterior pdf using theprior density as a proposal function. This is a scenario that appearsfrequently in Bayesian signal processing methods. We derive ageneral geometric procedure for the calculation of upper boundsthat can be used with a broad class of target pdf’s, includingscenarios with correlated observations, multimodal and/or mixturemeasurement noises. We provide some simple numerical examplesto illustrate the application of the proposed techniques},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Rejection sampling (RS) is a well-known method to generate(pseudo-)random samples from arbitrary probability distributionsthat enjoys important applications, either by itself or as a tool inmore sophisticated Monte Carlo techniques. Unfortunately, the useof RS techniques demands the calculation of tight upper bounds forthe ratio of the target probability density function (pdf) over theproposal density from which candidate samples are drawn. Exceptfor the class of log-concave target pdf’s, for which an efficientalgorithm exists, there are no general methods to analyticallydetermine this bound, which has to be derived from scratch foreach specific case. In this paper, we tackle the general problemof applying RS to draw from an arbitrary posterior pdf using theprior density as a proposal function. This is a scenario that appearsfrequently in Bayesian signal processing methods. We derive ageneral geometric procedure for the calculation of upper boundsthat can be used with a broad class of target pdf’s, includingscenarios with correlated observations, multimodal and/or mixturemeasurement noises. We provide some simple numerical examplesto illustrate the application of the proposed techniques
Perez-Cruz, Fernando; Kulkarni, S R
Distributed Least Square for Consensus Building in Sensor Networks Artículo en actas
En: 2009 IEEE International Symposium on Information Theory, pp. 2877–2881, IEEE, Seoul, 2009, ISBN: 978-1-4244-4312-3.
Resumen | Enlaces | BibTeX | Etiquetas: Change detection algorithms, Channel Coding, Distributed computing, distributed least square method, graphical models, Inference algorithms, Kernel, Least squares methods, nonparametric statistics, Parametric statistics, robustness, sensor-network learning, statistical analysis, Telecommunication network reliability, Wireless sensor network, Wireless Sensor Networks
@inproceedings{Perez-Cruz2009,
title = {Distributed Least Square for Consensus Building in Sensor Networks},
author = {Fernando Perez-Cruz and S R Kulkarni},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5205336},
isbn = {978-1-4244-4312-3},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE International Symposium on Information Theory},
pages = {2877--2881},
publisher = {IEEE},
address = {Seoul},
abstract = {We present a novel mechanism for consensus building in sensor networks. The proposed algorithm has three main properties that make it suitable for general 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 = {Change detection algorithms, Channel Coding, Distributed computing, distributed least square method, graphical models, Inference algorithms, Kernel, Least squares methods, nonparametric statistics, Parametric statistics, robustness, sensor-network learning, statistical analysis, Telecommunication network reliability, Wireless sensor network, Wireless Sensor Networks},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a novel mechanism for consensus building in sensor networks. The proposed algorithm has three main properties that make it suitable for general 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.
Fresia, Maria; Perez-Cruz, Fernando; Poor, Vincent H
Optimized Concatenated LDPC Codes for Joint Source-Channel Coding Artículo en actas
En: 2009 IEEE International Symposium on Information Theory, pp. 2131–2135, IEEE, Seoul, 2009, ISBN: 978-1-4244-4312-3.
Resumen | Enlaces | BibTeX | Etiquetas: approximation theory, asymptotic behavior analysis, Channel Coding, combined source-channel coding, Concatenated codes, Decoding, Entropy, EXIT chart, extrinsic information transfer, H infinity control, Information analysis, joint belief propagation decoder, joint source-channel coding, low-density-parity-check code, optimized concatenated independent LDPC codes, parity check codes, Redundancy, source coding, transmitter, Transmitters
@inproceedings{Fresia2009,
title = {Optimized Concatenated LDPC Codes for Joint Source-Channel Coding},
author = {Maria Fresia and Fernando Perez-Cruz and Vincent H Poor},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5205766},
isbn = {978-1-4244-4312-3},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE International Symposium on Information Theory},
pages = {2131--2135},
publisher = {IEEE},
address = {Seoul},
abstract = {In this paper a scheme for joint source-channel coding based on low-density-parity-check (LDPC) codes is investigated. Two concatenated independent LDPC codes are used in the transmitter: one for source coding and the other for channel coding, with a joint belief propagation decoder. The asymptotic behavior is analyzed using EXtrinsic Information Transfer (EXIT) charts and this approximation is corroborated with illustrative experiments. The optimization of the degree distributions for our sparse code to maximize the information transmission rate is also considered.},
keywords = {approximation theory, asymptotic behavior analysis, Channel Coding, combined source-channel coding, Concatenated codes, Decoding, Entropy, EXIT chart, extrinsic information transfer, H infinity control, Information analysis, joint belief propagation decoder, joint source-channel coding, low-density-parity-check code, optimized concatenated independent LDPC codes, parity check codes, Redundancy, source coding, transmitter, Transmitters},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper a scheme for joint source-channel coding based on low-density-parity-check (LDPC) codes is investigated. Two concatenated independent LDPC codes are used in the transmitter: one for source coding and the other for channel coding, with a joint belief propagation decoder. The asymptotic behavior is analyzed using EXtrinsic Information Transfer (EXIT) charts and this approximation is corroborated with illustrative experiments. The optimization of the degree distributions for our sparse code to maximize the information transmission rate is also considered.
Martino, Luca; Miguez, Joaquin
An Adaptive Accept/Reject Sampling Algorithm for Posterior Probability Distributions Artículo en actas
En: 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, pp. 45–48, IEEE, Cardiff, 2009, ISBN: 978-1-4244-2709-3.
Resumen | Enlaces | BibTeX | Etiquetas: adaptive accept/reject sampling, Adaptive rejection sampling, arbitrary target probability distributions, Computer Simulation, Filtering, Monte Carlo integration, Monte Carlo methods, posterior probability distributions, Probability, Probability density function, Probability distribution, Proposals, Rejection sampling, Sampling methods, sensor networks, Signal processing algorithms, signal sampling, Testing
@inproceedings{Martino2009b,
title = {An Adaptive Accept/Reject Sampling Algorithm for Posterior Probability Distributions},
author = {Luca Martino and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5278644},
isbn = {978-1-4244-2709-3},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE/SP 15th Workshop on Statistical Signal Processing},
pages = {45--48},
publisher = {IEEE},
address = {Cardiff},
abstract = {Accept/reject 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. In this paper we introduce an adaptive method to build a sequence of proposal pdf's that approximate the target density and hence can ensure a high acceptance rate. In order to illustrate the application of the method we design an accept/reject particle filter and then assess its performance and sampling efficiency numerically, by means of computer simulations.},
keywords = {adaptive accept/reject sampling, Adaptive rejection sampling, arbitrary target probability distributions, Computer Simulation, Filtering, Monte Carlo integration, Monte Carlo methods, posterior probability distributions, Probability, Probability density function, Probability distribution, Proposals, Rejection sampling, Sampling methods, sensor networks, Signal processing algorithms, signal sampling, Testing},
pubstate = {published},
tppubtype = {inproceedings}
}
Accept/reject 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. In this paper we introduce an adaptive method to build a sequence of proposal pdf's that approximate the target density and hence can ensure a high acceptance rate. In order to illustrate the application of the method we design an accept/reject particle filter and then assess its performance and sampling efficiency numerically, by means of computer simulations.
Vinuelas-Peris, Pablo; Artés-Rodríguez, Antonio
Sensing Matrix Optimization in Distributed Compressed Sensing Artículo en actas
En: 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, pp. 638–641, IEEE, Cardiff, 2009, ISBN: 978-1-4244-2709-3.
Resumen | Enlaces | BibTeX | Etiquetas: Compressed sensing, Computer Simulation, computer simulations, correlated signal, Correlated signals, correlation theory, Dictionaries, distributed coding strategy, distributed compressed sensing, Distributed control, efficient projection method, Encoding, joint recovery method, Matching pursuit algorithms, Optimization methods, orthogonal matching pursuit, Projection Matrix Optimization, sensing matrix optimization, Sensor Network, Sensor phenomena and characterization, Sensor systems, Signal processing, Sparse matrices, Technological innovation
@inproceedings{Vinuelas-Peris2009,
title = {Sensing Matrix Optimization in Distributed Compressed Sensing},
author = {Pablo Vinuelas-Peris and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5278496},
isbn = {978-1-4244-2709-3},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE/SP 15th Workshop on Statistical Signal Processing},
pages = {638--641},
publisher = {IEEE},
address = {Cardiff},
abstract = {Distributed compressed sensing (DCS) seeks to simultaneously measure signals that are each individually sparse in some domain(s) and also mutually correlated. In this paper we consider the scenario in which the (overcomplete) bases for common component and innovations are different. We propose and analyze a distributed coding strategy for the common component, and also the use of efficient projection (EP) method for optimizing the sensing matrices in this setting. We show the effectiveness of our approach by computer simulations using the orthogonal matching pursuit (OMP) as joint recovery method, and we discuss the configuration of the distribution strategy.},
keywords = {Compressed sensing, Computer Simulation, computer simulations, correlated signal, Correlated signals, correlation theory, Dictionaries, distributed coding strategy, distributed compressed sensing, Distributed control, efficient projection method, Encoding, joint recovery method, Matching pursuit algorithms, Optimization methods, orthogonal matching pursuit, Projection Matrix Optimization, sensing matrix optimization, Sensor Network, Sensor phenomena and characterization, Sensor systems, Signal processing, Sparse matrices, Technological innovation},
pubstate = {published},
tppubtype = {inproceedings}
}
Distributed compressed sensing (DCS) seeks to simultaneously measure signals that are each individually sparse in some domain(s) and also mutually correlated. In this paper we consider the scenario in which the (overcomplete) bases for common component and innovations are different. We propose and analyze a distributed coding strategy for the common component, and also the use of efficient projection (EP) method for optimizing the sensing matrices in this setting. We show the effectiveness of our approach by computer simulations using the orthogonal matching pursuit (OMP) as joint recovery method, and we discuss the configuration of the distribution strategy.
Perez-Cruz, Fernando; Rodrigues, Miguel R D; Verdu, Sergio
Optimal Precoding for Multiple-Input Multiple-Output Gaussian Channels Artículo en actas
En: Seminar PIIRS, Princeton, 2009.
Resumen | Enlaces | BibTeX | Etiquetas: Theory & Algorithms
@inproceedings{Perez-Cruz2009a,
title = {Optimal Precoding for Multiple-Input Multiple-Output Gaussian Channels},
author = {Fernando Perez-Cruz and Miguel R D Rodrigues and Sergio Verdu},
url = {http://eprints.pascal-network.org/archive/00006754/},
year = {2009},
date = {2009-01-01},
booktitle = {Seminar PIIRS},
address = {Princeton},
abstract = {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. The optimal linear precoder satisfies a fixed-point equation as a function of the channel and the input constellation. For nonGaussian 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 nonGaussian input distributions, but also for the interference among inputs.},
keywords = {Theory \& Algorithms},
pubstate = {published},
tppubtype = {inproceedings}
}
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. The optimal linear precoder satisfies a fixed-point equation as a function of the channel and the input constellation. For nonGaussian 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 nonGaussian input distributions, but also for the interference among inputs.
Miguez, Joaquin; Maiz, Cristina S; Djuric, Petar M; Crisan, Dan
Sequential Monte Carlo Optimization Using Artificial State-Space Models Artículo en actas
En: 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, pp. 268–273, IEEE, Marco Island, FL, 2009.
Resumen | Enlaces | BibTeX | Etiquetas: Acceleration, Cost function, Design optimization, discrete-time dynamical system, Educational institutions, Mathematics, maximum a posteriori estimate, maximum likelihood estimation, minimisation, Monte Carlo methods, Optimization methods, Probability distribution, sequential Monte Carlo optimization, Sequential optimization, Signal design, State-space methods, state-space model, Stochastic optimization
@inproceedings{Miguez2009,
title = {Sequential Monte Carlo Optimization Using Artificial State-Space Models},
author = {Joaquin Miguez and Cristina S Maiz and Petar M Djuric and Dan Crisan},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4785933},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop},
pages = {268--273},
publisher = {IEEE},
address = {Marco Island, FL},
abstract = {We introduce a method for sequential minimization of a certain class of (possibly non-convex) cost functions with respect to a high dimensional signal of interest. The proposed approach involves the transformation of the optimization problem into one of estimation in a discrete-time dynamical system. In particular, we describe a methodology for constructing an artificial state-space model which has the signal of interest as its unobserved dynamic state. The model is \"{a}dapted" to the cost function in the sense that the maximum a posteriori (MAP) estimate of the system state is also a global minimizer of the cost function. The advantage of the estimation framework is that we can draw from a pool of sequential Monte Carlo methods, for particle approximation of probability measures in dynamic systems, that enable the numerical computation of MAP estimates. We provide examples of how to apply the proposed methodology, including some illustrative simulation results.},
keywords = {Acceleration, Cost function, Design optimization, discrete-time dynamical system, Educational institutions, Mathematics, maximum a posteriori estimate, maximum likelihood estimation, minimisation, Monte Carlo methods, Optimization methods, Probability distribution, sequential Monte Carlo optimization, Sequential optimization, Signal design, State-space methods, state-space model, Stochastic optimization},
pubstate = {published},
tppubtype = {inproceedings}
}
We introduce a method for sequential minimization of a certain class of (possibly non-convex) cost functions with respect to a high dimensional signal of interest. The proposed approach involves the transformation of the optimization problem into one of estimation in a discrete-time dynamical system. In particular, we describe a methodology for constructing an artificial state-space model which has the signal of interest as its unobserved dynamic state. The model is ädapted" to the cost function in the sense that the maximum a posteriori (MAP) estimate of the system state is also a global minimizer of the cost function. The advantage of the estimation framework is that we can draw from a pool of sequential Monte Carlo methods, for particle approximation of probability measures in dynamic systems, that enable the numerical computation of MAP estimates. We provide examples of how to apply the proposed methodology, including some illustrative simulation results.
Fresia, Maria; Perez-Cruz, Fernando; Poor, Vincent H; Verdu, Sergio
Joint Source-Channel Coding with Concatenated LDPC Codes Artículo en actas
En: Information Theory and Applications (ITA), San Diego, 2009.
Resumen | Enlaces | BibTeX | Etiquetas: Learning/Statistics & Optimisation
@inproceedings{Fresia2009a,
title = {Joint Source-Channel Coding with Concatenated LDPC Codes},
author = {Maria Fresia and Fernando Perez-Cruz and Vincent H Poor and Sergio Verdu},
url = {http://eprints.pascal-network.org/archive/00004905/},
year = {2009},
date = {2009-01-01},
booktitle = {Information Theory and Applications (ITA)},
address = {San Diego},
abstract = {The separation principle, a milestone in information theory, establishes that for stationary sources and channels there is no loss of optimality when a channel-independent source encoder followed by a source-independent channel encoder are used to transmit the data, as the code length tends to infinity. Thereby, the source and channel encoding have been typically treated as independent problems. For finite-length codes, the separation principle does not hold and a joint encoder and decoder can potentially increase the achieved information transmission rate. In this paper, a scheme for joint source-channel coding based on low-density parity-check (LDPC) codes is presented. The source is compressed and protected with two concatenated LDPC codes and a joint belief propagation decoder is implemented. EXIT chart performance of the proposed schemes is studied. The results are verified with some illustrative experiments.},
keywords = {Learning/Statistics \& Optimisation},
pubstate = {published},
tppubtype = {inproceedings}
}
The separation principle, a milestone in information theory, establishes that for stationary sources and channels there is no loss of optimality when a channel-independent source encoder followed by a source-independent channel encoder are used to transmit the data, as the code length tends to infinity. Thereby, the source and channel encoding have been typically treated as independent problems. For finite-length codes, the separation principle does not hold and a joint encoder and decoder can potentially increase the achieved information transmission rate. In this paper, a scheme for joint source-channel coding based on low-density parity-check (LDPC) codes is presented. The source is compressed and protected with two concatenated LDPC codes and a joint belief propagation decoder is implemented. EXIT chart performance of the proposed schemes is studied. The results are verified with some illustrative experiments.
Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando
Gaussian Process Regressors for Multiuser Detection in DS-CDMA Systems Artículo de revista
En: IEEE Transactions on Communications, vol. 57, no. 8, pp. 2339–2347, 2009, ISSN: 0090-6778.
Resumen | Enlaces | BibTeX | Etiquetas: 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
@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}
}
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.
Mariño, Inés P.; Miguez, Joaquin; Meucci, Riccardo
Monte Carlo Method for Adaptively Estimating the Unknown Parameters and the Dynamic State of Chaotic Systems Artículo de revista
En: Physical Review E, vol. 79, no. 5, pp. 056218, 2009, ISSN: 1539-3755.
Resumen | Enlaces | BibTeX | Etiquetas:
@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 CO2We 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.
Vazquez, Manuel A; Miguez, Joaquin
Maximum-Likelihood Sequence Detection in Time- and Frequency-Selective MIMO Channels With Unknown Order Artículo de revista
En: IEEE Transactions on Vehicular Technology, vol. 58, no. 1, pp. 499–504, 2009, ISSN: 0018-9545.
Resumen | Enlaces | BibTeX | Etiquetas: 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–multiple-output (MIMO), per-survivor processing, per-survivor processing (PSP), telecommunication channels, time-selective multiple-input-multiple-output chan
@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}
}
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.
Koch, Tobias; Lapidoth, Amos; Sotiriadis, Paul P
Channels That Heat Up Artículo de revista
En: IEEE Transactions on Information Theory, vol. 55, no. 8, pp. 3594–3612, 2009, ISSN: 0018-9448.
Resumen | Enlaces | BibTeX | Etiquetas: 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
@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}
}
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.
Lázaro, Marcelino; González-Olasola, Jonathan
Blind Equalization Using the IRWLS Formulation of the Support Vector Machine Artículo de revista
En: Signal Processing, vol. 89, no. 7, pp. 1436–1445, 2009, ISSN: 01651684.
Resumen | Enlaces | BibTeX | Etiquetas:
@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}
}
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.
Goez, Roger; Lazaro, Marcelino
Training of Neural Classifiers by Separating Distributions at the Hidden Layer Artículo en actas
En: 2009 IEEE International Workshop on Machine Learning for Signal Processing, pp. 1–6, IEEE, Grenoble, 2009, ISBN: 978-1-4244-4947-7.
Resumen | Enlaces | BibTeX | Etiquetas: Artificial neural networks, Bayesian methods, Cost function, Curve fitting, Databases, Function approximation, Neural networks, Speech recognition, Support vector machine classification, Support vector machines
@inproceedings{Goez2009,
title = {Training of Neural Classifiers by Separating Distributions at the Hidden Layer},
author = {Roger Goez and Marcelino Lazaro},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5306240},
isbn = {978-1-4244-4947-7},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE International Workshop on Machine Learning for Signal Processing},
pages = {1--6},
publisher = {IEEE},
address = {Grenoble},
abstract = {A new cost function for training of binary classifiers based on neural networks is proposed. This cost function aims at separating the distributions for patterns of each class at the output of the hidden layer of the network. It has been implemented in a Generalized Radial Basis Function (GRBF) network and its performance has been evaluated under three different databases, showing advantages with respect to the conventional Mean Squared Error (MSE) cost function. With respect to the Support Vector Machine (SVM) classifier, the proposed method has also advantages both in terms of performance and complexity.},
keywords = {Artificial neural networks, Bayesian methods, Cost function, Curve fitting, Databases, Function approximation, Neural networks, Speech recognition, Support vector machine classification, Support vector machines},
pubstate = {published},
tppubtype = {inproceedings}
}
A new cost function for training of binary classifiers based on neural networks is proposed. This cost function aims at separating the distributions for patterns of each class at the output of the hidden layer of the network. It has been implemented in a Generalized Radial Basis Function (GRBF) network and its performance has been evaluated under three different databases, showing advantages with respect to the conventional Mean Squared Error (MSE) cost function. With respect to the Support Vector Machine (SVM) classifier, the proposed method has also advantages both in terms of performance and complexity.
Plata-Chaves, Jorge; Lazaro, Marcelino
Closed-Form Error Exponent for the Neyman-Pearson Fusion of Markov Local Decisions Artículo en actas
En: 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, pp. 533–536, IEEE, Cardiff, 2009, ISBN: 978-1-4244-2709-3.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Plata-Chaves2009,
title = {Closed-Form Error Exponent for the Neyman-Pearson Fusion of Markov Local Decisions},
author = {Jorge Plata-Chaves and Marcelino Lazaro},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=5278522},
isbn = {978-1-4244-2709-3},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE/SP 15th Workshop on Statistical Signal Processing},
pages = {533--536},
publisher = {IEEE},
address = {Cardiff},
abstract = {In this correspondence, we derive a closed-form expression of the error exponent associated with the binary Neyman-Pearson test performed at the fusion center of a distributed detection system where a large number of local detectors take dependent binary decisions regarding a specific phenomenon. We assume that the sensors are equally spaced along a straight line, that their local decisions are taken with no kind of cooperation, and that they are transmitted to the fusion center over an error free parallel access channel. Under each one of the two possible hypothesis, H0 and H1 the correlation structure of the local binary decisions is modelled with a first-order binary Markov chain whose transition probabilities are linked with different physical parameters of the network. Through different simulations based on the error exponent and a deterministic physical model of the aforementioned transition probabilities we study the effect of network density on the overall detection performance.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this correspondence, we derive a closed-form expression of the error exponent associated with the binary Neyman-Pearson test performed at the fusion center of a distributed detection system where a large number of local detectors take dependent binary decisions regarding a specific phenomenon. We assume that the sensors are equally spaced along a straight line, that their local decisions are taken with no kind of cooperation, and that they are transmitted to the fusion center over an error free parallel access channel. Under each one of the two possible hypothesis, H0 and H1 the correlation structure of the local binary decisions is modelled with a first-order binary Markov chain whose transition probabilities are linked with different physical parameters of the network. Through different simulations based on the error exponent and a deterministic physical model of the aforementioned transition probabilities we study the effect of network density on the overall detection performance.
Alvarez, Mauricio; Luengo, David; Lawrence, Neil D
Latent Force Models Artículo en actas
En: Conf. on Artificial Intelligence and Statistics, Clearwater Beach, 2009.
BibTeX | Etiquetas:
@inproceedings{Alvarez2009,
title = {Latent Force Models},
author = {Mauricio Alvarez and David Luengo and Neil D Lawrence},
year = {2009},
date = {2009-01-01},
booktitle = {Conf. on Artificial Intelligence and Statistics},
address = {Clearwater Beach},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Lazaro, Marcelino; Sanchez-Fernandez, Matilde; Artés-Rodríguez, Antonio
Optimal Sensor Selection in Binary Heterogeneous Sensor Networks Artículo de revista
En: IEEE Transactions on Signal Processing, vol. 57, no. 4, pp. 1577–1587, 2009, ISSN: 1053-587X.
Resumen | Enlaces | BibTeX | Etiquetas: 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
@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}
}
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.
2008
Vazquez-Vilar, Gonzalo; Majjigi, Vinay; Sezgin, Aydin; Paulraj, Arogyaswami
Mobility Dependent Feedback Scheme for point-to-point MIMO Systems Artículo en actas
En: Asilomar Conference on Signals, Systems, and Computers (Asilomar SSC 2008), Pacific Grove, CA, U.S.A., 2008.
BibTeX | Etiquetas:
@inproceedings{asilomar2008,
title = {Mobility Dependent Feedback Scheme for point-to-point MIMO Systems},
author = {Gonzalo Vazquez-Vilar and Vinay Majjigi and Aydin Sezgin and Arogyaswami Paulraj},
year = {2008},
date = {2008-10-01},
booktitle = {Asilomar Conference on Signals, Systems, and Computers (Asilomar SSC 2008)},
address = {Pacific Grove, CA, U.S.A.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Koch, Tobias; Lapidoth, Amos
On Multipath Fading Channels at High SNR Artículo en actas
En: 2008 IEEE International Symposium on Information Theory, pp. 1572–1576, IEEE, Toronto, 2008, ISBN: 978-1-4244-2256-2.
Resumen | Enlaces | BibTeX | Etiquetas: channel capacity, Delay, discrete time systems, discrete-time channels, Entropy, Fading, fading channels, Frequency, Mathematical model, multipath channels, multipath fading channels, noncoherent channel model, Random variables, Signal to noise ratio, signal-to-noise ratios, SNR, statistics, Transmitters
@inproceedings{Koch2008,
title = {On Multipath Fading Channels at High SNR},
author = {Tobias Koch and Amos Lapidoth},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=4595252},
isbn = {978-1-4244-2256-2},
year = {2008},
date = {2008-01-01},
booktitle = {2008 IEEE International Symposium on Information Theory},
pages = {1572--1576},
publisher = {IEEE},
address = {Toronto},
abstract = {This paper studies the capacity of discrete-time multipath fading channels. It is assumed that the number of paths is finite, i.e., that the channel output is influenced by the present and by the L previous channel inputs. A noncoherent channel model is considered where neither transmitter nor receiver are cognizant of the fading's realization, but both are aware of its statistic. The focus is on capacity at high signal-to-noise ratios (SNR). In particular, the capacity pre-loglog-defined as the limiting ratio of the capacity to loglog(SNR) as SNR tends to infinity-is studied. It is shown that, irrespective of the number of paths L, the capacity pre-loglog is 1.},
keywords = {channel capacity, Delay, discrete time systems, discrete-time channels, Entropy, Fading, fading channels, Frequency, Mathematical model, multipath channels, multipath fading channels, noncoherent channel model, Random variables, Signal to noise ratio, signal-to-noise ratios, SNR, statistics, Transmitters},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper studies the capacity of discrete-time multipath fading channels. It is assumed that the number of paths is finite, i.e., that the channel output is influenced by the present and by the L previous channel inputs. A noncoherent channel model is considered where neither transmitter nor receiver are cognizant of the fading's realization, but both are aware of its statistic. The focus is on capacity at high signal-to-noise ratios (SNR). In particular, the capacity pre-loglog-defined as the limiting ratio of the capacity to loglog(SNR) as SNR tends to infinity-is studied. It is shown that, irrespective of the number of paths L, the capacity pre-loglog is 1.
Vazquez, Manuel A; Miguez, Joaquin
A Per-Survivor Processing Algorithm for Maximum Likelihood Equalization of MIMO Channels with Unknown Order Artículo en actas
En: 2008 International ITG Workshop on Smart Antennas, pp. 387–391, IEEE, Vienna, 2008, ISBN: 978-1-4244-1756-8.
Resumen | Enlaces | BibTeX | Etiquetas: Channel estimation, channel impulse response, computational complexity, Computer science education, Computer Simulation, Degradation, Frequency, frequency-selective multiple-input multiple-output, maximum likelihood detection, maximum likelihood equalization, maximum likelihood estimation, maximum likelihood sequence detection, maximum likelihood sequence estimation, MIMO, MIMO channels, MIMO communication, per-survivor processing algorithm, time-selective channels, Transmitting antennas
@inproceedings{Vazquez2008,
title = {A Per-Survivor Processing Algorithm for Maximum Likelihood Equalization of MIMO Channels with Unknown Order},
author = {Manuel A Vazquez and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=4475587},
isbn = {978-1-4244-1756-8},
year = {2008},
date = {2008-01-01},
booktitle = {2008 International ITG Workshop on Smart Antennas},
pages = {387--391},
publisher = {IEEE},
address = {Vienna},
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), also referred to as the channel order, is known. However, this is not true in most practical situations and, in order to avoid the serious performance degradation that occurs when the CIR length is underestimated, a channel with "more than enough" taps is usually considered. This possibly means overestimating the channel order, and is not desirable since the computational complexity of maximum likelihood sequence detection (MLSD) in frequency-selective channels grows exponentially with the channel order. In addition to that, the higher the channel order considered, the more the number of channel coefficients that need to be estimated from the same set of observations. 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. Besides the analytical derivation of the algorithm, we provide computer simulation results that illustrate the effectiveness of the resulting receiver},
keywords = {Channel estimation, channel impulse response, computational complexity, Computer science education, Computer Simulation, Degradation, Frequency, frequency-selective multiple-input multiple-output, maximum likelihood detection, maximum likelihood equalization, maximum likelihood estimation, maximum likelihood sequence detection, maximum likelihood sequence estimation, MIMO, MIMO channels, MIMO communication, per-survivor processing algorithm, time-selective channels, Transmitting antennas},
pubstate = {published},
tppubtype = {inproceedings}
}
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), also referred to as the channel order, is known. However, this is not true in most practical situations and, in order to avoid the serious performance degradation that occurs when the CIR length is underestimated, a channel with "more than enough" taps is usually considered. This possibly means overestimating the channel order, and is not desirable since the computational complexity of maximum likelihood sequence detection (MLSD) in frequency-selective channels grows exponentially with the channel order. In addition to that, the higher the channel order considered, the more the number of channel coefficients that need to be estimated from the same set of observations. 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. Besides the analytical derivation of the algorithm, we provide computer simulation results that illustrate the effectiveness of the resulting receiver
Miguez, Joaquin
Analysis of a Sequential Monte Carlo Optimization Methodology Artículo en actas
En: 16th European Signal Processing Conference (EUSIPCO 2008, Lausanne, 2008.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Miguez2008,
title = {Analysis of a Sequential Monte Carlo Optimization Methodology},
author = {Joaquin Miguez},
url = {http://www.eurasip.org/Proceedings/Eusipco/Eusipco2008/papers/1569105254.pdf},
year = {2008},
date = {2008-01-01},
booktitle = {16th European Signal Processing Conference (EUSIPCO 2008},
address = {Lausanne},
abstract = {We investigate a family of stochastic exploration methods that has been recently proposed to carry out estimation and prediction in discrete-time random dynamical systems. The key of the novel approach is to identify a cost function whose minima provide valid estimates of the system state at successive time instants. This function is recursively optimized using a sequential Monte Carlo minimization (SMCM) procedure which is similar to standard particle filtering algorithms but does not require a explicit probabilistic model to be imposed on the system. In this paper, we analyze the asymptotic convergence of SMCM methods and show that a properly designed algorithm produces a sequence of system-state estimates with individually minimal contributions to the cost function. We apply the SMCM method to a target tracking problem in order to illustrate how convergence is achieved in the way predicted by the theory.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We investigate a family of stochastic exploration methods that has been recently proposed to carry out estimation and prediction in discrete-time random dynamical systems. The key of the novel approach is to identify a cost function whose minima provide valid estimates of the system state at successive time instants. This function is recursively optimized using a sequential Monte Carlo minimization (SMCM) procedure which is similar to standard particle filtering algorithms but does not require a explicit probabilistic model to be imposed on the system. In this paper, we analyze the asymptotic convergence of SMCM methods and show that a properly designed algorithm produces a sequence of system-state estimates with individually minimal contributions to the cost function. We apply the SMCM method to a target tracking problem in order to illustrate how convergence is achieved in the way predicted by the theory.
Perez-Cruz, Fernando
Kullback-Leibler Divergence Estimation of Continuous Distributions Artículo en actas
En: 2008 IEEE International Symposium on Information Theory, pp. 1666–1670, IEEE, Toronto, 2008, ISBN: 978-1-4244-2256-2.
Resumen | Enlaces | BibTeX | Etiquetas: Convergence, density estimation, Density measurement, Entropy, Frequency estimation, H infinity control, information theory, k-nearest-neighbour density estimation, Kullback-Leibler divergence estimation, Machine learning, Mutual information, neuroscience, Random variables, statistical distributions, waiting-times distributions
@inproceedings{Perez-Cruz2008,
title = {Kullback-Leibler Divergence Estimation of Continuous Distributions},
author = {Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4595271},
isbn = {978-1-4244-2256-2},
year = {2008},
date = {2008-01-01},
booktitle = {2008 IEEE International Symposium on Information Theory},
pages = {1666--1670},
publisher = {IEEE},
address = {Toronto},
abstract = {We present a method for estimating the KL divergence between continuous densities and we prove it converges almost surely. Divergence estimation is typically solved estimating the densities first. Our main result shows this intermediate step is unnecessary and that the divergence can be either estimated using the empirical cdf or k-nearest-neighbour density estimation, which does not converge to the true measure for finite k. The convergence proof is based on describing the statistics of our estimator using waiting-times distributions, as the exponential or Erlang. We illustrate the proposed estimators and show how they compare to existing methods based on density estimation, and we also outline how our divergence estimators can be used for solving the two-sample problem.},
keywords = {Convergence, density estimation, Density measurement, Entropy, Frequency estimation, H infinity control, information theory, k-nearest-neighbour density estimation, Kullback-Leibler divergence estimation, Machine learning, Mutual information, neuroscience, Random variables, statistical distributions, waiting-times distributions},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a method for estimating the KL divergence between continuous densities and we prove it converges almost surely. Divergence estimation is typically solved estimating the densities first. Our main result shows this intermediate step is unnecessary and that the divergence can be either estimated using the empirical cdf or k-nearest-neighbour density estimation, which does not converge to the true measure for finite k. The convergence proof is based on describing the statistics of our estimator using waiting-times distributions, as the exponential or Erlang. We illustrate the proposed estimators and show how they compare to existing methods based on density estimation, and we also outline how our divergence estimators can be used for solving the two-sample problem.
Perez-Cruz, Fernando; Rodrigues, Miguel R D; Verdu, Sergio
Optimal Precoding for Digital Subscriber Lines Artículo en actas
En: 2008 IEEE International Conference on Communications, pp. 1200–1204, IEEE, Beijing, 2008, ISBN: 978-1-4244-2075-9.
Resumen | Enlaces | BibTeX | Etiquetas: Bit error rate, channel matrix diagonalization, Communications Society, Computer science, digital subscriber lines, DSL, Equations, fixed-point equation, Gaussian channels, least mean squares methods, linear codes, matrix algebra, MIMO, MIMO communication, MIMO Gaussian channel, minimum mean squared error method, MMSE, multiple-input multiple-output communication, Mutual information, optimal linear precoder, precoding, Telecommunications, Telephony
@inproceedings{Perez-Cruz2008a,
title = {Optimal Precoding for Digital Subscriber Lines},
author = {Fernando Perez-Cruz and Miguel R D Rodrigues and Sergio Verdu},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4533270},
isbn = {978-1-4244-2075-9},
year = {2008},
date = {2008-01-01},
booktitle = {2008 IEEE International Conference on Communications},
pages = {1200--1204},
publisher = {IEEE},
address = {Beijing},
abstract = {We determine the linear precoding policy that maximizes 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 squared error (MMSE). The optimal linear precoder can be computed by means of a fixed- point equation as a function of the channel and the input constellation. We show that diagonalizing the channel matrix does not maximize the information transmission rate for nonGaussian inputs. A full precoding matrix may significantly increase the information transmission rate, even for parallel non-interacting channels. We illustrate the application of our results to typical Gigabit DSL systems.},
keywords = {Bit error rate, channel matrix diagonalization, Communications Society, Computer science, digital subscriber lines, DSL, Equations, fixed-point equation, Gaussian channels, least mean squares methods, linear codes, matrix algebra, MIMO, MIMO communication, MIMO Gaussian channel, minimum mean squared error method, MMSE, multiple-input multiple-output communication, Mutual information, optimal linear precoder, precoding, Telecommunications, Telephony},
pubstate = {published},
tppubtype = {inproceedings}
}
We determine the linear precoding policy that maximizes 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 squared error (MMSE). The optimal linear precoder can be computed by means of a fixed- point equation as a function of the channel and the input constellation. We show that diagonalizing the channel matrix does not maximize the information transmission rate for nonGaussian inputs. A full precoding matrix may significantly increase the information transmission rate, even for parallel non-interacting channels. We illustrate the application of our results to typical Gigabit DSL systems.
Koch, Tobias; Lapidoth, Amos
Multipath Channels of Bounded Capacity Artículo en actas
En: 2008 IEEE Information Theory Workshop, pp. 6–10, IEEE, Oporto, 2008, ISBN: 978-1-4244-2269-2.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Koch2008a,
title = {Multipath Channels of Bounded Capacity},
author = {Tobias Koch and Amos Lapidoth},
url = {http://www.researchgate.net/publication/4353168_Multipath_channels_of_bounded_capacity},
isbn = {978-1-4244-2269-2},
year = {2008},
date = {2008-01-01},
booktitle = {2008 IEEE Information Theory Workshop},
pages = {6--10},
publisher = {IEEE},
address = {Oporto},
abstract = {The capacity of discrete-time, non-coherent, multi-path fading channels is considered. It is shown that if the delay spread is large in the sense that the variances of the path gains do not decay faster than geometrically, then capacity is bounded in the signal-to-noise ratio.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The capacity of discrete-time, non-coherent, multi-path fading channels is considered. It is shown that if the delay spread is large in the sense that the variances of the path gains do not decay faster than geometrically, then capacity is bounded in the signal-to-noise ratio.
Leiva-murillo, Jose M; Artés-Rodríguez, Antonio
Linear Dimensionality Reduction With Gausian Mixture Models Artículo en actas
En: Cognitive Information Processing, (CIP) 2008, Santorini, 2008.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{JoseM.Leiva-murillo2008,
title = {Linear Dimensionality Reduction With Gausian Mixture Models},
author = {Jose M Leiva-murillo and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.167.798},
year = {2008},
date = {2008-01-01},
booktitle = {Cognitive Information Processing, (CIP) 2008},
address = {Santorini},
abstract = {In this paper, we explore the application of several informationtheoretic criteria to the problem of reducing the dimension in pattern recognition. We consider the use of Gaussian mixture models for estimating the distribution of the data. Three algorithms are proposed for linear feature extraction by the maximization of the mutual information, the likelihood or the hypotheses test, respectively. The experiments show that the proposed methods outperform the classical methods based on parametric Gaussian models, and avoid the intense computational complexity of nonparametric kernel density estimators.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we explore the application of several informationtheoretic criteria to the problem of reducing the dimension in pattern recognition. We consider the use of Gaussian mixture models for estimating the distribution of the data. Three algorithms are proposed for linear feature extraction by the maximization of the mutual information, the likelihood or the hypotheses test, respectively. The experiments show that the proposed methods outperform the classical methods based on parametric Gaussian models, and avoid the intense computational complexity of nonparametric kernel density estimators.
Koch, Tobias; Lapidoth, Amos
Multipath Channels of Unbounded Capacity Artículo en actas
En: 2008 IEEE 25th Convention of Electrical and Electronics Engineers in Israel, pp. 640–644, IEEE, Eilat, 2008, ISBN: 978-1-4244-2481-8.
Resumen | Enlaces | BibTeX | Etiquetas: channel capacity, discrete-time capacity, Entropy, Fading, fading channels, Frequency, H infinity control, Information rates, multipath channels, multipath fading channels, noncoherent, noncoherent capacity, path gains decay, Signal to noise ratio, statistics, Transmitters, unbounded capacity
@inproceedings{Koch2008b,
title = {Multipath Channels of Unbounded Capacity},
author = {Tobias Koch and Amos Lapidoth},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=4736611},
isbn = {978-1-4244-2481-8},
year = {2008},
date = {2008-01-01},
booktitle = {2008 IEEE 25th Convention of Electrical and Electronics Engineers in Israel},
pages = {640--644},
publisher = {IEEE},
address = {Eilat},
abstract = {The capacity of discrete-time, noncoherent, multipath fading channels is considered. It is shown that if the variances of the path gains decay faster than exponentially, then capacity is unbounded in the transmit power.},
keywords = {channel capacity, discrete-time capacity, Entropy, Fading, fading channels, Frequency, H infinity control, Information rates, multipath channels, multipath fading channels, noncoherent, noncoherent capacity, path gains decay, Signal to noise ratio, statistics, Transmitters, unbounded capacity},
pubstate = {published},
tppubtype = {inproceedings}
}
The capacity of discrete-time, noncoherent, multipath fading channels is considered. It is shown that if the variances of the path gains decay faster than exponentially, then capacity is unbounded in the transmit power.