2015
Martino, Luca; Elvira, Victor; Luengo, David; Artés-Rodríguez, Antonio; Corander, Jukka
Smelly Parallel MCMC Chains Proceedings Article
En: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4070–4074, IEEE, Brisbane, 2015, ISBN: 978-1-4673-6997-8.
Resumen | Enlaces | BibTeX | Etiquetas: Bayesian inference, learning (artificial intelligence), Machine learning, Markov chain Monte Carlo, Markov chain Monte Carlo algorithms, Markov processes, MC methods, MCMC algorithms, MCMC scheme, mean square error, mean square error methods, Monte Carlo methods, optimisation, parallel and interacting chains, Probability density function, Proposals, robustness, Sampling methods, Signal processing, Signal processing algorithms, signal sampling, smelly parallel chains, smelly parallel MCMC chains, Stochastic optimization
@inproceedings{Martino2015a,
title = {Smelly Parallel MCMC Chains},
author = {Luca Martino and Victor Elvira and David Luengo and Antonio Art\'{e}s-Rodr\'{i}guez and Jukka Corander},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7178736 http://www.tsc.uc3m.es/~velvira/papers/ICASSP2015_martino.pdf},
doi = {10.1109/ICASSP.2015.7178736},
isbn = {978-1-4673-6997-8},
year = {2015},
date = {2015-04-01},
booktitle = {2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {4070--4074},
publisher = {IEEE},
address = {Brisbane},
abstract = {Monte Carlo (MC) methods are useful tools for Bayesian inference and stochastic optimization that have been widely applied in signal processing and machine learning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce a novel parallel interacting MCMC scheme, where the parallel chains share information, thus yielding a faster exploration of the state space. The interaction is carried out generating a dynamic repulsion among the “smelly” parallel chains that takes into account the entire population of current states. The ergodicity of the scheme and its relationship with other sampling methods are discussed. Numerical results show the advantages of the proposed approach in terms of mean square error, robustness w.r.t. to initial values and parameter choice.},
keywords = {Bayesian inference, learning (artificial intelligence), Machine learning, Markov chain Monte Carlo, Markov chain Monte Carlo algorithms, Markov processes, MC methods, MCMC algorithms, MCMC scheme, mean square error, mean square error methods, Monte Carlo methods, optimisation, parallel and interacting chains, Probability density function, Proposals, robustness, Sampling methods, Signal processing, Signal processing algorithms, signal sampling, smelly parallel chains, smelly parallel MCMC chains, Stochastic optimization},
pubstate = {published},
tppubtype = {inproceedings}
}
Monte Carlo (MC) methods are useful tools for Bayesian inference and stochastic optimization that have been widely applied in signal processing and machine learning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce a novel parallel interacting MCMC scheme, where the parallel chains share information, thus yielding a faster exploration of the state space. The interaction is carried out generating a dynamic repulsion among the “smelly” parallel chains that takes into account the entire population of current states. The ergodicity of the scheme and its relationship with other sampling methods are discussed. Numerical results show the advantages of the proposed approach in terms of mean square error, robustness w.r.t. to initial values and parameter choice.
Ruiz, Francisco J R; Perez-Cruz, Fernando
A Generative Model for Predicting Outcomes in College Basketball Artículo de revista
En: Journal of Quantitative Analysis in Sports, vol. 11, no 1 Special Issue, pp. 39–52, 2015, ISSN: 1559-0410.
Resumen | Enlaces | BibTeX | Etiquetas: CASI CAM CM, GAMMA-L+ UC3M, Journal, NCAA tournament, Poisson factorization, Probabilistic modeling, variational inference
@article{Ruiz2015b,
title = {A Generative Model for Predicting Outcomes in College Basketball},
author = {Francisco J R Ruiz and Fernando Perez-Cruz},
url = {http://www.degruyter.com/view/j/jqas.2015.11.issue-1/jqas-2014-0055/jqas-2014-0055.xml},
doi = {10.1515/jqas-2014-0055},
issn = {1559-0410},
year = {2015},
date = {2015-03-01},
journal = {Journal of Quantitative Analysis in Sports},
volume = {11},
number = {1 Special Issue},
pages = {39--52},
abstract = {We show that a classical model for soccer can also provide competitive results in predicting basketball outcomes. We modify the classical model in two ways in order to capture both the specific behavior of each National collegiate athletic association (NCAA) conference and different strategies of teams and conferences. Through simulated bets on six online betting houses, we show that this extension leads to better predictive performance in terms of profit we make. We compare our estimates with the probabilities predicted by the winner of the recent Kaggle competition on the 2014 NCAA tournament, and conclude that our model tends to provide results that differ more from the implicit probabilities of the betting houses and, therefore, has the potential to provide higher benefits.},
keywords = {CASI CAM CM, GAMMA-L+ UC3M, Journal, NCAA tournament, Poisson factorization, Probabilistic modeling, variational inference},
pubstate = {published},
tppubtype = {article}
}
We show that a classical model for soccer can also provide competitive results in predicting basketball outcomes. We modify the classical model in two ways in order to capture both the specific behavior of each National collegiate athletic association (NCAA) conference and different strategies of teams and conferences. Through simulated bets on six online betting houses, we show that this extension leads to better predictive performance in terms of profit we make. We compare our estimates with the probabilities predicted by the winner of the recent Kaggle competition on the 2014 NCAA tournament, and conclude that our model tends to provide results that differ more from the implicit probabilities of the betting houses and, therefore, has the potential to provide higher benefits.
Koblents, Eugenia; Miguez, Joaquin
A Population Monte Carlo Scheme with Transformed Weights and Its Application to Stochastic Kinetic Models Artículo de revista
En: Statistics and Computing, vol. 25, no 2, pp. 407–425, 2015, ISSN: 0960-3174.
Resumen | Enlaces | BibTeX | Etiquetas: COMPREHENSION, degeneracy of importance weights, Importance sampling, Journal, population Monte Carlo, Stochastic kinetic models
@article{Koblents2014b,
title = {A Population Monte Carlo Scheme with Transformed Weights and Its Application to Stochastic Kinetic Models},
author = {Eugenia Koblents and Joaquin Miguez},
url = {http://link.springer.com/10.1007/s11222-013-9440-2 http://gts.tsc.uc3m.es/wp-content/uploads/2014/01/NPMC_A-population-Monte-Carlo-scheme-with-transformed_jma.pdf},
doi = {10.1007/s11222-013-9440-2},
issn = {0960-3174},
year = {2015},
date = {2015-03-01},
journal = {Statistics and Computing},
volume = {25},
number = {2},
pages = {407--425},
abstract = {This paper addresses the Monte Carlo approximation of posterior probability distributions. In particular, we consider the population Monte Carlo (PMC) technique, which is based on an iterative importance sampling (IS) approach. An important drawback of this methodology is the degeneracy of the importance weights (IWs) when the dimension of either the observations or the variables of interest is high. To alleviate this difficulty, we propose a new method that performs a nonlinear transformation of the IWs. This operation reduces the weight variation, hence it avoids degeneracy and increases the efficiency of the IS scheme, specially when drawing from proposal functions which are poorly adapted to the true posterior. For the sake of illustration, we have applied the proposed algorithm to the estimation of the parameters of a Gaussian mixture model. This is a simple problem that enables us to discuss the main features of the proposed technique. As a practical application, we have also considered the challenging problem of estimating the rate parameters of a stochastic kinetic model (SKM). SKMs are multivariate systems that model molecular interactions in biological and chemical problems. We introduce a particularization of the proposed algorithm to SKMs and present numerical results.},
keywords = {COMPREHENSION, degeneracy of importance weights, Importance sampling, Journal, population Monte Carlo, Stochastic kinetic models},
pubstate = {published},
tppubtype = {article}
}
This paper addresses the Monte Carlo approximation of posterior probability distributions. In particular, we consider the population Monte Carlo (PMC) technique, which is based on an iterative importance sampling (IS) approach. An important drawback of this methodology is the degeneracy of the importance weights (IWs) when the dimension of either the observations or the variables of interest is high. To alleviate this difficulty, we propose a new method that performs a nonlinear transformation of the IWs. This operation reduces the weight variation, hence it avoids degeneracy and increases the efficiency of the IS scheme, specially when drawing from proposal functions which are poorly adapted to the true posterior. For the sake of illustration, we have applied the proposed algorithm to the estimation of the parameters of a Gaussian mixture model. This is a simple problem that enables us to discuss the main features of the proposed technique. As a practical application, we have also considered the challenging problem of estimating the rate parameters of a stochastic kinetic model (SKM). SKMs are multivariate systems that model molecular interactions in biological and chemical problems. We introduce a particularization of the proposed algorithm to SKMs and present numerical results.
Varando, Gherardo; López-Cruz, Pedro L; Nielsen, Thomas D; Larrañaga, Pedro; Bielza, Concha
Conditional Density Approximations with Mixtures of Polynomials Artículo de revista
En: International Journal of Intelligent Systems, vol. 30, no 3, pp. 236–264, 2015, ISSN: 08848173.
Resumen | Enlaces | BibTeX | Etiquetas: CASI CAM CM, CIG UPM, Journal
@article{Varando2015b,
title = {Conditional Density Approximations with Mixtures of Polynomials},
author = {Gherardo Varando and Pedro L L\'{o}pez-Cruz and Thomas D Nielsen and Pedro Larra\~{n}aga and Concha Bielza},
url = {http://doi.wiley.com/10.1002/int.21699 http://cig.fi.upm.es/articles/2015/Varando-2015-IJIS.pdf},
doi = {10.1002/int.21699},
issn = {08848173},
year = {2015},
date = {2015-03-01},
journal = {International Journal of Intelligent Systems},
volume = {30},
number = {3},
pages = {236--264},
abstract = {Mixtures of polynomials (MoPs) are a nonparametric density estimation technique especially designed for hybrid Bayesian networks with continuous and discrete variables. Algorithms to learn one- and multidimensional (marginal) MoPs from data have recently been proposed. In this paper, we introduce two methods for learning MoP approximations of conditional densities from data. Both approaches are based on learning MoP approximations of the joint density and the marginal density of the conditioning variables, but they differ as to how the MoP approximation of the quotient of the two densities is found. We illustrate and study the methods using data sampled from known parametric distributions, and demonstrate their applicability by learning models based on real neuroscience data. Finally, we compare the performance of the proposed methods with an approach for learning mixtures of truncated basis functions (MoTBFs). The empirical results show that the proposed methods generally yield models that are comparable to or significantly better than those found using the MoTBF-based method.},
keywords = {CASI CAM CM, CIG UPM, Journal},
pubstate = {published},
tppubtype = {article}
}
Mixtures of polynomials (MoPs) are a nonparametric density estimation technique especially designed for hybrid Bayesian networks with continuous and discrete variables. Algorithms to learn one- and multidimensional (marginal) MoPs from data have recently been proposed. In this paper, we introduce two methods for learning MoP approximations of conditional densities from data. Both approaches are based on learning MoP approximations of the joint density and the marginal density of the conditioning variables, but they differ as to how the MoP approximation of the quotient of the two densities is found. We illustrate and study the methods using data sampled from known parametric distributions, and demonstrate their applicability by learning models based on real neuroscience data. Finally, we compare the performance of the proposed methods with an approach for learning mixtures of truncated basis functions (MoTBFs). The empirical results show that the proposed methods generally yield models that are comparable to or significantly better than those found using the MoTBF-based method.
Ruiz, Francisco J R; Perez-Cruz, Fernando
A Generative Model for Predicting Outcomes in College Basketball Artículo de revista
En: Journal of Quantitative Analysis in Sports, vol. 11, no 1 Special Issue, pp. 39–52, 2015, ISSN: 1559-0410.
Resumen | Enlaces | BibTeX | Etiquetas: CASI CAM CM, GAMMA-L+ UC3M, NCAA tournament, Poisson factorization, Probabilistic modeling, variational inference
@article{Ruiz2015bb,
title = {A Generative Model for Predicting Outcomes in College Basketball},
author = {Francisco J R Ruiz and Fernando Perez-Cruz},
url = {http://www.degruyter.com/view/j/jqas.2015.11.issue-1/jqas-2014-0055/jqas-2014-0055.xml},
doi = {10.1515/jqas-2014-0055},
issn = {1559-0410},
year = {2015},
date = {2015-03-01},
journal = {Journal of Quantitative Analysis in Sports},
volume = {11},
number = {1 Special Issue},
pages = {39--52},
abstract = {We show that a classical model for soccer can also provide competitive results in predicting basketball outcomes. We modify the classical model in two ways in order to capture both the specific behavior of each National collegiate athletic association (NCAA) conference and different strategies of teams and conferences. Through simulated bets on six online betting houses, we show that this extension leads to better predictive performance in terms of profit we make. We compare our estimates with the probabilities predicted by the winner of the recent Kaggle competition on the 2014 NCAA tournament, and conclude that our model tends to provide results that differ more from the implicit probabilities of the betting houses and, therefore, has the potential to provide higher benefits.},
keywords = {CASI CAM CM, GAMMA-L+ UC3M, NCAA tournament, Poisson factorization, Probabilistic modeling, variational inference},
pubstate = {published},
tppubtype = {article}
}
We show that a classical model for soccer can also provide competitive results in predicting basketball outcomes. We modify the classical model in two ways in order to capture both the specific behavior of each National collegiate athletic association (NCAA) conference and different strategies of teams and conferences. Through simulated bets on six online betting houses, we show that this extension leads to better predictive performance in terms of profit we make. We compare our estimates with the probabilities predicted by the winner of the recent Kaggle competition on the 2014 NCAA tournament, and conclude that our model tends to provide results that differ more from the implicit probabilities of the betting houses and, therefore, has the potential to provide higher benefits.
Perez-Cruz, Fernando; Huang, Howard
A Blind Nonparametric Non-line of Sight Bias Model for Accurate Localization Proceedings Article
En: Information Theory and Applications (ITA), San Diego, 2015.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Perez-Cruz2015,
title = {A Blind Nonparametric Non-line of Sight Bias Model for Accurate Localization},
author = {Fernando Perez-Cruz and Howard Huang},
url = {http://ita.ucsd.edu/workshop/15/files/abstract/abstract_1462.txt},
year = {2015},
date = {2015-02-01},
booktitle = {Information Theory and Applications (ITA)},
address = {San Diego},
abstract = {One of the most promising solutions for accurate localization services is estimating the Time Difference of Arrival (TDoA) with a cellular infrastructure and triangulating the position of the sought device. There are three different elements that limit the accuracy of TDoA: bandwidth/snr, clock accuracy and non-line-of-sight (NLOS) bias. The Cramer-Rao lower bound is well known and can be made sufficiently low (centimeters) with existing technologies. GPS clock accuracy is below 15ns (less than 5 meters). NLOS is difficult to characterize and depends heavily on the environment. We cannot rely on simple distributions to model it and we should not expect it to follow a few typical scenarios. In this talk, we present a nonparametric model for estimating the NLOS bias and an algorithm that learns the model on the fly without feedback on the true position. This procedure allows getting accurate localization in any environment and without needing to fine-tune a priori de NLOS for each base station. The actual accuracy depends on the number of bases that hear the device, but uncontrolled outliers no longer limit it. For a dense infrastructure, we show that the localization error can be measured in a few meters.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
One of the most promising solutions for accurate localization services is estimating the Time Difference of Arrival (TDoA) with a cellular infrastructure and triangulating the position of the sought device. There are three different elements that limit the accuracy of TDoA: bandwidth/snr, clock accuracy and non-line-of-sight (NLOS) bias. The Cramer-Rao lower bound is well known and can be made sufficiently low (centimeters) with existing technologies. GPS clock accuracy is below 15ns (less than 5 meters). NLOS is difficult to characterize and depends heavily on the environment. We cannot rely on simple distributions to model it and we should not expect it to follow a few typical scenarios. In this talk, we present a nonparametric model for estimating the NLOS bias and an algorithm that learns the model on the fly without feedback on the true position. This procedure allows getting accurate localization in any environment and without needing to fine-tune a priori de NLOS for each base station. The actual accuracy depends on the number of bases that hear the device, but uncontrolled outliers no longer limit it. For a dense infrastructure, we show that the localization error can be measured in a few meters.
Salamanca, Luis; Murillo-Fuentes, Juan José; Olmos, Pablo M; Perez-Cruz, Fernando; Verdu, Sergio
Approaching the DT Bound Using Linear Codes in the Short Blocklength Regime Artículo de revista
En: IEEE Communications Letters, vol. 19, no 2, pp. 123–126, 2015, ISSN: 1089-7798.
Resumen | Enlaces | BibTeX | Etiquetas: binary erasure channel, Channel Coding, Complexity theory, finite blocklength regime, LDPC codes, Maximum likelihood decoding, ML decoding, parity check codes, random coding
@article{Salamanca2014bb,
title = {Approaching the DT Bound Using Linear Codes in the Short Blocklength Regime},
author = {Luis Salamanca and Juan Jos\'{e} Murillo-Fuentes and Pablo M Olmos and Fernando Perez-Cruz and Sergio Verdu},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6957577},
doi = {10.1109/LCOMM.2014.2371032},
issn = {1089-7798},
year = {2015},
date = {2015-02-01},
journal = {IEEE Communications Letters},
volume = {19},
number = {2},
pages = {123--126},
abstract = {The dependence-testing (DT) bound is one of the strongest achievability bounds for the binary erasure channel (BEC) in the finite block length regime. In this paper, we show that maximum likelihood decoded regular low-density paritycheck (LDPC) codes with at least 5 ones per column almost achieve the DT bound. Specifically, using quasi-regular LDPC codes with block length of 256 bits, we achieve a rate that is less than 1% away from the rate predicted by the DT bound for a word error rate below 103. The results also indicate that the maximum-likelihood solution is computationally feasible for decoding block codes over the BEC with several hundred bits.},
keywords = {binary erasure channel, Channel Coding, Complexity theory, finite blocklength regime, LDPC codes, Maximum likelihood decoding, ML decoding, parity check codes, random coding},
pubstate = {published},
tppubtype = {article}
}
The dependence-testing (DT) bound is one of the strongest achievability bounds for the binary erasure channel (BEC) in the finite block length regime. In this paper, we show that maximum likelihood decoded regular low-density paritycheck (LDPC) codes with at least 5 ones per column almost achieve the DT bound. Specifically, using quasi-regular LDPC codes with block length of 256 bits, we achieve a rate that is less than 1% away from the rate predicted by the DT bound for a word error rate below 103. The results also indicate that the maximum-likelihood solution is computationally feasible for decoding block codes over the BEC with several hundred bits.
Manzano, Mario; Espinosa, Felipe; Bravo-Santos, Ángel M; Gardel-Vicente, Alfredo
Cognitive Self-Scheduled Mechanism for Access Control in Noisy Vehicular Ad Hoc Networks Artículo de revista
En: Mathematical Problems in Engineering., vol. 2015, pp. 1–12, 2015.
Resumen | Enlaces | BibTeX | Etiquetas:
@article{Manzano2015,
title = {Cognitive Self-Scheduled Mechanism for Access Control in Noisy Vehicular Ad Hoc Networks},
author = {Mario Manzano and Felipe Espinosa and \'{A}ngel M Bravo-Santos and Alfredo Gardel-Vicente},
url = {http://www.hindawi.com/journals/mpe/2015/354292/ http://dx.doi.org/10.1155/2015/354292},
doi = {10.1155/2015/354292},
year = {2015},
date = {2015-01-01},
journal = {Mathematical Problems in Engineering.},
volume = {2015},
pages = {1--12},
abstract = {Within the challenging environment of intelligent transportation systems (ITS), networked control systems such as platooning guidance of autonomous vehicles require innovative mechanisms to provide real-time communications. Although several proposals are currently under discussion, the design of a rapid, efficient, flexible, and reliable medium access control mechanism which meets the specific constraints of such real-time communications applications remains unsolved in this highly dynamic environment. However, cognitive radio (CR) combines the capacity to sense the radio spectrum with the flexibility to adapt to transmission parameters in order to maximize system performance and has thus become an effective approach for the design of dynamic spectrum access (DSA) mechanisms. This paper presents the enhanced noncooperative cognitive division multiple access (ENCCMA) proposal combining time division multiple access (TDMA) and frequency division multiple access (FDMA) schemes with CR techniques to obtain a mechanism fulfilling the requirements of real-time communications. The analysis presented here considers the IEEE WAVE and 802.11p as reference standards; however, the proposed medium access control (MAC) mechanism can be adapted to operate on the physical layer of different standards. The mechanism also offers the advantage of avoiding signaling, thus enhancing system autonomy as well as behavior in adverse scenarios.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Within the challenging environment of intelligent transportation systems (ITS), networked control systems such as platooning guidance of autonomous vehicles require innovative mechanisms to provide real-time communications. Although several proposals are currently under discussion, the design of a rapid, efficient, flexible, and reliable medium access control mechanism which meets the specific constraints of such real-time communications applications remains unsolved in this highly dynamic environment. However, cognitive radio (CR) combines the capacity to sense the radio spectrum with the flexibility to adapt to transmission parameters in order to maximize system performance and has thus become an effective approach for the design of dynamic spectrum access (DSA) mechanisms. This paper presents the enhanced noncooperative cognitive division multiple access (ENCCMA) proposal combining time division multiple access (TDMA) and frequency division multiple access (FDMA) schemes with CR techniques to obtain a mechanism fulfilling the requirements of real-time communications. The analysis presented here considers the IEEE WAVE and 802.11p as reference standards; however, the proposed medium access control (MAC) mechanism can be adapted to operate on the physical layer of different standards. The mechanism also offers the advantage of avoiding signaling, thus enhancing system autonomy as well as behavior in adverse scenarios.
Varando, Gherardo; Bielza, Concha; Larrañaga, Pedro
Decision boundary for discrete Bayesian network classifiers Artículo de revista
En: Journal of Machine Learning Research, 2015.
Resumen | Enlaces | BibTeX | Etiquetas: Bayesian networks, CASI CAM CM, CIG UPM, decision boundary, Journal, Lagrange basis, polynomial, supervised classi cation, threshold function
@article{Varando2015c,
title = {Decision boundary for discrete Bayesian network classifiers},
author = {Gherardo Varando and Concha Bielza and Pedro Larra\~{n}aga},
url = {http://cig.fi.upm.es/node/881 http://cig.fi.upm.es/articles/2015/Varando-2015-JMLR.pdf},
year = {2015},
date = {2015-01-01},
journal = {Journal of Machine Learning Research},
abstract = {Bayesian network classi ers are a powerful machine learning tool. In order to evaluate the expressive power of these models, we compute families of polynomials that sign-represent decision functions induced by Bayesian network classi ers. We prove that those families are linear combinations of products of Lagrange basis polynomials. In absence of V -structures in the predictor sub-graph, we are also able to prove that this family of polynomials does indeed characterize the speci c classi er considered. We then use this representation to bound the number of decision functions representable by Bayesian network classi ers with a given structure},
keywords = {Bayesian networks, CASI CAM CM, CIG UPM, decision boundary, Journal, Lagrange basis, polynomial, supervised classi cation, threshold function},
pubstate = {published},
tppubtype = {article}
}
Bayesian network classi ers are a powerful machine learning tool. In order to evaluate the expressive power of these models, we compute families of polynomials that sign-represent decision functions induced by Bayesian network classi ers. We prove that those families are linear combinations of products of Lagrange basis polynomials. In absence of V -structures in the predictor sub-graph, we are also able to prove that this family of polynomials does indeed characterize the speci c classi er considered. We then use this representation to bound the number of decision functions representable by Bayesian network classi ers with a given structure
Manzano, Mario; Espinosa, Felipe; Bravo-Santos, Ángel M; Gardel-Vicente, Alfredo
Cognitive Self-Scheduled Mechanism for Access Control in Noisy Vehicular Ad Hoc Networks Artículo de revista
En: Mathematical Problems in Engineering., vol. 2015, pp. 1–12, 2015.
Resumen | Enlaces | BibTeX | Etiquetas: Journal
@article{Manzano2015b,
title = {Cognitive Self-Scheduled Mechanism for Access Control in Noisy Vehicular Ad Hoc Networks},
author = {Mario Manzano and Felipe Espinosa and \'{A}ngel M Bravo-Santos and Alfredo Gardel-Vicente},
url = {http://www.hindawi.com/journals/mpe/2015/354292/ http://dx.doi.org/10.1155/2015/354292},
doi = {10.1155/2015/354292},
year = {2015},
date = {2015-01-01},
journal = {Mathematical Problems in Engineering.},
volume = {2015},
pages = {1--12},
abstract = {Within the challenging environment of intelligent transportation systems (ITS), networked control systems such as platooning guidance of autonomous vehicles require innovative mechanisms to provide real-time communications. Although several proposals are currently under discussion, the design of a rapid, efficient, flexible, and reliable medium access control mechanism which meets the specific constraints of such real-time communications applications remains unsolved in this highly dynamic environment. However, cognitive radio (CR) combines the capacity to sense the radio spectrum with the flexibility to adapt to transmission parameters in order to maximize system performance and has thus become an effective approach for the design of dynamic spectrum access (DSA) mechanisms. This paper presents the enhanced noncooperative cognitive division multiple access (ENCCMA) proposal combining time division multiple access (TDMA) and frequency division multiple access (FDMA) schemes with CR techniques to obtain a mechanism fulfilling the requirements of real-time communications. The analysis presented here considers the IEEE WAVE and 802.11p as reference standards; however, the proposed medium access control (MAC) mechanism can be adapted to operate on the physical layer of different standards. The mechanism also offers the advantage of avoiding signaling, thus enhancing system autonomy as well as behavior in adverse scenarios.},
keywords = {Journal},
pubstate = {published},
tppubtype = {article}
}
Within the challenging environment of intelligent transportation systems (ITS), networked control systems such as platooning guidance of autonomous vehicles require innovative mechanisms to provide real-time communications. Although several proposals are currently under discussion, the design of a rapid, efficient, flexible, and reliable medium access control mechanism which meets the specific constraints of such real-time communications applications remains unsolved in this highly dynamic environment. However, cognitive radio (CR) combines the capacity to sense the radio spectrum with the flexibility to adapt to transmission parameters in order to maximize system performance and has thus become an effective approach for the design of dynamic spectrum access (DSA) mechanisms. This paper presents the enhanced noncooperative cognitive division multiple access (ENCCMA) proposal combining time division multiple access (TDMA) and frequency division multiple access (FDMA) schemes with CR techniques to obtain a mechanism fulfilling the requirements of real-time communications. The analysis presented here considers the IEEE WAVE and 802.11p as reference standards; however, the proposed medium access control (MAC) mechanism can be adapted to operate on the physical layer of different standards. The mechanism also offers the advantage of avoiding signaling, thus enhancing system autonomy as well as behavior in adverse scenarios.
Luengo, David; Monzon, Sandra; Trigano, Tom; Vía, Javier; Artés-Rodríguez, Antonio
Blind Analysis of Atrial Fibrillation Electrograms: A Sparsity-Aware Formulation Artículo de revista
En: Integrated Computer-Aided Engineering, vol. 22, no 1, pp. 71–85, 2015.
Resumen | Enlaces | BibTeX | Etiquetas: atrial fibrillation, biomedical signal processing
@article{Luengo2014bb,
title = {Blind Analysis of Atrial Fibrillation Electrograms: A Sparsity-Aware Formulation},
author = {David Luengo and Sandra Monzon and Tom Trigano and Javier V\'{i}a and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://content.iospress.com/articles/integrated-computer-aided-engineering/ica00471
http://www.tsc.uc3m.es/~dluengo/sparseEGM.pdf},
year = {2015},
date = {2015-01-01},
journal = {Integrated Computer-Aided Engineering},
volume = {22},
number = {1},
pages = {71--85},
abstract = {The problem of blind sparse analysis of electrogram (EGM) signals under atrial fibrillation (AF) conditions is considered in this paper. A mathematical model for the observed signals that takes into account the multiple foci typically appearing inside the heart during AF is firstly introduced. Then, a reconstruction model based on a fixed dictionary is developed and several alternatives for choosing the dictionary are discussed. In order to obtain a sparse solution, which takes into account the biological restrictions of the problem at the same time, the paper proposes using a Least Absolute Shrinkage and Selection Operator (LASSO) regularization followed by a post-processing stage that removes low amplitude coefficients violating the refractory period characteristic of cardiac cells. Finally, spectral analysis is performed on the clean activation sequence obtained from the sparse learning stage in order to estimate the number of latent foci and their frequencies. Simulations on synthetic signals and applications on real data are provided to validate the proposed approach.},
keywords = {atrial fibrillation, biomedical signal processing},
pubstate = {published},
tppubtype = {article}
}
The problem of blind sparse analysis of electrogram (EGM) signals under atrial fibrillation (AF) conditions is considered in this paper. A mathematical model for the observed signals that takes into account the multiple foci typically appearing inside the heart during AF is firstly introduced. Then, a reconstruction model based on a fixed dictionary is developed and several alternatives for choosing the dictionary are discussed. In order to obtain a sparse solution, which takes into account the biological restrictions of the problem at the same time, the paper proposes using a Least Absolute Shrinkage and Selection Operator (LASSO) regularization followed by a post-processing stage that removes low amplitude coefficients violating the refractory period characteristic of cardiac cells. Finally, spectral analysis is performed on the clean activation sequence obtained from the sparse learning stage in order to estimate the number of latent foci and their frequencies. Simulations on synthetic signals and applications on real data are provided to validate the proposed approach.
Martín-Fernández, L; Ruiz, Diego; Torija, Antonio; Miguez, Joaquin
A Bayesian Method for Model Selection in Environmental Noise Prediction Artículo de revista
En: Journal of Environmental Informatics, vol. January 20, 2015, ISSN: 1726-2135.
Resumen | Enlaces | BibTeX | Etiquetas:
@article{Martin-Fernandez2015bb,
title = {A Bayesian Method for Model Selection in Environmental Noise Prediction},
author = {L Mart\'{i}n-Fern\'{a}ndez and Diego Ruiz and Antonio Torija and Joaquin Miguez},
url = {http://www.researchgate.net/publication/268213140_A_Bayesian_method_for_model_selection_in_environmental_noise_prediction},
issn = {1726-2135},
year = {2015},
date = {2015-01-01},
journal = {Journal of Environmental Informatics},
volume = {January 20},
abstract = {Environmental noise prediction and modeling are key factors for addressing a proper planning and management of urban sound environments. In this paper we propose a maximum a posteriori (MAP) method to compare nonlinear state-space models that describe the problem of predicting environmental sound levels. The numerical implementation of this method is based on particle filtering and we use a Markov chain Monte Carlo technique to improve the resampling step. In order to demonstrate the validity of the proposed approach for this particular problem, we have conducted a set of experiments where two prediction models are quantitatively compared using real noise measurement data collected in different urban areas.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Environmental noise prediction and modeling are key factors for addressing a proper planning and management of urban sound environments. In this paper we propose a maximum a posteriori (MAP) method to compare nonlinear state-space models that describe the problem of predicting environmental sound levels. The numerical implementation of this method is based on particle filtering and we use a Markov chain Monte Carlo technique to improve the resampling step. In order to demonstrate the validity of the proposed approach for this particular problem, we have conducted a set of experiments where two prediction models are quantitatively compared using real noise measurement data collected in different urban areas.
Bravo-Santos, Ángel M; Djuric, Petar M
Detectors for Cooperative Mesh Networks with Decode-and-Forward Relays Artículo de revista
En: IEEE Transactions on Signal Processing, vol. 63, no 1, pp. 5–17, 2015, ISSN: 1053-587X.
Resumen | Enlaces | BibTeX | Etiquetas: Cooperative systems, Detectors, Mesh networks, Modulation, Relays, spread spectrum communication, Wireless communication
@article{Bravo-Santos2014b,
title = {Detectors for Cooperative Mesh Networks with Decode-and-Forward Relays},
author = {\'{A}ngel M Bravo-Santos and Petar M Djuric},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6928514},
doi = {10.1109/TSP.2014.2364016},
issn = {1053-587X},
year = {2015},
date = {2015-01-01},
journal = {IEEE Transactions on Signal Processing},
volume = {63},
number = {1},
pages = {5--17},
publisher = {IEEE},
abstract = {We consider mesh networks composed of groups of relaying nodes which operate in decode-andforward mode. Each node from a group relays information to all the nodes in the next group. We study these networks in two setups, one where the nodes have complete state information about the channels through which they receive the signals, and another when they only have the statistics of the channels. We derive recursive expressions for the probabilities of errors of the nodes and present several implementations of detectors used in these networks. We compare the mesh networks with multi-hop networks formed by a set of parallel sections of multiple relaying nodes. We demonstrate with numerous simulations that there are significant improvements in performance of mesh over multi-hop networks in various scenarios.},
keywords = {Cooperative systems, Detectors, Mesh networks, Modulation, Relays, spread spectrum communication, Wireless communication},
pubstate = {published},
tppubtype = {article}
}
We consider mesh networks composed of groups of relaying nodes which operate in decode-andforward mode. Each node from a group relays information to all the nodes in the next group. We study these networks in two setups, one where the nodes have complete state information about the channels through which they receive the signals, and another when they only have the statistics of the channels. We derive recursive expressions for the probabilities of errors of the nodes and present several implementations of detectors used in these networks. We compare the mesh networks with multi-hop networks formed by a set of parallel sections of multiple relaying nodes. We demonstrate with numerous simulations that there are significant improvements in performance of mesh over multi-hop networks in various scenarios.
Bravo-Santos, Ángel M; Djuric, Petar M
Detectors for Cooperative Mesh Networks with Decode-and-Forward Relays Artículo de revista
En: IEEE Transactions on Signal Processing, vol. 63, no 1, pp. 5–17, 2015, ISSN: 1053-587X.
Resumen | Enlaces | BibTeX | Etiquetas: Cooperative systems, Detectors, Journal, Mesh networks, Modulation, Relays, spread spectrum communication, Wireless communication
@article{Bravo-Santos2014bb,
title = {Detectors for Cooperative Mesh Networks with Decode-and-Forward Relays},
author = {\'{A}ngel M Bravo-Santos and Petar M Djuric},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6928514},
doi = {10.1109/TSP.2014.2364016},
issn = {1053-587X},
year = {2015},
date = {2015-01-01},
journal = {IEEE Transactions on Signal Processing},
volume = {63},
number = {1},
pages = {5--17},
publisher = {IEEE},
abstract = {We consider mesh networks composed of groups of relaying nodes which operate in decode-andforward mode. Each node from a group relays information to all the nodes in the next group. We study these networks in two setups, one where the nodes have complete state information about the channels through which they receive the signals, and another when they only have the statistics of the channels. We derive recursive expressions for the probabilities of errors of the nodes and present several implementations of detectors used in these networks. We compare the mesh networks with multi-hop networks formed by a set of parallel sections of multiple relaying nodes. We demonstrate with numerous simulations that there are significant improvements in performance of mesh over multi-hop networks in various scenarios.},
keywords = {Cooperative systems, Detectors, Journal, Mesh networks, Modulation, Relays, spread spectrum communication, Wireless communication},
pubstate = {published},
tppubtype = {article}
}
We consider mesh networks composed of groups of relaying nodes which operate in decode-andforward mode. Each node from a group relays information to all the nodes in the next group. We study these networks in two setups, one where the nodes have complete state information about the channels through which they receive the signals, and another when they only have the statistics of the channels. We derive recursive expressions for the probabilities of errors of the nodes and present several implementations of detectors used in these networks. We compare the mesh networks with multi-hop networks formed by a set of parallel sections of multiple relaying nodes. We demonstrate with numerous simulations that there are significant improvements in performance of mesh over multi-hop networks in various scenarios.
2014
Farajtabar, Mehrdad; Du, Nan; Gomez-rodriguez, Manuel; Valera, Isabel; Zha, Hongyuan; Song, Le
Shaping Social Activity by Incentivizing Users Proceedings Article
En: Advances in Neural Information Processing Systems, pp. 2474–2482, Montreal, 2014.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Farajtabar2014,
title = {Shaping Social Activity by Incentivizing Users},
author = {Mehrdad Farajtabar and Nan Du and Manuel Gomez-rodriguez and Isabel Valera and Hongyuan Zha and Le Song},
url = {http://papers.nips.cc/paper/5365-shaping-social-activity-by-incentivizing-users.pdf},
year = {2014},
date = {2014-12-01},
booktitle = {Advances in Neural Information Processing Systems},
volume = {December},
pages = {2474--2482},
address = {Montreal},
abstract = {Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network. How much external drive should be provided to each user, such that the network activity can be steered towards a target state? In this paper, we model social events using multivariate Hawkes processes, which can capture both endogenous and exogenous event intensities, and derive a time dependent linear relation between the intensity of exogenous events and the overall network activity. Exploiting this connection, we develop a convex optimization framework for determining the required level of external drive in order for the network to reach a desired activity level. We experimented with event data gathered from Twitter, and show that our method can steer the activity of the network more accurately than alternatives.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network. How much external drive should be provided to each user, such that the network activity can be steered towards a target state? In this paper, we model social events using multivariate Hawkes processes, which can capture both endogenous and exogenous event intensities, and derive a time dependent linear relation between the intensity of exogenous events and the overall network activity. Exploiting this connection, we develop a convex optimization framework for determining the required level of external drive in order for the network to reach a desired activity level. We experimented with event data gathered from Twitter, and show that our method can steer the activity of the network more accurately than alternatives.
Bocharova, Irina E; i Fàbregas, Albert Guillén; Kudryashov, Boris D; Martinez, Alfonso; Campo, Adria Tauste; Vazquez-Vilar, Gonzalo
Source-Channel Coding with Multiple Classes Proceedings Article
En: 2014 IEEE International Symposium on Information Theory (ISIT 2014), Honolulu, HI, USA, 2014.
BibTeX | Etiquetas:
@inproceedings{gvazquez-isit2014,
title = {Source-Channel Coding with Multiple Classes},
author = {Irina E Bocharova and Albert Guill\'{e}n i F\`{a}bregas and Boris D Kudryashov and Alfonso Martinez and Adria Tauste Campo and Gonzalo Vazquez-Vilar},
year = {2014},
date = {2014-06-01},
booktitle = {2014 IEEE International Symposium on Information Theory (ISIT 2014)},
address = {Honolulu, HI, USA},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Taborda, Camilo G; Perez-Cruz, Fernando; Guo, Dongning
New Information-Estimation Results for Poisson, Binomial and Negative Binomial Models Proceedings Article
En: 2014 IEEE International Symposium on Information Theory, pp. 2207–2211, IEEE, Honolulu, 2014, ISBN: 978-1-4799-5186-4.
Resumen | Enlaces | BibTeX | Etiquetas: Bregman divergence, Estimation, estimation measures, Gaussian models, Gaussian processes, information measures, information theory, information-estimation results, negative binomial models, Poisson models, Stochastic processes
@inproceedings{Taborda2014,
title = {New Information-Estimation Results for Poisson, Binomial and Negative Binomial Models},
author = {Camilo G Taborda and Fernando Perez-Cruz and Dongning Guo},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6875225},
doi = {10.1109/ISIT.2014.6875225},
isbn = {978-1-4799-5186-4},
year = {2014},
date = {2014-06-01},
booktitle = {2014 IEEE International Symposium on Information Theory},
pages = {2207--2211},
publisher = {IEEE},
address = {Honolulu},
abstract = {In recent years, a number of mathematical relationships have been established between information measures and estimation measures for various models, including Gaussian, Poisson and binomial models. In this paper, it is shown that the second derivative of the input-output mutual information with respect to the input scaling can be expressed as the expectation of a certain Bregman divergence pertaining to the conditional expectations of the input and the input power. This result is similar to that found for the Gaussian model where the Bregman divergence therein is the square distance. In addition, the Poisson, binomial and negative binomial models are shown to be similar in the small scaling regime in the sense that the derivative of the mutual information and the derivative of the relative entropy converge to the same value.},
keywords = {Bregman divergence, Estimation, estimation measures, Gaussian models, Gaussian processes, information measures, information theory, information-estimation results, negative binomial models, Poisson models, Stochastic processes},
pubstate = {published},
tppubtype = {inproceedings}
}
In recent years, a number of mathematical relationships have been established between information measures and estimation measures for various models, including Gaussian, Poisson and binomial models. In this paper, it is shown that the second derivative of the input-output mutual information with respect to the input scaling can be expressed as the expectation of a certain Bregman divergence pertaining to the conditional expectations of the input and the input power. This result is similar to that found for the Gaussian model where the Bregman divergence therein is the square distance. In addition, the Poisson, binomial and negative binomial models are shown to be similar in the small scaling regime in the sense that the derivative of the mutual information and the derivative of the relative entropy converge to the same value.
Miguez, Joaquin
On the uniform asymptotic convergence of a distributed particle filter Proceedings Article
En: 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM), pp. 241–244, IEEE, A Coruña, 2014, ISBN: 978-1-4799-1481-4.
Resumen | Enlaces | BibTeX | Etiquetas: ad hoc networks, Approximation algorithms, approximation errors, Approximation methods, classical convergence theorems, Convergence, convergence of numerical methods, distributed particle filter scheme, distributed signal processing algorithms, Monte Carlo methods, parallel computing systems, particle filtering (numerical methods), Signal processing, Signal processing algorithms, stability assumptions, uniform asymptotic convergence, Wireless Sensor Networks, WSNs
@inproceedings{Miguez2014,
title = {On the uniform asymptotic convergence of a distributed particle filter},
author = {Joaquin Miguez},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6882385},
doi = {10.1109/SAM.2014.6882385},
isbn = {978-1-4799-1481-4},
year = {2014},
date = {2014-06-01},
booktitle = {2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM)},
pages = {241--244},
publisher = {IEEE},
address = {A Coru\~{n}a},
abstract = {Distributed signal processing algorithms suitable for their implementation over wireless sensor networks (WSNs) and ad hoc networks with communications and computing capabilities have become a hot topic during the past years. One class of algorithms that have received special attention are particles filters. However, most distributed versions of this type of methods involve various heuristic or simplifying approximations and, as a consequence, classical convergence theorems for standard particle filters do not hold for their distributed counterparts. In this paper, we look into a distributed particle filter scheme that has been proposed for implementation in both parallel computing systems and WSNs, and prove that, under certain stability assumptions regarding the physical system of interest, its asymptotic convergence is guaranteed. Moreover, we show that convergence is attained uniformly over time. This means that approximation errors can be kept bounded for an arbitrarily long period of time without having to progressively increase the computational effort.},
keywords = {ad hoc networks, Approximation algorithms, approximation errors, Approximation methods, classical convergence theorems, Convergence, convergence of numerical methods, distributed particle filter scheme, distributed signal processing algorithms, Monte Carlo methods, parallel computing systems, particle filtering (numerical methods), Signal processing, Signal processing algorithms, stability assumptions, uniform asymptotic convergence, Wireless Sensor Networks, WSNs},
pubstate = {published},
tppubtype = {inproceedings}
}
Distributed signal processing algorithms suitable for their implementation over wireless sensor networks (WSNs) and ad hoc networks with communications and computing capabilities have become a hot topic during the past years. One class of algorithms that have received special attention are particles filters. However, most distributed versions of this type of methods involve various heuristic or simplifying approximations and, as a consequence, classical convergence theorems for standard particle filters do not hold for their distributed counterparts. In this paper, we look into a distributed particle filter scheme that has been proposed for implementation in both parallel computing systems and WSNs, and prove that, under certain stability assumptions regarding the physical system of interest, its asymptotic convergence is guaranteed. Moreover, we show that convergence is attained uniformly over time. This means that approximation errors can be kept bounded for an arbitrarily long period of time without having to progressively increase the computational effort.
Santiago-Mozos, Ricardo; Perez-Cruz, Fernando; Madden, Michael; Artés-Rodríguez, Antonio
An Automated Screening System for Tuberculosis Artículo de revista
En: IEEE journal of biomedical and health informatics, vol. 18, no 3, pp. 855-862, 2014, ISSN: 2168-2208.
Resumen | Enlaces | BibTeX | Etiquetas: Automated screening, Bayesian, Decision making, Sequential analysis, Tuberculosis
@article{Santiago-Mozos2013,
title = {An Automated Screening System for Tuberculosis},
author = {Ricardo Santiago-Mozos and Fernando Perez-Cruz and Michael Madden and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.tsc.uc3m.es/~antonio/papers/P47_2014_An Automated Screening System for Tuberculosis.pdf http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6630069},
issn = {2168-2208},
year = {2014},
date = {2014-05-01},
journal = {IEEE journal of biomedical and health informatics},
volume = {18},
number = {3},
pages = {855-862},
publisher = {IEEE},
abstract = {Automated screening systems are commonly used to detect some agent in a sample and take a global decision about the subject (e.g. ill/healthy) based on these detections. We propose a Bayesian methodology for taking decisions in (sequential) screening systems that considers the false alarm rate of the detector. Our approach assesses the quality of its decisions and provides lower bounds on the achievable performance of the screening system from the training data. In addition, we develop a complete screening system for sputum smears in tuberculosis diagnosis, and show, using a real-world database, the advantages of the proposed framework when compared to the commonly used count detections and threshold approach.},
keywords = {Automated screening, Bayesian, Decision making, Sequential analysis, Tuberculosis},
pubstate = {published},
tppubtype = {article}
}
Automated screening systems are commonly used to detect some agent in a sample and take a global decision about the subject (e.g. ill/healthy) based on these detections. We propose a Bayesian methodology for taking decisions in (sequential) screening systems that considers the false alarm rate of the detector. Our approach assesses the quality of its decisions and provides lower bounds on the achievable performance of the screening system from the training data. In addition, we develop a complete screening system for sputum smears in tuberculosis diagnosis, and show, using a real-world database, the advantages of the proposed framework when compared to the commonly used count detections and threshold approach.
Crisan, Dan; Miguez, Joaquin
Nested Particle Filters for Sequential Parameter Estimation in Discrete-time State-space Models Proceedings Article
En: SIAM 2014 Conference on Uncertainty Quantification, Savannah, 2014.
@inproceedings{Crisan2014b,
title = {Nested Particle Filters for Sequential Parameter Estimation in Discrete-time State-space Models},
author = {Dan Crisan and Joaquin Miguez},
year = {2014},
date = {2014-03-01},
booktitle = {SIAM 2014 Conference on Uncertainty Quantification},
address = {Savannah},
abstract = {The problem of estimating the parameters of nonlinear, possibly non-Gaussian discrete-time state models has drawn considerable attention during the past few years, leading to the appearance of general methodologies (SMC2, particle MCMC, recursive ML) that have improved on earlier, simpler extensions of the standard particle filter. However, there is still a lack of recursive (online) methods that can provide a theoretically-grounded approximation of the joint posterior probability distribution of the parameters and the dynamic state variables of the model. In the talk, we will describe a two-layer particle filtering scheme that addresses this problem. Both a recursive algorithm, suitable for online implementation, and some results regarding its asymptotic convergence will be presented.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The problem of estimating the parameters of nonlinear, possibly non-Gaussian discrete-time state models has drawn considerable attention during the past few years, leading to the appearance of general methodologies (SMC2, particle MCMC, recursive ML) that have improved on earlier, simpler extensions of the standard particle filter. However, there is still a lack of recursive (online) methods that can provide a theoretically-grounded approximation of the joint posterior probability distribution of the parameters and the dynamic state variables of the model. In the talk, we will describe a two-layer particle filtering scheme that addresses this problem. Both a recursive algorithm, suitable for online implementation, and some results regarding its asymptotic convergence will be presented.
Impedovo, Sebastiano; Liu, Cheng-Lin; Impedovo, Donato; Pirlo, Giuseppe; Read, Jesse; Martino, Luca; Luengo, David
Efficient Monte Carlo Methods for Multi-Dimensional Learning with Classifier Chains Artículo de revista
En: Pattern Recognition, vol. 47, no 3, pp. 1535–1546, 2014.
Resumen | Enlaces | BibTeX | Etiquetas: Bayesian inference, Classifier chains, Monte Carlo methods, Multi-dimensional classification, Multi-label classification
@article{Impedovo2014b,
title = {Efficient Monte Carlo Methods for Multi-Dimensional Learning with Classifier Chains},
author = {Sebastiano Impedovo and Cheng-Lin Liu and Donato Impedovo and Giuseppe Pirlo and Jesse Read and Luca Martino and David Luengo},
url = {http://www.sciencedirect.com/science/article/pii/S0031320313004160},
year = {2014},
date = {2014-01-01},
journal = {Pattern Recognition},
volume = {47},
number = {3},
pages = {1535--1546},
abstract = {Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance \textendash at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest-performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for finding a good chain sequence and performing efficient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.},
keywords = {Bayesian inference, Classifier chains, Monte Carlo methods, Multi-dimensional classification, Multi-label classification},
pubstate = {published},
tppubtype = {article}
}
Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance – at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest-performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for finding a good chain sequence and performing efficient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.
Read, Jesse; Achutegui, Katrin; Miguez, Joaquin
A Distributed Particle Filter for Nonlinear Tracking in Wireless Sensor Networks Artículo de revista
En: Signal Processing, vol. 98, pp. 121–134, 2014.
Resumen | Enlaces | BibTeX | Etiquetas: Distributed filtering, Target tracking, Wireless sensor network
@article{Read2014b,
title = {A Distributed Particle Filter for Nonlinear Tracking in Wireless Sensor Networks},
author = {Jesse Read and Katrin Achutegui and Joaquin Miguez},
url = {http://www.tsc.uc3m.es/~jmiguez/papers/P40_2014_A Distributed Particle Filter for Nonlinear Tracking in Wireless Sensor Networks.pdf
http://www.sciencedirect.com/science/article/pii/S0165168413004568},
year = {2014},
date = {2014-01-01},
journal = {Signal Processing},
volume = {98},
pages = {121--134},
abstract = {The use of distributed particle filters for tracking in sensor networks has become popular in recent years. The distributed particle filters proposed in the literature up to now are only approximations of the centralized particle filter or, if they are a proper distributed version of the particle filter, their implementation in a wireless sensor network demands a prohibitive communication capability. In this work, we propose a mathematically sound distributed particle filter for tracking in a real-world indoor wireless sensor network composed of low-power nodes. We provide formal and general descriptions of our methodology and then present the results of both real-world experiments and/or computer simulations that use models fitted with real data. With the same number of particles as a centralized filter, the distributed algorithm is over four times faster, yet our simulations show that, even assuming the same processing speed, the accuracy of the centralized and distributed algorithms is practically identical. The main limitation of the proposed scheme is the need to make all the sensor observations available to every processing node. Therefore, it is better suited to broadcast networks or multihop networks where the volume of generated data is kept low, e.g., by an adequate local pre-processing of the observations.},
keywords = {Distributed filtering, Target tracking, Wireless sensor network},
pubstate = {published},
tppubtype = {article}
}
The use of distributed particle filters for tracking in sensor networks has become popular in recent years. The distributed particle filters proposed in the literature up to now are only approximations of the centralized particle filter or, if they are a proper distributed version of the particle filter, their implementation in a wireless sensor network demands a prohibitive communication capability. In this work, we propose a mathematically sound distributed particle filter for tracking in a real-world indoor wireless sensor network composed of low-power nodes. We provide formal and general descriptions of our methodology and then present the results of both real-world experiments and/or computer simulations that use models fitted with real data. With the same number of particles as a centralized filter, the distributed algorithm is over four times faster, yet our simulations show that, even assuming the same processing speed, the accuracy of the centralized and distributed algorithms is practically identical. The main limitation of the proposed scheme is the need to make all the sensor observations available to every processing node. Therefore, it is better suited to broadcast networks or multihop networks where the volume of generated data is kept low, e.g., by an adequate local pre-processing of the observations.
Alvarado, Alex; Brannstrom, Fredrik; Agrell, Erik; Koch, Tobias
High-SNR Asymptotics of Mutual Information for Discrete Constellations With Applications to BICM Artículo de revista
En: IEEE Transactions on Information Theory, vol. 60, no 2, pp. 1061–1076, 2014, ISSN: 0018-9448.
Resumen | Enlaces | BibTeX | Etiquetas: additive white Gaussian noise channel, Anti-Gray code, bit-interleaved coded modulation, discrete constellations, Entropy, Gray code, high-SNR asymptotics, IP networks, Labeling, minimum-mean square error, Modulation, Mutual information, Signal to noise ratio, Vectors
@article{Alvarado2014,
title = {High-SNR Asymptotics of Mutual Information for Discrete Constellations With Applications to BICM},
author = {Alex Alvarado and Fredrik Brannstrom and Erik Agrell and Tobias Koch},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6671479
http://www.tsc.uc3m.es/~koch/files/IEEE_TIT_60%282%29.pdf},
issn = {0018-9448},
year = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {60},
number = {2},
pages = {1061--1076},
abstract = {Asymptotic expressions of the mutual information between any discrete input and the corresponding output of the scalar additive white Gaussian noise channel are presented in the limit as the signal-to-noise ratio (SNR) tends to infinity. Asymptotic expressions of the symbol-error probability (SEP) and the minimum mean-square error (MMSE) achieved by estimating the channel input given the channel output are also developed. It is shown that for any input distribution, the conditional entropy of the channel input given the output, MMSE, and SEP have an asymptotic behavior proportional to the Gaussian Q-function. The argument of the Q-function depends only on the minimum Euclidean distance (MED) of the constellation and the SNR, and the proportionality constants are functions of the MED and the probabilities of the pairs of constellation points at MED. The developed expressions are then generalized to study the high-SNR behavior of the generalized mutual information (GMI) for bit-interleaved coded modulation (BICM). By means of these asymptotic expressions, the long-standing conjecture that Gray codes are the binary labelings that maximize the BICM-GMI at high SNR is proven. It is further shown that for any equally spaced constellation whose size is a power of two, there always exists an anti-Gray code giving the lowest BICM-GMI at high SNR.},
keywords = {additive white Gaussian noise channel, Anti-Gray code, bit-interleaved coded modulation, discrete constellations, Entropy, Gray code, high-SNR asymptotics, IP networks, Labeling, minimum-mean square error, Modulation, Mutual information, Signal to noise ratio, Vectors},
pubstate = {published},
tppubtype = {article}
}
Asymptotic expressions of the mutual information between any discrete input and the corresponding output of the scalar additive white Gaussian noise channel are presented in the limit as the signal-to-noise ratio (SNR) tends to infinity. Asymptotic expressions of the symbol-error probability (SEP) and the minimum mean-square error (MMSE) achieved by estimating the channel input given the channel output are also developed. It is shown that for any input distribution, the conditional entropy of the channel input given the output, MMSE, and SEP have an asymptotic behavior proportional to the Gaussian Q-function. The argument of the Q-function depends only on the minimum Euclidean distance (MED) of the constellation and the SNR, and the proportionality constants are functions of the MED and the probabilities of the pairs of constellation points at MED. The developed expressions are then generalized to study the high-SNR behavior of the generalized mutual information (GMI) for bit-interleaved coded modulation (BICM). By means of these asymptotic expressions, the long-standing conjecture that Gray codes are the binary labelings that maximize the BICM-GMI at high SNR is proven. It is further shown that for any equally spaced constellation whose size is a power of two, there always exists an anti-Gray code giving the lowest BICM-GMI at high SNR.
Martin-Fernandez, L; Gilioli, G; Lanzarone, E; Miguez, Joaquin; Pasquali, S; Ruggeri, F; Ruiz, D P
A Rao-Blackwellized Particle Filter for Joint Parameter Estimation and Biomass Tracking in a Stochastic Predator-Prey System Artículo de revista
En: Mathematical Biosciences and Engineering, vol. 11, no 3, pp. 573–597, 2014.
Resumen | Enlaces | BibTeX | Etiquetas:
@article{Martin-Fernandez2014,
title = {A Rao-Blackwellized Particle Filter for Joint Parameter Estimation and Biomass Tracking in a Stochastic Predator-Prey System},
author = {L Martin-Fernandez and G Gilioli and E Lanzarone and Joaquin Miguez and S Pasquali and F Ruggeri and D P Ruiz},
url = {http://www.tsc.uc3m.es/~jmiguez/papers/P42_2014_A Rao-Blackwellized Particle Filter for Joint Parameter Estimation and Biomass Tracking in a Stochastic Predator-Prey System.pdf https://www.aimsciences.org/journals/pdfs.jsp?paperID=9557\&mode=full http://gts.tsc.uc3m.es/wp-content/uploads/2014/01/LMF_et_al_MBE13_A-RAO-BLACKWELLIZED-PARTICLE-FILTER_-jma.pdf https://www.aimsciences.org/journals/displayArticlesnew.jsp?paperID=9557},
year = {2014},
date = {2014-01-01},
journal = {Mathematical Biosciences and Engineering},
volume = {11},
number = {3},
pages = {573--597},
abstract = {Functional response estimation and population tracking in predator- prey systems are critical problems in ecology. In this paper we consider a stochastic predator-prey system with a Lotka-Volterra functional response and propose a particle ltering method for: (a) estimating the behavioral parameter representing the rate of e ective search per predator in the functional response and (b) forecasting the population biomass using eld data. In particular, the proposed technique combines a sequential Monte Carlo sampling scheme for tracking the time-varying biomass with the analytical integration of the unknown behavioral parameter. In order to assess the performance of the method, we show results for both synthetic and observed data collected in an acarine predator-prey system, namely the pest mite Tetranychus urticae and the predatory mite Phytoseiulus persimilis.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Functional response estimation and population tracking in predator- prey systems are critical problems in ecology. In this paper we consider a stochastic predator-prey system with a Lotka-Volterra functional response and propose a particle ltering method for: (a) estimating the behavioral parameter representing the rate of e ective search per predator in the functional response and (b) forecasting the population biomass using eld data. In particular, the proposed technique combines a sequential Monte Carlo sampling scheme for tracking the time-varying biomass with the analytical integration of the unknown behavioral parameter. In order to assess the performance of the method, we show results for both synthetic and observed data collected in an acarine predator-prey system, namely the pest mite Tetranychus urticae and the predatory mite Phytoseiulus persimilis.
Piñeiro-Ave, José; Blanco-Velasco, Manuel; Cruz-Roldán, Fernando; Artés-Rodríguez, Antonio
Target Detection for Low Cost Uncooled MWIR Cameras Based on Empirical Mode Decomposition Artículo de revista
En: Infrared Physics & Technology, vol. 63, pp. 222–231, 2014, ISSN: 13504495.
Resumen | Enlaces | BibTeX | Etiquetas: Background subtraction, Change detection, Denoising, Drift, Empirical Mode Decomposition (EMD), Intrinsic Mode Function (IMF)
@article{Pineiro-Ave2014,
title = {Target Detection for Low Cost Uncooled MWIR Cameras Based on Empirical Mode Decomposition},
author = {Jos\'{e} Pi\~{n}eiro-Ave and Manuel Blanco-Velasco and Fernando Cruz-Rold\'{a}n and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.tsc.uc3m.es/~antonio/papers/P49_2014_Target Detection for Low Cost Uncooled MWIR Cameras Based on Empirical Mode Decomposition.pdf
http://www.sciencedirect.com/science/article/pii/S1350449514000085},
issn = {13504495},
year = {2014},
date = {2014-01-01},
journal = {Infrared Physics \& Technology},
volume = {63},
pages = {222--231},
abstract = {In this work, a novel method for detecting low intensity fast moving objects with low cost Medium Wavelength Infrared (MWIR) cameras is proposed. The method is based on background subtraction in a video sequence obtained with a low density Focal Plane Array (FPA) of the newly available uncooled lead selenide (PbSe) detectors. Thermal instability along with the lack of specific electronics and mechanical devices for canceling the effect of distortion make background image identification very difficult. As a result, the identification of targets is performed in low signal to noise ratio (SNR) conditions, which may considerably restrict the sensitivity of the detection algorithm. These problems are addressed in this work by means of a new technique based on the empirical mode decomposition, which accomplishes drift estimation and target detection. Given that background estimation is the most important stage for detecting, a previous denoising step enabling a better drift estimation is designed. Comparisons are conducted against a denoising technique based on the wavelet transform and also with traditional drift estimation methods such as Kalman filtering and running average. The results reported by the simulations show that the proposed scheme has superior performance.},
keywords = {Background subtraction, Change detection, Denoising, Drift, Empirical Mode Decomposition (EMD), Intrinsic Mode Function (IMF)},
pubstate = {published},
tppubtype = {article}
}
In this work, a novel method for detecting low intensity fast moving objects with low cost Medium Wavelength Infrared (MWIR) cameras is proposed. The method is based on background subtraction in a video sequence obtained with a low density Focal Plane Array (FPA) of the newly available uncooled lead selenide (PbSe) detectors. Thermal instability along with the lack of specific electronics and mechanical devices for canceling the effect of distortion make background image identification very difficult. As a result, the identification of targets is performed in low signal to noise ratio (SNR) conditions, which may considerably restrict the sensitivity of the detection algorithm. These problems are addressed in this work by means of a new technique based on the empirical mode decomposition, which accomplishes drift estimation and target detection. Given that background estimation is the most important stage for detecting, a previous denoising step enabling a better drift estimation is designed. Comparisons are conducted against a denoising technique based on the wavelet transform and also with traditional drift estimation methods such as Kalman filtering and running average. The results reported by the simulations show that the proposed scheme has superior performance.
Koblents, Eugenia; Miguez, Joaquin
A Population Monte Carlo Scheme with Transformed Weights and Its Application to Stochastic Kinetic Models Artículo de revista
En: Statistics and Computing, no (to appear), 2014, ISSN: 0960-3174.
Resumen | Enlaces | BibTeX | Etiquetas: degeneracy of importance weights, Importance sampling, population Monte Carlo, Stochastic kinetic models
@article{Koblents2014bb,
title = {A Population Monte Carlo Scheme with Transformed Weights and Its Application to Stochastic Kinetic Models},
author = {Eugenia Koblents and Joaquin Miguez},
url = {http://link.springer.com/10.1007/s11222-013-9440-2 http://gts.tsc.uc3m.es/wp-content/uploads/2014/01/NPMC_A-population-Monte-Carlo-scheme-with-transformed_jma.pdf},
issn = {0960-3174},
year = {2014},
date = {2014-01-01},
journal = {Statistics and Computing},
number = {(to appear)},
abstract = {This paper addresses the Monte Carlo approximation of posterior probability distributions. In particular, we consider the population Monte Carlo (PMC) technique, which is based on an iterative importance sampling (IS) approach. An important drawback of this methodology is the degeneracy of the importance weights (IWs) when the dimension of either the observations or the variables of interest is high. To alleviate this difficulty, we propose a new method that performs a nonlinear transformation of the IWs. This operation reduces the weight variation, hence it avoids degeneracy and increases the efficiency of the IS scheme, specially when drawing from proposal functions which are poorly adapted to the true posterior. For the sake of illustration, we have applied the proposed algorithm to the estimation of the parameters of a Gaussian mixture model. This is a simple problem that enables us to discuss the main features of the proposed technique. As a practical application, we have also considered the challenging problem of estimating the rate parameters of a stochastic kinetic model (SKM). SKMs are multivariate systems that model molecular interactions in biological and chemical problems. We introduce a particularization of the proposed algorithm to SKMs and present numerical results.},
keywords = {degeneracy of importance weights, Importance sampling, population Monte Carlo, Stochastic kinetic models},
pubstate = {published},
tppubtype = {article}
}
This paper addresses the Monte Carlo approximation of posterior probability distributions. In particular, we consider the population Monte Carlo (PMC) technique, which is based on an iterative importance sampling (IS) approach. An important drawback of this methodology is the degeneracy of the importance weights (IWs) when the dimension of either the observations or the variables of interest is high. To alleviate this difficulty, we propose a new method that performs a nonlinear transformation of the IWs. This operation reduces the weight variation, hence it avoids degeneracy and increases the efficiency of the IS scheme, specially when drawing from proposal functions which are poorly adapted to the true posterior. For the sake of illustration, we have applied the proposed algorithm to the estimation of the parameters of a Gaussian mixture model. This is a simple problem that enables us to discuss the main features of the proposed technique. As a practical application, we have also considered the challenging problem of estimating the rate parameters of a stochastic kinetic model (SKM). SKMs are multivariate systems that model molecular interactions in biological and chemical problems. We introduce a particularization of the proposed algorithm to SKMs and present numerical results.
Crisan, Dan; Miguez, Joaquin
Particle-Kernel Estimation of the Filter Density in State-Space Models Artículo de revista
En: Bernoulli, vol. (to appear, 2014.
Resumen | Enlaces | BibTeX | Etiquetas: density estimation, Markov systems., Models, Sequential Monte Carlo, state-space, stochastic filtering
@article{Crisan2014bb,
title = {Particle-Kernel Estimation of the Filter Density in State-Space Models},
author = {Dan Crisan and Joaquin Miguez},
url = {http://www.tsc.uc3m.es/~jmiguez/papers/P43_2014_Particle-Kernel Estimation of the Filter Density in State-Space Models.pdf
http://www.bernoulli-society.org/index.php/publications/bernoulli-journal/bernoulli-journal-papers},
year = {2014},
date = {2014-01-01},
journal = {Bernoulli},
volume = {(to appear},
abstract = {Sequential Monte Carlo (SMC) methods, also known as particle filters, are simulation-based recursive algorithms for the approximation of the a posteriori probability measures generated by state-space dynamical models. At any given time t, a SMC method produces a set of samples over the state space of the system of interest (often termed “particles”) that is used to build a discrete and random approximation of the posterior probability distribution of the state variables, conditional on a sequence of available observations. One potential application of the methodology is the estimation of the densities associated to the sequence of a posteriori distributions. While practitioners have rather freely applied such density approximations in the past, the issue has received less attention from a theoretical perspective. In this paper, we address the problem of constructing kernel-based estimates of the posterior probability density function and its derivatives, and obtain asymptotic convergence results for the estimation errors. In particular, we find convergence rates for the approximation errors that hold uniformly on the state space and guarantee that the error vanishes almost surely as the number of particles in the filter grows. Based on this uniform convergence result, we first show how to build continuous measures that converge almost surely (with known rate) toward the posterior measure and then address a few applications. The latter include maximum a posteriori estimation of the system state using the approximate derivatives of the posterior density and the approximation of functionals of it, e.g., Shannon’s entropy.},
keywords = {density estimation, Markov systems., Models, Sequential Monte Carlo, state-space, stochastic filtering},
pubstate = {published},
tppubtype = {article}
}
Sequential Monte Carlo (SMC) methods, also known as particle filters, are simulation-based recursive algorithms for the approximation of the a posteriori probability measures generated by state-space dynamical models. At any given time t, a SMC method produces a set of samples over the state space of the system of interest (often termed “particles”) that is used to build a discrete and random approximation of the posterior probability distribution of the state variables, conditional on a sequence of available observations. One potential application of the methodology is the estimation of the densities associated to the sequence of a posteriori distributions. While practitioners have rather freely applied such density approximations in the past, the issue has received less attention from a theoretical perspective. In this paper, we address the problem of constructing kernel-based estimates of the posterior probability density function and its derivatives, and obtain asymptotic convergence results for the estimation errors. In particular, we find convergence rates for the approximation errors that hold uniformly on the state space and guarantee that the error vanishes almost surely as the number of particles in the filter grows. Based on this uniform convergence result, we first show how to build continuous measures that converge almost surely (with known rate) toward the posterior measure and then address a few applications. The latter include maximum a posteriori estimation of the system state using the approximate derivatives of the posterior density and the approximation of functionals of it, e.g., Shannon’s entropy.
Martino, Luca; Elvira, Víctor; Luengo, David
An Adaptive Population Importance Sampler Proceedings Article
En: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), Florencia, 2014.
Enlaces | BibTeX | Etiquetas: ALCIT, COMPREHENSION
@inproceedings{Martino2014,
title = {An Adaptive Population Importance Sampler},
author = {Luca Martino and V\'{i}ctor Elvira and David Luengo},
url = {http://www.icassp2014.org/home.html},
year = {2014},
date = {2014-01-01},
booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)},
address = {Florencia},
keywords = {ALCIT, COMPREHENSION},
pubstate = {published},
tppubtype = {inproceedings}
}
Pastore, Adriano; Koch, Tobias; Fonollosa, Javier Rodriguez
A Rate-Splitting Approach to Fading Multiple-Access Channels with Imperfect Channel-State Information Proceedings Article
En: International Zurich Seminar on Communications (IZS), Zurich, 2014.
Resumen | Enlaces | BibTeX | Etiquetas: ALCIT
@inproceedings{Pastore2014,
title = {A Rate-Splitting Approach to Fading Multiple-Access Channels with Imperfect Channel-State Information},
author = {Adriano Pastore and Tobias Koch and Javier Rodriguez Fonollosa},
url = {http://www.tsc.uc3m.es/~koch/files/IZS_2014_009-012.pdf http://e-collection.library.ethz.ch/eserv/eth:8192/eth-8192-01.pdf},
year = {2014},
date = {2014-01-01},
booktitle = {International Zurich Seminar on Communications (IZS)},
address = {Zurich},
abstract = {As shown by M´edard, the capacity of fading channels with imperfect channel-state information (CSI) can be lowerbounded by assuming a Gaussian channel input and by treating the unknown portion of the channel multiplied by the channel input as independent worst-case (Gaussian) noise. Recently, we have demonstrated that this lower bound can be sharpened by a rate-splitting approach: by expressing the channel input as the sum of two independent Gaussian random variables (referred to as layers), say X = X1+X2, and by applying M´edard’s bounding technique to first lower-bound the capacity of the virtual channel from X1 to the channel output Y (while treating X2 as noise), and then lower-bound the capacity of the virtual channel from X2 to Y (while assuming X1 to be known), one obtains a lower bound that is strictly larger than M´edard’s bound. This ratesplitting approach is reminiscent of an approach used by Rimoldi and Urbanke to achieve points on the capacity region of the Gaussian multiple-access channel (MAC). Here we blend these two rate-splitting approaches to derive a novel inner bound on the capacity region of the memoryless fading MAC with imperfect CSI. Generalizing the above rate-splitting approach to more than two layers, we show that, irrespective of how we assign powers to each layer, the supremum of all rate-splitting bounds is approached as the number of layers tends to infinity, and we derive an integral expression for this supremum. We further derive an expression for the vertices of the best inner bound, maximized over the number of layers and over all power assignments.},
keywords = {ALCIT},
pubstate = {published},
tppubtype = {inproceedings}
}
As shown by M´edard, the capacity of fading channels with imperfect channel-state information (CSI) can be lowerbounded by assuming a Gaussian channel input and by treating the unknown portion of the channel multiplied by the channel input as independent worst-case (Gaussian) noise. Recently, we have demonstrated that this lower bound can be sharpened by a rate-splitting approach: by expressing the channel input as the sum of two independent Gaussian random variables (referred to as layers), say X = X1+X2, and by applying M´edard’s bounding technique to first lower-bound the capacity of the virtual channel from X1 to the channel output Y (while treating X2 as noise), and then lower-bound the capacity of the virtual channel from X2 to Y (while assuming X1 to be known), one obtains a lower bound that is strictly larger than M´edard’s bound. This ratesplitting approach is reminiscent of an approach used by Rimoldi and Urbanke to achieve points on the capacity region of the Gaussian multiple-access channel (MAC). Here we blend these two rate-splitting approaches to derive a novel inner bound on the capacity region of the memoryless fading MAC with imperfect CSI. Generalizing the above rate-splitting approach to more than two layers, we show that, irrespective of how we assign powers to each layer, the supremum of all rate-splitting bounds is approached as the number of layers tends to infinity, and we derive an integral expression for this supremum. We further derive an expression for the vertices of the best inner bound, maximized over the number of layers and over all power assignments.
Ruiz, Francisco J R; Valera, Isabel; Blanco, Carlos; Perez-Cruz, Fernando
Bayesian Nonparametric Comorbidity Analysis of Psychiatric Disorders Artículo de revista
En: Journal of Machine Learning Research, vol. 15, no 1, pp. 1215–1248, 2014.
Resumen | Enlaces | BibTeX | Etiquetas: ALCIT, Bayesian Non-parametrics, categorical observations, Indian Buet Process, Laplace approximation, multinomial-logit function, variational inference
@article{Ruiz2014,
title = {Bayesian Nonparametric Comorbidity Analysis of Psychiatric Disorders},
author = {Francisco J R Ruiz and Isabel Valera and Carlos Blanco and Fernando Perez-Cruz},
url = {http://jmlr.org/papers/volume15/ruiz14a/ruiz14a.pdf
http://arxiv.org/abs/1401.7620},
year = {2014},
date = {2014-01-01},
journal = {Journal of Machine Learning Research},
volume = {15},
number = {1},
pages = {1215--1248},
abstract = {The analysis of comorbidity is an open and complex research field in the branch of psychiatry, where clinical experience and several studies suggest that the relation among the psychiatric disorders may have etiological and treatment implications. In this paper, we are interested in applying latent feature modeling to find the latent structure behind the psychiatric disorders that can help to examine and explain the relationships among them. To this end, we use the large amount of information collected in the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) database and propose to model these data using a nonparametric latent model based on the Indian Buffet Process (IBP). Due to the discrete nature of the data, we first need to adapt the observation model for discrete random variables. We propose a generative model in which the observations are drawn from a multinomial-logit distribution given the IBP matrix. The implementation of an efficient Gibbs sampler is accomplished using the Laplace approximation, which allows integrating out the weighting factors of the multinomial-logit likelihood model. We also provide a variational inference algorithm for this model, which provides a complementary (and less expensive in terms of computational complexity) alternative to the Gibbs sampler allowing us to deal with a larger number of data. Finally, we use the model to analyze comorbidity among the psychiatric disorders diagnosed by experts from the NESARC database.},
keywords = {ALCIT, Bayesian Non-parametrics, categorical observations, Indian Buet Process, Laplace approximation, multinomial-logit function, variational inference},
pubstate = {published},
tppubtype = {article}
}
The analysis of comorbidity is an open and complex research field in the branch of psychiatry, where clinical experience and several studies suggest that the relation among the psychiatric disorders may have etiological and treatment implications. In this paper, we are interested in applying latent feature modeling to find the latent structure behind the psychiatric disorders that can help to examine and explain the relationships among them. To this end, we use the large amount of information collected in the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) database and propose to model these data using a nonparametric latent model based on the Indian Buffet Process (IBP). Due to the discrete nature of the data, we first need to adapt the observation model for discrete random variables. We propose a generative model in which the observations are drawn from a multinomial-logit distribution given the IBP matrix. The implementation of an efficient Gibbs sampler is accomplished using the Laplace approximation, which allows integrating out the weighting factors of the multinomial-logit likelihood model. We also provide a variational inference algorithm for this model, which provides a complementary (and less expensive in terms of computational complexity) alternative to the Gibbs sampler allowing us to deal with a larger number of data. Finally, we use the model to analyze comorbidity among the psychiatric disorders diagnosed by experts from the NESARC database.
O'Mahony, Niamh; Florentino-Liaño, Blanca; Carballo, Juan J; Baca-García, Enrique; Artés-Rodríguez, Antonio
Objective diagnosis of ADHD using IMUs Artículo de revista
En: Medical engineering & physics, vol. 36, no 7, pp. 922–6, 2014, ISSN: 1873-4030.
Resumen | Enlaces | BibTeX | Etiquetas: Attention deficit/hyperactivity disorder, Classification, Inertial sensors, Machine learning, Objective diagnosis
@article{O'Mahony2014,
title = {Objective diagnosis of ADHD using IMUs},
author = {Niamh O'Mahony and Blanca Florentino-Lia\~{n}o and Juan J Carballo and Enrique Baca-Garc\'{i}a and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.tsc.uc3m.es/~antonio/papers/P50_2014_Objective Diagnosis of ADHD Using IMUs.pdf
http://www.sciencedirect.com/science/article/pii/S1350453314000459},
issn = {1873-4030},
year = {2014},
date = {2014-01-01},
journal = {Medical engineering \& physics},
volume = {36},
number = {7},
pages = {922--6},
abstract = {This work proposes the use of miniature wireless inertial sensors as an objective tool for the diagnosis of ADHD. The sensors, consisting of both accelerometers and gyroscopes to measure linear and rotational movement, respectively, are used to characterize the motion of subjects in the setting of a psychiatric consultancy. A support vector machine is used to classify a group of subjects as either ADHD or non-ADHD and a classification accuracy of greater than 95% has been achieved. Separate analyses of the motion data recorded during various activities throughout the visit to the psychiatric consultancy show that motion recorded during a continuous performance test (a forced concentration task) provides a better classification performance than that recorded during "free time".},
keywords = {Attention deficit/hyperactivity disorder, Classification, Inertial sensors, Machine learning, Objective diagnosis},
pubstate = {published},
tppubtype = {article}
}
This work proposes the use of miniature wireless inertial sensors as an objective tool for the diagnosis of ADHD. The sensors, consisting of both accelerometers and gyroscopes to measure linear and rotational movement, respectively, are used to characterize the motion of subjects in the setting of a psychiatric consultancy. A support vector machine is used to classify a group of subjects as either ADHD or non-ADHD and a classification accuracy of greater than 95% has been achieved. Separate analyses of the motion data recorded during various activities throughout the visit to the psychiatric consultancy show that motion recorded during a continuous performance test (a forced concentration task) provides a better classification performance than that recorded during "free time".
Montoya-Martinez, Jair; Artés-Rodríguez, Antonio; Pontil, Massimiliano
Structured Sparse-Low Rank Matrix Factorization for the EEG Inverse Problem Proceedings Article
En: 4th International Workshop on Cognitive Information Processing (CIP 2014), Copenhagen, 2014.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Montoya-Martinez2014,
title = {Structured Sparse-Low Rank Matrix Factorization for the EEG Inverse Problem},
author = {Jair Montoya-Martinez and Antonio Art\'{e}s-Rodr\'{i}guez and Massimiliano Pontil},
url = {http://www.conwiz.dk/cgi-all/cip2014/view_abstract.pl?idno=21},
year = {2014},
date = {2014-01-01},
booktitle = {4th International Workshop on Cognitive Information Processing (CIP 2014)},
address = {Copenhagen},
abstract = {We consider the estimation of the Brain Electrical Sources (BES) matrix from noisy EEG measurements, commonly named as the EEG inverse problem. We propose a new method based on the factorization of the BES as a product of a sparse coding matrix and a dense latent source matrix. This structure is enforced by minimizing a regularized functional that includes the $backslash ell_21$-norm of the coding matrix and the squared Frobenius norm of the latent source matrix. We develop an alternating optimization algorithm to solve the resulting nonsmooth-nonconvex minimization problem. We have evaluated our approach under a simulated scenario consisting on estimating a synthetic BES matrix with 5124 sources. We compare the performance of our method respect to the Lasso, Group Lasso, Sparse Group Lasso and Trace norm regularizers.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider the estimation of the Brain Electrical Sources (BES) matrix from noisy EEG measurements, commonly named as the EEG inverse problem. We propose a new method based on the factorization of the BES as a product of a sparse coding matrix and a dense latent source matrix. This structure is enforced by minimizing a regularized functional that includes the $backslash ell_21$-norm of the coding matrix and the squared Frobenius norm of the latent source matrix. We develop an alternating optimization algorithm to solve the resulting nonsmooth-nonconvex minimization problem. We have evaluated our approach under a simulated scenario consisting on estimating a synthetic BES matrix with 5124 sources. We compare the performance of our method respect to the Lasso, Group Lasso, Sparse Group Lasso and Trace norm regularizers.
Elvira, Víctor; Nazabal, Alfredo; Artés-Rodríguez, Antonio
A Novel Feature Extraction Technique for Human Activity Recognition Proceedings Article
En: 2014 IEEE Workshop on Statistical Signal Processing (SSP 2014), Gold Coast, 2014.
Resumen | Enlaces | BibTeX | Etiquetas: Activity Classification, Ambulatory Monitoring, Features Extraction, Inertial sensors, Magnetic, orientation estimation, Quaternions., sensors
@inproceedings{Elvira2014,
title = {A Novel Feature Extraction Technique for Human Activity Recognition},
author = {V\'{i}ctor Elvira and Alfredo Nazabal and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://edas.info/p15153#S1569490857},
year = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE Workshop on Statistical Signal Processing (SSP 2014)},
address = {Gold Coast},
abstract = {This work presents a novel feature extraction technique for human activity recognition using inertial and magnetic sensors. The proposed method estimates the orientation of the person with respect to the earth frame by using quaternion representation. This estimation is performed automatically without any extra information about where the sensor is placed on the body of the person. Furthermore, the method is also robust to displacements of the sensor with respect to the body. This novel feature extraction technique is used to feed a classification algorithm showing excellent results that outperform those obtained by an existing state-of-the-art feature extraction technique.},
keywords = {Activity Classification, Ambulatory Monitoring, Features Extraction, Inertial sensors, Magnetic, orientation estimation, Quaternions., sensors},
pubstate = {published},
tppubtype = {inproceedings}
}
This work presents a novel feature extraction technique for human activity recognition using inertial and magnetic sensors. The proposed method estimates the orientation of the person with respect to the earth frame by using quaternion representation. This estimation is performed automatically without any extra information about where the sensor is placed on the body of the person. Furthermore, the method is also robust to displacements of the sensor with respect to the body. This novel feature extraction technique is used to feed a classification algorithm showing excellent results that outperform those obtained by an existing state-of-the-art feature extraction technique.
Martino, Luca; Elvira, Víctor; Luengo, David; Artés-Rodríguez, Antonio; Corander, Jukka
Orthogonal MCMC Algorithms Proceedings Article
En: 2014 IEEE Workshop on Statistical Signal Processing (SSP 2014), Gold Coast, 2014.
Resumen | Enlaces | BibTeX | Etiquetas: Bayesian inference, Markov Chain Monte Carlo (MCMC), Parallel Chains, population Monte Carlo
@inproceedings{Martino2014b,
title = {Orthogonal MCMC Algorithms},
author = {Luca Martino and V\'{i}ctor Elvira and David Luengo and Antonio Art\'{e}s-Rodr\'{i}guez and Jukka Corander},
url = {http://edas.info/p15153#S1569490857},
year = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE Workshop on Statistical Signal Processing (SSP 2014)},
address = {Gold Coast},
abstract = {Monte Carlo (MC) methods are widely used in signal processing, machine learning and stochastic optimization. A wellknown class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce a novel parallel interacting MCMC scheme, where the parallel chains share information using another MCMC technique working on the entire population of current states. These parallel “vertical” chains are led by random-walk proposals, whereas the “horizontal” MCMC uses a independent proposal, which can be easily adapted by making use of all the generated samples. Numerical results show the advantages of the proposed sampling scheme in terms of mean absolute error, as well as robustness w.r.t. to initial values and parameter choice.},
keywords = {Bayesian inference, Markov Chain Monte Carlo (MCMC), Parallel Chains, population Monte Carlo},
pubstate = {published},
tppubtype = {inproceedings}
}
Monte Carlo (MC) methods are widely used in signal processing, machine learning and stochastic optimization. A wellknown class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce a novel parallel interacting MCMC scheme, where the parallel chains share information using another MCMC technique working on the entire population of current states. These parallel “vertical” chains are led by random-walk proposals, whereas the “horizontal” MCMC uses a independent proposal, which can be easily adapted by making use of all the generated samples. Numerical results show the advantages of the proposed sampling scheme in terms of mean absolute error, as well as robustness w.r.t. to initial values and parameter choice.
Trigano, Tom; Kolesnikov, V; Luengo, David; Artés-Rodríguez, Antonio
Grouped Sparsity Algorithm for Multichannel Intracardiac ECG Synchronization Proceedings Article
En: 22nd European Signal Processing Conference (EUSIPCO 2014), Lisbon, 2014.
BibTeX | Etiquetas:
@inproceedings{Trigano2014,
title = {Grouped Sparsity Algorithm for Multichannel Intracardiac ECG Synchronization},
author = {Tom Trigano and V Kolesnikov and David Luengo and Antonio Art\'{e}s-Rodr\'{i}guez},
year = {2014},
date = {2014-01-01},
booktitle = {22nd European Signal Processing Conference (EUSIPCO 2014)},
address = {Lisbon},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Montoya-Martinez, Jair; Artés-Rodríguez, Antonio; Pontil, Massimiliano; Hansen, Lars Kai
A Regularized Matrix Factorization Approach to Induce Structured Sparse-Low Rank Solutions in the EEG Inverse Problem Artículo de revista
En: EURASIP Journal on Advances in Signal Processing, vol. 2014, no 1, pp. 97, 2014, ISSN: 1687-6180.
Resumen | Enlaces | BibTeX | Etiquetas: Low rank, Matrix factorization, Nonsmooth-nonconvex optimization, Regularization, Structured sparsity
@article{Montoya-Martinez2014b,
title = {A Regularized Matrix Factorization Approach to Induce Structured Sparse-Low Rank Solutions in the EEG Inverse Problem},
author = {Jair Montoya-Martinez and Antonio Art\'{e}s-Rodr\'{i}guez and Massimiliano Pontil and Lars Kai Hansen},
url = {http://www.tsc.uc3m.es/~antonio/papers/P48_2014_A Regularized Matrix Factorization Approach to Induce Structured Sparse-Low Rank Solutions in the EEG Inverse Problem.pdf
http://asp.eurasipjournals.com/content/2014/1/97/abstract},
issn = {1687-6180},
year = {2014},
date = {2014-01-01},
journal = {EURASIP Journal on Advances in Signal Processing},
volume = {2014},
number = {1},
pages = {97},
publisher = {Springer},
abstract = {We consider the estimation of the Brain Electrical Sources (BES) matrix from noisy Electroencephalographic (EEG) measurements, commonly named as the EEG inverse problem. We propose a new method to induce neurophysiological meaningful solutions, which takes into account the smoothness, structured sparsity and low rank of the BES matrix. The method is based on the factorization of the BES matrix as a product of a sparse coding matrix and a dense latent source matrix. The structured sparse-low rank structure is enforced by minimizing a regularized functional that includes the l21-norm of the coding matrix and the squared Frobenius norm of the latent source matrix. We develop an alternating optimization algorithm to solve the resulting nonsmooth-nonconvex minimization problem. We analyze the convergence of the optimization procedure, and we compare, under different synthetic scenarios, the performance of our method respect to the Group Lasso and Trace Norm regularizers when they are applied directly to the target matrix.},
keywords = {Low rank, Matrix factorization, Nonsmooth-nonconvex optimization, Regularization, Structured sparsity},
pubstate = {published},
tppubtype = {article}
}
We consider the estimation of the Brain Electrical Sources (BES) matrix from noisy Electroencephalographic (EEG) measurements, commonly named as the EEG inverse problem. We propose a new method to induce neurophysiological meaningful solutions, which takes into account the smoothness, structured sparsity and low rank of the BES matrix. The method is based on the factorization of the BES matrix as a product of a sparse coding matrix and a dense latent source matrix. The structured sparse-low rank structure is enforced by minimizing a regularized functional that includes the l21-norm of the coding matrix and the squared Frobenius norm of the latent source matrix. We develop an alternating optimization algorithm to solve the resulting nonsmooth-nonconvex minimization problem. We analyze the convergence of the optimization procedure, and we compare, under different synthetic scenarios, the performance of our method respect to the Group Lasso and Trace Norm regularizers when they are applied directly to the target matrix.
A, Pastore; Koch, Tobias; Fonollosa, Javier Rodriguez
A Rate-Splitting Approach to Fading Channels With Imperfect Channel-State Information Artículo de revista
En: IEEE Transactions on Information Theory, vol. 60, no 7, pp. 4266–4285, 2014, ISSN: 0018-9448.
Resumen | Enlaces | BibTeX | Etiquetas: channel capacity, COMONSENS, DEIPRO, Entropy, Fading, fading channels, flat fading, imperfect channel-state information, MobileNET, Mutual information, OTOSiS, Random variables, Receivers, Signal to noise ratio, Upper bound
@article{Pastore2014a,
title = {A Rate-Splitting Approach to Fading Channels With Imperfect Channel-State Information},
author = {Pastore A and Tobias Koch and Javier Rodriguez Fonollosa},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6832779 http://www.tsc.uc3m.es/~koch/files/IEEE_TIT_60(7).pdf http://arxiv.org/pdf/1301.6120.pdf},
issn = {0018-9448},
year = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {60},
number = {7},
pages = {4266--4285},
publisher = {IEEE},
abstract = {As shown by M\'{e}dard, the capacity of fading channels with imperfect channel-state information can be lower-bounded by assuming a Gaussian channel input (X) with power (P) and by upper-bounding the conditional entropy (h(X|Y,hat {H})) by the entropy of a Gaussian random variable with variance equal to the linear minimum mean-square error in estimating (X) from ((Y,hat {H})) . We demonstrate that, using a rate-splitting approach, this lower bound can be sharpened: by expressing the Gaussian input (X) as the sum of two independent Gaussian variables (X_1) and (X_2) and by applying M\'{e}dard's lower bound first to bound the mutual information between (X_1) and (Y) while treating (X_2) as noise, and by applying it a second time to the mutual information between (X_2) and (Y) while assuming (X_1) to be known, we obtain a capacity lower bound that is strictly larger than M\'{e}dard's lower bound. We then generalize this approach to an arbi- rary number (L) of layers, where (X) is expressed as the sum of (L) independent Gaussian random variables of respective variances (P_ell ) , (ell = 1,dotsc ,L) summing up to (P) . Among all such rate-splitting bounds, we determine the supremum over power allocations (P_ell ) and total number of layers (L) . This supremum is achieved for (L rightarrow infty ) and gives rise to an analytically expressible capacity lower bound. For Gaussian fading, this novel bound is shown to converge to the Gaussian-input mutual information as the signal-to-noise ratio (SNR) grows, provided that the variance of the channel estimation error (H-hat {H}) tends to zero as the SNR tends to infinity.},
keywords = {channel capacity, COMONSENS, DEIPRO, Entropy, Fading, fading channels, flat fading, imperfect channel-state information, MobileNET, Mutual information, OTOSiS, Random variables, Receivers, Signal to noise ratio, Upper bound},
pubstate = {published},
tppubtype = {article}
}
As shown by Médard, the capacity of fading channels with imperfect channel-state information can be lower-bounded by assuming a Gaussian channel input (X) with power (P) and by upper-bounding the conditional entropy (h(X|Y,hat Ħ)) by the entropy of a Gaussian random variable with variance equal to the linear minimum mean-square error in estimating (X) from ((Y,hat Ħ)) . We demonstrate that, using a rate-splitting approach, this lower bound can be sharpened: by expressing the Gaussian input (X) as the sum of two independent Gaussian variables (X_1) and (X_2) and by applying Médard's lower bound first to bound the mutual information between (X_1) and (Y) while treating (X_2) as noise, and by applying it a second time to the mutual information between (X_2) and (Y) while assuming (X_1) to be known, we obtain a capacity lower bound that is strictly larger than Médard's lower bound. We then generalize this approach to an arbi- rary number (L) of layers, where (X) is expressed as the sum of (L) independent Gaussian random variables of respective variances (P_ell ) , (ell = 1,dotsc ,L) summing up to (P) . Among all such rate-splitting bounds, we determine the supremum over power allocations (P_ell ) and total number of layers (L) . This supremum is achieved for (L rightarrow infty ) and gives rise to an analytically expressible capacity lower bound. For Gaussian fading, this novel bound is shown to converge to the Gaussian-input mutual information as the signal-to-noise ratio (SNR) grows, provided that the variance of the channel estimation error (H-hat Ħ) tends to zero as the SNR tends to infinity.
Campo, Adria Tauste; Vazquez-Vilar, Gonzalo; i Fàbregas, Albert Guillén; Koch, Tobias; Martinez, Alfonso
A Derivation of the Source-Channel Error Exponent Using Nonidentical Product Distributions Artículo de revista
En: IEEE Transactions on Information Theory, vol. 60, no 6, pp. 3209–3217, 2014, ISSN: 0018-9448.
Resumen | Enlaces | BibTeX | Etiquetas: ALCIT, Channel Coding, COMONSENS, DEIPRO, error probability, joint source-channel coding, Joints, MobileNET, Probability distribution, product distributions, random coding, Reliability, reliability function, sphere-packing bound, Upper bound
@article{TausteCampo2014,
title = {A Derivation of the Source-Channel Error Exponent Using Nonidentical Product Distributions},
author = {Adria Tauste Campo and Gonzalo Vazquez-Vilar and Albert Guill\'{e}n i F\`{a}bregas and Tobias Koch and Alfonso Martinez},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6803047 http://www.tsc.uc3m.es/~koch/files/IEEE_TIT_60(6).pdf},
issn = {0018-9448},
year = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {60},
number = {6},
pages = {3209--3217},
publisher = {IEEE},
abstract = {This paper studies the random-coding exponent of joint source-channel coding for a scheme where source messages are assigned to disjoint subsets (referred to as classes), and codewords are independently generated according to a distribution that depends on the class index of the source message. For discrete memoryless systems, two optimally chosen classes and product distributions are found to be sufficient to attain the sphere-packing exponent in those cases where it is tight.},
keywords = {ALCIT, Channel Coding, COMONSENS, DEIPRO, error probability, joint source-channel coding, Joints, MobileNET, Probability distribution, product distributions, random coding, Reliability, reliability function, sphere-packing bound, Upper bound},
pubstate = {published},
tppubtype = {article}
}
This paper studies the random-coding exponent of joint source-channel coding for a scheme where source messages are assigned to disjoint subsets (referred to as classes), and codewords are independently generated according to a distribution that depends on the class index of the source message. For discrete memoryless systems, two optimally chosen classes and product distributions are found to be sufficient to attain the sphere-packing exponent in those cases where it is tight.
Cespedes, Javier; Olmos, Pablo M; Sanchez-Fernandez, Matilde; Perez-Cruz, Fernando
Expectation Propagation Detection for High-order High-dimensional MIMO Systems Artículo de revista
En: IEEE Transactions on Communications, vol. PP, no 99, pp. 1–1, 2014, ISSN: 0090-6778.
Resumen | Enlaces | BibTeX | Etiquetas: Approximation methods, computational complexity, Detectors, MIMO, Signal to noise ratio, Vectors
@article{Cespedes2014,
title = {Expectation Propagation Detection for High-order High-dimensional MIMO Systems},
author = {Javier Cespedes and Pablo M Olmos and Matilde Sanchez-Fernandez and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6841617},
issn = {0090-6778},
year = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Communications},
volume = {PP},
number = {99},
pages = {1--1},
abstract = {Modern communications systems use multiple-input multiple-output (MIMO) and high-order QAM constellations for maximizing spectral efficiency. However, as the number of antennas and the order of the constellation grow, the design of efficient and low-complexity MIMO receivers possesses big technical challenges. For example, symbol detection can no longer rely on maximum likelihood detection or sphere-decoding methods, as their complexity increases exponentially with the number of transmitters/receivers. In this paper, we propose a low-complexity high-accuracy MIMO symbol detector based on the Expectation Propagation (EP) algorithm. EP allows approximating iteratively at polynomial-time the posterior distribution of the transmitted symbols. We also show that our EP MIMO detector outperforms classic and state-of-the-art solutions reducing the symbol error rate at a reduced computational complexity.},
keywords = {Approximation methods, computational complexity, Detectors, MIMO, Signal to noise ratio, Vectors},
pubstate = {published},
tppubtype = {article}
}
Modern communications systems use multiple-input multiple-output (MIMO) and high-order QAM constellations for maximizing spectral efficiency. However, as the number of antennas and the order of the constellation grow, the design of efficient and low-complexity MIMO receivers possesses big technical challenges. For example, symbol detection can no longer rely on maximum likelihood detection or sphere-decoding methods, as their complexity increases exponentially with the number of transmitters/receivers. In this paper, we propose a low-complexity high-accuracy MIMO symbol detector based on the Expectation Propagation (EP) algorithm. EP allows approximating iteratively at polynomial-time the posterior distribution of the transmitted symbols. We also show that our EP MIMO detector outperforms classic and state-of-the-art solutions reducing the symbol error rate at a reduced computational complexity.
Read, Jesse; Bielza, Concha; Larranaga, Pedro
Multi-Dimensional Classification with Super-Classes Artículo de revista
En: IEEE Transactions on Knowledge and Data Engineering, vol. 26, no 7, pp. 1720–1733, 2014, ISSN: 1041-4347.
Resumen | Enlaces | BibTeX | Etiquetas: Accuracy, Bayes methods, Classification, COMPRHENSION, conditional dependence, Context, core goals, data instance, evaluation metrics, Integrated circuit modeling, modeling class dependencies, multi-dimensional, Multi-dimensional classification, multidimensional classification problem, multidimensional datasets, multidimensional learners, multilabel classification, multilabel research, multiple class variables, ordinary class, pattern classification, problem transformation, recently-popularized task, super classes, super-class partitions, tractable running time, Training, Vectors
@article{Read2014bb,
title = {Multi-Dimensional Classification with Super-Classes},
author = {Jesse Read and Concha Bielza and Pedro Larranaga},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6648319},
issn = {1041-4347},
year = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Knowledge and Data Engineering},
volume = {26},
number = {7},
pages = {1720--1733},
publisher = {IEEE},
abstract = {The multi-dimensional classification problem is a generalisation of the recently-popularised task of multi-label classification, where each data instance is associated with multiple class variables. There has been relatively little research carried out specific to multi-dimensional classification and, although one of the core goals is similar (modelling dependencies among classes), there are important differences; namely a higher number of possible classifications. In this paper we present method for multi-dimensional classification, drawing from the most relevant multi-label research, and combining it with important novel developments. Using a fast method to model the conditional dependence between class variables, we form super-class partitions and use them to build multi-dimensional learners, learning each super-class as an ordinary class, and thus explicitly modelling class dependencies. Additionally, we present a mechanism to deal with the many class values inherent to super-classes, and thus make learning efficient. To investigate the effectiveness of this approach we carry out an empirical evaluation on a range of multi-dimensional datasets, under different evaluation metrics, and in comparison with high-performing existing multi-dimensional approaches from the literature. Analysis of results shows that our approach offers important performance gains over competing methods, while also exhibiting tractable running time.},
keywords = {Accuracy, Bayes methods, Classification, COMPRHENSION, conditional dependence, Context, core goals, data instance, evaluation metrics, Integrated circuit modeling, modeling class dependencies, multi-dimensional, Multi-dimensional classification, multidimensional classification problem, multidimensional datasets, multidimensional learners, multilabel classification, multilabel research, multiple class variables, ordinary class, pattern classification, problem transformation, recently-popularized task, super classes, super-class partitions, tractable running time, Training, Vectors},
pubstate = {published},
tppubtype = {article}
}
The multi-dimensional classification problem is a generalisation of the recently-popularised task of multi-label classification, where each data instance is associated with multiple class variables. There has been relatively little research carried out specific to multi-dimensional classification and, although one of the core goals is similar (modelling dependencies among classes), there are important differences; namely a higher number of possible classifications. In this paper we present method for multi-dimensional classification, drawing from the most relevant multi-label research, and combining it with important novel developments. Using a fast method to model the conditional dependence between class variables, we form super-class partitions and use them to build multi-dimensional learners, learning each super-class as an ordinary class, and thus explicitly modelling class dependencies. Additionally, we present a mechanism to deal with the many class values inherent to super-classes, and thus make learning efficient. To investigate the effectiveness of this approach we carry out an empirical evaluation on a range of multi-dimensional datasets, under different evaluation metrics, and in comparison with high-performing existing multi-dimensional approaches from the literature. Analysis of results shows that our approach offers important performance gains over competing methods, while also exhibiting tractable running time.
Pradier, Melanie F.; Garcia-Moreno, Pablo; Ruiz, Francisco J R; Valera, Isabel; Molina-Bulla, Harold; Perez-Cruz, Fernando
Map/Reduce Uncollapsed Gibbs Sampling for Bayesian Non Parametric Models Proceedings Article
En: NIPS Workshop on Software Engineering for Machine Learning, Montreal, 2014.
BibTeX | Etiquetas:
@inproceedings{Pradier2014,
title = {Map/Reduce Uncollapsed Gibbs Sampling for Bayesian Non Parametric Models},
author = {Melanie F. Pradier and Pablo Garcia-Moreno and Francisco J R Ruiz and Isabel Valera and Harold Molina-Bulla and Fernando Perez-Cruz},
year = {2014},
date = {2014-01-01},
booktitle = {NIPS Workshop on Software Engineering for Machine Learning},
address = {Montreal},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ruiz, Francisco J R; Lawrence, Neil D; Hensman, James
True Natural Gradient of Collapsed Variational Bayes Proceedings Article
En: NIPS Workshop on Advances in Variational Inference, Montreal, 2014.
BibTeX | Etiquetas:
@inproceedings{Ruiz2014b,
title = {True Natural Gradient of Collapsed Variational Bayes},
author = {Francisco J R Ruiz and Neil D Lawrence and James Hensman},
year = {2014},
date = {2014-01-01},
booktitle = {NIPS Workshop on Advances in Variational Inference},
address = {Montreal},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Valera, Isabel; Ruiz, Francisco J R; Perez-Cruz, Fernando
Infinite Factorial Unbounded Hidden Markov Model for Blind Multiuser Channel Estimation Proceedings Article
En: 2014 4th International Workshop on Cognitive Information Processing (CIP), pp. 1–6, IEEE, Copenhagen, 2014, ISBN: 978-1-4799-3696-0.
Resumen | Enlaces | BibTeX | Etiquetas: Bayes methods, Bayesian non parametrics, Bayesian nonparametric models, blind multiuser channel estimation, Channel estimation, degrees of freedom, detection problems, dispersive channel model, generative model, Hidden Markov models, HMM, inference algorithm, infinite factorial unbounded hidden Markov model, Markov chain Monte Carlo, Markov processes, MIMO, MIMO communication, MIMO communication systems, multiple-input multiple-output (MIMO), multiple-input multiple-output communication syste, receiver performance, Receivers, Signal to noise ratio, Transmitters, unbounded channel length, unbounded number, user detection
@inproceedings{Valera2014a,
title = {Infinite Factorial Unbounded Hidden Markov Model for Blind Multiuser Channel Estimation},
author = {Isabel Valera and Francisco J R Ruiz and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6844506},
isbn = {978-1-4799-3696-0},
year = {2014},
date = {2014-01-01},
booktitle = {2014 4th International Workshop on Cognitive Information Processing (CIP)},
pages = {1--6},
publisher = {IEEE},
address = {Copenhagen},
abstract = {Bayesian nonparametric models allow solving estimation and detection problems with an unbounded number of degrees of freedom. In multiuser multiple-input multiple-output (MIMO) communication systems we might not know the number of active users and the channel they face, and assuming maximal scenarios (maximum number of transmitters and maximum channel length) might degrade the receiver performance. In this paper, we propose a Bayesian nonparametric prior and its associated inference algorithm, which is able to detect an unbounded number of users with an unbounded channel length. This generative model provides the dispersive channel model for each user and a probabilistic estimate for each transmitted symbol in a fully blind manner, i.e., without the need of pilot (training) symbols.},
keywords = {Bayes methods, Bayesian non parametrics, Bayesian nonparametric models, blind multiuser channel estimation, Channel estimation, degrees of freedom, detection problems, dispersive channel model, generative model, Hidden Markov models, HMM, inference algorithm, infinite factorial unbounded hidden Markov model, Markov chain Monte Carlo, Markov processes, MIMO, MIMO communication, MIMO communication systems, multiple-input multiple-output (MIMO), multiple-input multiple-output communication syste, receiver performance, Receivers, Signal to noise ratio, Transmitters, unbounded channel length, unbounded number, user detection},
pubstate = {published},
tppubtype = {inproceedings}
}
Bayesian nonparametric models allow solving estimation and detection problems with an unbounded number of degrees of freedom. In multiuser multiple-input multiple-output (MIMO) communication systems we might not know the number of active users and the channel they face, and assuming maximal scenarios (maximum number of transmitters and maximum channel length) might degrade the receiver performance. In this paper, we propose a Bayesian nonparametric prior and its associated inference algorithm, which is able to detect an unbounded number of users with an unbounded channel length. This generative model provides the dispersive channel model for each user and a probabilistic estimate for each transmitted symbol in a fully blind manner, i.e., without the need of pilot (training) symbols.
Gopalan, Prem; Ruiz, Francisco J R; Ranganath, Rajesh; Blei, David M
Bayesian Nonparametric Poisson Factorization for Recommendation Systems Proceedings Article
En: International Conference on Artificial Intelligence and Statistics (AISTATS), Reykjavik, 2014.
@inproceedings{Gopalan2014,
title = {Bayesian Nonparametric Poisson Factorization for Recommendation Systems},
author = {Prem Gopalan and Francisco J R Ruiz and Rajesh Ranganath and David M Blei},
year = {2014},
date = {2014-01-01},
booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
address = {Reykjavik},
abstract = {We develop a Bayesian nonparametric Poisson factorization model for recommendation systems. Poisson factorization implicitly models each user's limited budget of attention (or money) that allows consumption of only a small subset of the available items. In our Bayesian nonparametric variant, the number of latent components is theoretically unbounded and e ectively estimated when computing a posterior with observed user behavior data. To approximate the posterior, we develop an ecient variational inference algorithm. It adapts the dimensionality of the latent components to the data, only requires iteration over the user/item pairs that have been rated, and has computational complexity on the same order as for a parametric model with xed dimensionality. We studied our model and algorithm with large realworld data sets of user-movie preferences. Our model eases the computational burden of searching for the number of latent components and gives better predictive performance than its parametric counterpart.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We develop a Bayesian nonparametric Poisson factorization model for recommendation systems. Poisson factorization implicitly models each user's limited budget of attention (or money) that allows consumption of only a small subset of the available items. In our Bayesian nonparametric variant, the number of latent components is theoretically unbounded and e ectively estimated when computing a posterior with observed user behavior data. To approximate the posterior, we develop an ecient variational inference algorithm. It adapts the dimensionality of the latent components to the data, only requires iteration over the user/item pairs that have been rated, and has computational complexity on the same order as for a parametric model with xed dimensionality. We studied our model and algorithm with large realworld data sets of user-movie preferences. Our model eases the computational burden of searching for the number of latent components and gives better predictive performance than its parametric counterpart.
Yang, Wei; Durisi, Giuseppe; Koch, Tobias; Polyanskiy, Yury
Dispersion of Quasi-Static MIMO Fading Channels via Stokes' Theorem Proceedings Article
En: 2014 IEEE International Symposium on Information Theory, pp. 2072–2076, IEEE, Honolulu, 2014, ISBN: 978-1-4799-5186-4.
Resumen | Enlaces | BibTeX | Etiquetas: channel capacity, differential form integration, Dispersion, Fading, fading channels, fading distribution, integration, Manifolds, Measurement, MIMO, MIMO communication, quasistatic MIMO fading channels dispersion, quasistatic multiple-input multiple-output fading, radio transmitters, Random variables, Stoke Theorem, transmitter
@inproceedings{Yang2014b,
title = {Dispersion of Quasi-Static MIMO Fading Channels via Stokes' Theorem},
author = {Wei Yang and Giuseppe Durisi and Tobias Koch and Yury Polyanskiy},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6875198},
isbn = {978-1-4799-5186-4},
year = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE International Symposium on Information Theory},
pages = {2072--2076},
publisher = {IEEE},
address = {Honolulu},
abstract = {This paper analyzes the channel dispersion of quasi-static multiple-input multiple-output fading channels with no channel state information at the transmitter. We show that the channel dispersion is zero under mild conditions on the fading distribution. The proof of our result is based on Stokes' theorem, which deals with the integration of differential forms on manifolds with boundary.},
keywords = {channel capacity, differential form integration, Dispersion, Fading, fading channels, fading distribution, integration, Manifolds, Measurement, MIMO, MIMO communication, quasistatic MIMO fading channels dispersion, quasistatic multiple-input multiple-output fading, radio transmitters, Random variables, Stoke Theorem, transmitter},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper analyzes the channel dispersion of quasi-static multiple-input multiple-output fading channels with no channel state information at the transmitter. We show that the channel dispersion is zero under mild conditions on the fading distribution. The proof of our result is based on Stokes' theorem, which deals with the integration of differential forms on manifolds with boundary.
Koch, Tobias
On the Dither-Quantized Gaussian Channel at Low SNR Proceedings Article
En: 2014 IEEE International Symposium on Information Theory, pp. 186–190, IEEE, Honolulu, 2014, ISBN: 978-1-4799-5186-4.
Resumen | Enlaces | BibTeX | Etiquetas: Additive noise, channel capacity, dither quantized Gaussian channel, Entropy, Gaussian channels, low signal-to-noise-ratio, low-SNR asymptotic capacity, peak power constraint, peak-and-average-power-limited Gaussian channel, Quantization (signal), Signal to noise ratio
@inproceedings{Koch2014,
title = {On the Dither-Quantized Gaussian Channel at Low SNR},
author = {Tobias Koch},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6874820},
isbn = {978-1-4799-5186-4},
year = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE International Symposium on Information Theory},
pages = {186--190},
publisher = {IEEE},
address = {Honolulu},
abstract = {We study the capacity of the peak-and-average-power-limited Gaussian channel when its output is quantized using a dithered, infinite-level, uniform quantizer of step size $Delta$. We focus on the low signal-to-noise-ratio (SNR) regime, where communication at low spectral efficiencies takes place. We show that, when the peak-power constraint is absent, the low-SNR asymptotic capacity is equal to that of the unquantized channel irrespective of $Delta$. We further derive an expression for the low-SNR asymptotic capacity for finite peak-to-average-power ratios and evaluate it in the low- and high-resolution limit. We demonstrate that, in this case, the low-SNR asymptotic capacity converges to that of the unquantized channel when $Delta$ tends to zero, and it tends to zero when $Delta$ tends to infinity.},
keywords = {Additive noise, channel capacity, dither quantized Gaussian channel, Entropy, Gaussian channels, low signal-to-noise-ratio, low-SNR asymptotic capacity, peak power constraint, peak-and-average-power-limited Gaussian channel, Quantization (signal), Signal to noise ratio},
pubstate = {published},
tppubtype = {inproceedings}
}
We study the capacity of the peak-and-average-power-limited Gaussian channel when its output is quantized using a dithered, infinite-level, uniform quantizer of step size $Delta$. We focus on the low signal-to-noise-ratio (SNR) regime, where communication at low spectral efficiencies takes place. We show that, when the peak-power constraint is absent, the low-SNR asymptotic capacity is equal to that of the unquantized channel irrespective of $Delta$. We further derive an expression for the low-SNR asymptotic capacity for finite peak-to-average-power ratios and evaluate it in the low- and high-resolution limit. We demonstrate that, in this case, the low-SNR asymptotic capacity converges to that of the unquantized channel when $Delta$ tends to zero, and it tends to zero when $Delta$ tends to infinity.
Ostman, Johan; Yang, Wei; Durisi, Giuseppe; Koch, Tobias
Diversity Versus Multiplexing at Finite Blocklength Proceedings Article
En: 2014 11th International Symposium on Wireless Communications Systems (ISWCS), pp. 702–706, IEEE, Barcelona, 2014, ISBN: 978-1-4799-5863-4.
Resumen | Enlaces | BibTeX | Etiquetas: Antennas, Channel Coding, channel selectivity, Coherence, delay-sensitive ultra-reliable communication links, diversity reception, diversity-exploiting schemes, diversity-multiplexing tradeoff, Fading, finite blocklength analysis, maximum channel coding rate, multiple-antenna block-memoryless Rayleigh-fading, Multiplexing, nonasymptotic bounds, packet size, radio links, Rayleigh channels, Time-frequency analysis, Transmitters, Upper bound
@inproceedings{Ostman2014,
title = {Diversity Versus Multiplexing at Finite Blocklength},
author = {Johan Ostman and Wei Yang and Giuseppe Durisi and Tobias Koch},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6933444},
isbn = {978-1-4799-5863-4},
year = {2014},
date = {2014-01-01},
booktitle = {2014 11th International Symposium on Wireless Communications Systems (ISWCS)},
pages = {702--706},
publisher = {IEEE},
address = {Barcelona},
abstract = {A finite blocklenth analysis of the diversity-multiplexing tradeoff is presented, based on nonasymptotic bounds on the maximum channel coding rate of multiple-antenna block-memoryless Rayleigh-fading channels. The bounds in this paper allow one to numerically assess for which packet size, number of antennas, and degree of channel selectivity, diversity-exploiting schemes are close to optimal, and when instead the available spatial degrees of freedom should be used to provide spatial multiplexing. This finite blocklength view on the diversity-multiplexing tradeoff provides insights on the design of delay-sensitive ultra-reliable communication links.},
keywords = {Antennas, Channel Coding, channel selectivity, Coherence, delay-sensitive ultra-reliable communication links, diversity reception, diversity-exploiting schemes, diversity-multiplexing tradeoff, Fading, finite blocklength analysis, maximum channel coding rate, multiple-antenna block-memoryless Rayleigh-fading, Multiplexing, nonasymptotic bounds, packet size, radio links, Rayleigh channels, Time-frequency analysis, Transmitters, Upper bound},
pubstate = {published},
tppubtype = {inproceedings}
}
A finite blocklenth analysis of the diversity-multiplexing tradeoff is presented, based on nonasymptotic bounds on the maximum channel coding rate of multiple-antenna block-memoryless Rayleigh-fading channels. The bounds in this paper allow one to numerically assess for which packet size, number of antennas, and degree of channel selectivity, diversity-exploiting schemes are close to optimal, and when instead the available spatial degrees of freedom should be used to provide spatial multiplexing. This finite blocklength view on the diversity-multiplexing tradeoff provides insights on the design of delay-sensitive ultra-reliable communication links.
Cespedes, Javier; Olmos, Pablo M; Sanchez-Fernandez, Matilde; Perez-Cruz, Fernando
Improved Performance of LDPC-Coded MIMO Systems with EP-based Soft-Decisions Proceedings Article
En: 2014 IEEE International Symposium on Information Theory, pp. 1997–2001, IEEE, Honolulu, 2014, ISBN: 978-1-4799-5186-4.
Resumen | Enlaces | BibTeX | Etiquetas: Approximation algorithms, Approximation methods, approximation theory, Channel Coding, channel decoder, communication complexity, complexity, Complexity theory, Detectors, encoding scheme, EP soft bit probability, EP-based soft decision, error statistics, expectation propagation, expectation-maximisation algorithm, expectation-propagation algorithm, Gaussian approximation, Gaussian channels, LDPC, LDPC coded MIMO system, Low Complexity receiver, MIMO, MIMO communication, MIMO communication systems, MIMO receiver, modern communication system, multiple input multiple output, parity check codes, per-antenna soft bit probability, posterior marginalization problem, posterior probability computation, QAM constellation, Quadrature amplitude modulation, radio receivers, signaling, spectral analysis, spectral efficiency maximization, symbol detection, telecommunication signalling, Vectors
@inproceedings{Cespedes2014b,
title = {Improved Performance of LDPC-Coded MIMO Systems with EP-based Soft-Decisions},
author = {Javier Cespedes and Pablo M Olmos and Matilde Sanchez-Fernandez and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6875183},
isbn = {978-1-4799-5186-4},
year = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE International Symposium on Information Theory},
pages = {1997--2001},
publisher = {IEEE},
address = {Honolulu},
abstract = {Modern communications systems use efficient encoding schemes, multiple-input multiple-output (MIMO) and high-order QAM constellations for maximizing spectral efficiency. However, as the dimensions of the system grow, the design of efficient and low-complexity MIMO receivers possesses technical challenges. Symbol detection can no longer rely on conventional approaches for posterior probability computation due to complexity. Marginalization of this posterior to obtain per-antenna soft-bit probabilities to be fed to a channel decoder is computationally challenging when realistic signaling is used. In this work, we propose to use Expectation Propagation (EP) algorithm to provide an accurate low-complexity Gaussian approximation to the posterior, easily solving the posterior marginalization problem. EP soft-bit probabilities are used in an LDPC-coded MIMO system, achieving outstanding performance improvement compared to similar approaches in the literature for low-complexity LDPC MIMO decoding.},
keywords = {Approximation algorithms, Approximation methods, approximation theory, Channel Coding, channel decoder, communication complexity, complexity, Complexity theory, Detectors, encoding scheme, EP soft bit probability, EP-based soft decision, error statistics, expectation propagation, expectation-maximisation algorithm, expectation-propagation algorithm, Gaussian approximation, Gaussian channels, LDPC, LDPC coded MIMO system, Low Complexity receiver, MIMO, MIMO communication, MIMO communication systems, MIMO receiver, modern communication system, multiple input multiple output, parity check codes, per-antenna soft bit probability, posterior marginalization problem, posterior probability computation, QAM constellation, Quadrature amplitude modulation, radio receivers, signaling, spectral analysis, spectral efficiency maximization, symbol detection, telecommunication signalling, Vectors},
pubstate = {published},
tppubtype = {inproceedings}
}
Modern communications systems use efficient encoding schemes, multiple-input multiple-output (MIMO) and high-order QAM constellations for maximizing spectral efficiency. However, as the dimensions of the system grow, the design of efficient and low-complexity MIMO receivers possesses technical challenges. Symbol detection can no longer rely on conventional approaches for posterior probability computation due to complexity. Marginalization of this posterior to obtain per-antenna soft-bit probabilities to be fed to a channel decoder is computationally challenging when realistic signaling is used. In this work, we propose to use Expectation Propagation (EP) algorithm to provide an accurate low-complexity Gaussian approximation to the posterior, easily solving the posterior marginalization problem. EP soft-bit probabilities are used in an LDPC-coded MIMO system, achieving outstanding performance improvement compared to similar approaches in the literature for low-complexity LDPC MIMO decoding.
Stinner, Markus; Olmos, Pablo M
Analyzing Finite-length Protograph-Based Spatially Coupled LDPC Codes Proceedings Article
En: 2014 IEEE International Symposium on Information Theory, pp. 891–895, IEEE, Honolulu, 2014, ISBN: 978-1-4799-5186-4.
Resumen | Enlaces | BibTeX | Etiquetas: binary erasure channel, covariance analysis, covariance evolution, Decoding, degree-one check nodes, Error analysis, finite-length protograph, mean evolution, Monte Carlo methods, parity check codes, peeling decoding, protograph-based SC-LDPC codes, spatially coupled low-density parity-check codes, stable decoding phase, Steady-state, Vectors
@inproceedings{Stinner2014,
title = {Analyzing Finite-length Protograph-Based Spatially Coupled LDPC Codes},
author = {Markus Stinner and Pablo M Olmos},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6874961},
isbn = {978-1-4799-5186-4},
year = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE International Symposium on Information Theory},
pages = {891--895},
publisher = {IEEE},
address = {Honolulu},
abstract = {The peeling decoding for spatially coupled low-density parity-check (SC-LDPC) codes is analyzed for a binary erasure channel. An analytical calculation of the mean evolution of degree-one check nodes of protograph-based SC-LDPC codes is given and an estimate for the covariance evolution of degree-one check nodes is proposed in the stable decoding phase where the decoding wave propagates along the chain of coupled codes. Both results are verified numerically. Protograph-based SC-LDPC codes turn out to have a more robust behavior than unstructured random SC-LDPC codes. Using the analytically calculated parameters, the finite-length scaling laws for these constructions are given and verified by numerical simulations.},
keywords = {binary erasure channel, covariance analysis, covariance evolution, Decoding, degree-one check nodes, Error analysis, finite-length protograph, mean evolution, Monte Carlo methods, parity check codes, peeling decoding, protograph-based SC-LDPC codes, spatially coupled low-density parity-check codes, stable decoding phase, Steady-state, Vectors},
pubstate = {published},
tppubtype = {inproceedings}
}
The peeling decoding for spatially coupled low-density parity-check (SC-LDPC) codes is analyzed for a binary erasure channel. An analytical calculation of the mean evolution of degree-one check nodes of protograph-based SC-LDPC codes is given and an estimate for the covariance evolution of degree-one check nodes is proposed in the stable decoding phase where the decoding wave propagates along the chain of coupled codes. Both results are verified numerically. Protograph-based SC-LDPC codes turn out to have a more robust behavior than unstructured random SC-LDPC codes. Using the analytically calculated parameters, the finite-length scaling laws for these constructions are given and verified by numerical simulations.
Olmos, Pablo M; Mitchell, David G M; Truhachev, Dimitri; Costello, Daniel J
Improving the Finite-Length Performance of Long SC-LDPC Code Chains by Connecting Consecutive Chains Proceedings Article
En: 8th IEEE International Symposium on Turbo Codes & Iterative Information Processing, pp. 72–76, IEEE, Bremen, 2014.
Resumen | Enlaces | BibTeX | Etiquetas: Decoding, Error analysis, error probability, Information processing, parity check codes, Turbo codes
@inproceedings{Olmos2014,
title = {Improving the Finite-Length Performance of Long SC-LDPC Code Chains by Connecting Consecutive Chains},
author = {Pablo M Olmos and David G M Mitchell and Dimitri Truhachev and Daniel J Costello},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6955088},
year = {2014},
date = {2014-01-01},
booktitle = {8th IEEE International Symposium on Turbo Codes \& Iterative Information Processing},
pages = {72--76},
publisher = {IEEE},
address = {Bremen},
abstract = {We propose a novel encoding/transmission scheme called continuous chain (CC) transmission that is able to improve the finite-length performance of a system using long spatially coupled low-density parity-check (SC-LDPC) code chains. First, we show that the decoding of SC-LDPC code chains is more reliable for shorter chain lengths, i.e., the scaling between block error rate and gap to threshold is more favorable for shorter chains. This motivates the use of CC transmission in which, instead of transmitting a sequence of independent codewords from a long SC-LDPC chain, we connect multiple chains in a layered format, where encoding, transmission, and decoding are now performed in a continuous fashion. Finally, we show that CC transmission can be implemented with only a small increase in decoding complexity or delay with respect to a system employing a single SC-LDPC code chain for transmission},
keywords = {Decoding, Error analysis, error probability, Information processing, parity check codes, Turbo codes},
pubstate = {published},
tppubtype = {inproceedings}
}
We propose a novel encoding/transmission scheme called continuous chain (CC) transmission that is able to improve the finite-length performance of a system using long spatially coupled low-density parity-check (SC-LDPC) code chains. First, we show that the decoding of SC-LDPC code chains is more reliable for shorter chain lengths, i.e., the scaling between block error rate and gap to threshold is more favorable for shorter chains. This motivates the use of CC transmission in which, instead of transmitting a sequence of independent codewords from a long SC-LDPC chain, we connect multiple chains in a layered format, where encoding, transmission, and decoding are now performed in a continuous fashion. Finally, we show that CC transmission can be implemented with only a small increase in decoding complexity or delay with respect to a system employing a single SC-LDPC code chain for transmission