2011
Olmos, Pablo M; Urbanke, Rudiger
Scaling Behavior of Convolutional LDPC Ensembles over the BEC Proceedings Article
En: 2011 IEEE International Symposium on Information Theory Proceedings, pp. 1816–1820, IEEE, Saint Petersburg, 2011, ISSN: 2157-8095.
Resumen | Enlaces | BibTeX | Etiquetas: BEC, binary codes, binary erasure channel, Bit error rate, convolutional codes, convolutional LDPC ensembles, coupled sparse graph codes, Couplings, Decoding, error probability, Iterative decoding, parity check codes, scaling behavior
@inproceedings{Olmos2011,
title = {Scaling Behavior of Convolutional LDPC Ensembles over the BEC},
author = {Pablo M Olmos and Rudiger Urbanke},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6033863},
issn = {2157-8095},
year = {2011},
date = {2011-01-01},
booktitle = {2011 IEEE International Symposium on Information Theory Proceedings},
pages = {1816--1820},
publisher = {IEEE},
address = {Saint Petersburg},
abstract = {We study the scaling behavior of coupled sparse graph codes over the binary erasure channel. In particular, let 2L+1 be the length of the coupled chain, let M be the number of variables in each of the 2L+1 local copies, let ℓ be the number of iterations, let Pb denote the bit error probability, and let ∈ denote the channel parameter. We are interested in how these quantities scale when we let the blocklength (2L + 1)M tend to infinity. Based on empirical evidence we show that the threshold saturation phenomenon is rather stable with respect to the scaling of the various parameters and we formulate some general rules of thumb which can serve as a guide for the design of coding systems based on coupled graphs.},
keywords = {BEC, binary codes, binary erasure channel, Bit error rate, convolutional codes, convolutional LDPC ensembles, coupled sparse graph codes, Couplings, Decoding, error probability, Iterative decoding, parity check codes, scaling behavior},
pubstate = {published},
tppubtype = {inproceedings}
}
We study the scaling behavior of coupled sparse graph codes over the binary erasure channel. In particular, let 2L+1 be the length of the coupled chain, let M be the number of variables in each of the 2L+1 local copies, let ℓ be the number of iterations, let Pb denote the bit error probability, and let ∈ denote the channel parameter. We are interested in how these quantities scale when we let the blocklength (2L + 1)M tend to infinity. Based on empirical evidence we show that the threshold saturation phenomenon is rather stable with respect to the scaling of the various parameters and we formulate some general rules of thumb which can serve as a guide for the design of coding systems based on coupled graphs.
Taborda, Camilo G; Perez-Cruz, Fernando
Information Theory Concepts and their Relationship with the Bregman Loss Functions Proceedings Article
En: Workshop on Topics in Information Theory and Communications (WTITC’11), Porto, 2011.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Taborda2011,
title = {Information Theory Concepts and their Relationship with the Bregman Loss Functions},
author = {Camilo G Taborda and Fernando Perez-Cruz},
url = {http://www.it.pt/auto_temp_web_page_preview.asp?id=961},
year = {2011},
date = {2011-01-01},
booktitle = {Workshop on Topics in Information Theory and Communications (WTITC’11)},
address = {Porto},
abstract = {Among the past eight years the information theory has become interested in the exploration of links between the information and estimation theory. The best known results show how the mean square error and the mutual information between two random variables (input and output) over a Gaussian channel can be related. Similar results illustrate that, for the Poisson channel, exists different loss functions that can be associated with information theory concepts such as the mutual information and the relative entropy. The talk is oriented in the following way; initially we analyzed different properties that share the mean square error and its counterparts for the Poisson channel. Some results obtained early by the research community can be seen as consequences of the behavior of the analyzed loss functions. In addition, we present a broader version of the results obtained previously for both channels, we also establish the behavior of the mutual information between two random variables when the conditional distribution of the channel comes from the exponential family. One of the main issues explored along the talk is determining in which cases the loss function involved in the behavior of the mutual information corresponds to a Bregman Loss Function, situation that enable us to establish geometrical properties of the found relations},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Among the past eight years the information theory has become interested in the exploration of links between the information and estimation theory. The best known results show how the mean square error and the mutual information between two random variables (input and output) over a Gaussian channel can be related. Similar results illustrate that, for the Poisson channel, exists different loss functions that can be associated with information theory concepts such as the mutual information and the relative entropy. The talk is oriented in the following way; initially we analyzed different properties that share the mean square error and its counterparts for the Poisson channel. Some results obtained early by the research community can be seen as consequences of the behavior of the analyzed loss functions. In addition, we present a broader version of the results obtained previously for both channels, we also establish the behavior of the mutual information between two random variables when the conditional distribution of the channel comes from the exponential family. One of the main issues explored along the talk is determining in which cases the loss function involved in the behavior of the mutual information corresponds to a Bregman Loss Function, situation that enable us to establish geometrical properties of the found relations
Goparaju, S; Calderbank, A R; Carson, W R; Rodrigues, Miguel R D; Perez-Cruz, Fernando
When to Add Another Dimension when Communicating over MIMO Channels Proceedings Article
En: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3100–3103, IEEE, Prague, 2011, ISSN: 1520-6149.
Resumen | Enlaces | BibTeX | Etiquetas: divide and conquer approach, divide and conquer methods, error probability, error rate, error statistics, Gaussian channels, Lattices, Manganese, MIMO, MIMO channel, MIMO communication, multiple input multiple output Gaussian channel, Mutual information, optimal power allocation, power allocation, power constraint, receive filter, Resource management, Signal to noise ratio, signal-to-noise ratio, transmit filter, Upper bound
@inproceedings{Goparaju2011,
title = {When to Add Another Dimension when Communicating over MIMO Channels},
author = {S Goparaju and A R Calderbank and W R Carson and Miguel R D Rodrigues and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5946351},
issn = {1520-6149},
year = {2011},
date = {2011-01-01},
booktitle = {2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {3100--3103},
publisher = {IEEE},
address = {Prague},
abstract = {This paper introduces a divide and conquer approach to the design of transmit and receive filters for communication over a Multiple Input Multiple Output (MIMO) Gaussian channel subject to an average power constraint. It involves conversion to a set of parallel scalar channels, possibly with very different gains, followed by coding per sub-channel (i.e. over time) rather than coding across sub-channels (i.e. over time and space). The loss in performance is negligible at high signal-to-noise ratio (SNR) and not significant at medium SNR. The advantages are reduction in signal processing complexity and greater insight into the SNR thresholds at which a channel is first allocated power. This insight is a consequence of formulating the optimal power allocation in terms of an upper bound on error rate that is determined by parameters of the input lattice such as the minimum distance and kissing number. The resulting thresholds are given explicitly in terms of these lattice parameters. By contrast, when the optimization problem is phrased in terms of maximizing mutual information, the solution is mercury waterfilling, and the thresholds are implicit.},
keywords = {divide and conquer approach, divide and conquer methods, error probability, error rate, error statistics, Gaussian channels, Lattices, Manganese, MIMO, MIMO channel, MIMO communication, multiple input multiple output Gaussian channel, Mutual information, optimal power allocation, power allocation, power constraint, receive filter, Resource management, Signal to noise ratio, signal-to-noise ratio, transmit filter, Upper bound},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper introduces a divide and conquer approach to the design of transmit and receive filters for communication over a Multiple Input Multiple Output (MIMO) Gaussian channel subject to an average power constraint. It involves conversion to a set of parallel scalar channels, possibly with very different gains, followed by coding per sub-channel (i.e. over time) rather than coding across sub-channels (i.e. over time and space). The loss in performance is negligible at high signal-to-noise ratio (SNR) and not significant at medium SNR. The advantages are reduction in signal processing complexity and greater insight into the SNR thresholds at which a channel is first allocated power. This insight is a consequence of formulating the optimal power allocation in terms of an upper bound on error rate that is determined by parameters of the input lattice such as the minimum distance and kissing number. The resulting thresholds are given explicitly in terms of these lattice parameters. By contrast, when the optimization problem is phrased in terms of maximizing mutual information, the solution is mercury waterfilling, and the thresholds are implicit.
Leiva-Murillo, Jose M; Artés-Rodríguez, Antonio; Baca-García, Enrique
Visualization and Prediction of Disease Interactions with Continuous-Time Hidden Markov Models Proceedings Article
En: NIPS 2011 Workshop on Personalized Medicine., Sierra Nevada, 2011.
Resumen | Enlaces | BibTeX | Etiquetas: Computational, Information-Theoretic Learning with Statistics, Theory & Algorithms
@inproceedings{Leiva-Murillo2011,
title = {Visualization and Prediction of Disease Interactions with Continuous-Time Hidden Markov Models},
author = {Jose M Leiva-Murillo and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a},
url = {http://eprints.pascal-network.org/archive/00009110/},
year = {2011},
date = {2011-01-01},
booktitle = {NIPS 2011 Workshop on Personalized Medicine.},
address = {Sierra Nevada},
abstract = {This paper describes a method for discovering disease relationships and the evolution of diseases from medical records. The method makes use of continuous-time Markov chain models that overcome some drawbacks of the more widely used discrete-time chain models. The model addresses uncertainty in the diagnoses, possible diagnosis errors and the existence of multiple alternative diagnoses in the records. A set of experiments, performed on a dataset of psychiatric medical records, shows the capability of the model to visualize maps of comorbidity and causal interactions among diseases as well as to perform predictions of future evolution of diseases.},
keywords = {Computational, Information-Theoretic Learning with Statistics, Theory \& Algorithms},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper describes a method for discovering disease relationships and the evolution of diseases from medical records. The method makes use of continuous-time Markov chain models that overcome some drawbacks of the more widely used discrete-time chain models. The model addresses uncertainty in the diagnoses, possible diagnosis errors and the existence of multiple alternative diagnoses in the records. A set of experiments, performed on a dataset of psychiatric medical records, shows the capability of the model to visualize maps of comorbidity and causal interactions among diseases as well as to perform predictions of future evolution of diseases.
Olmos, Pablo M; Salamanca, Luis; Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando
An Application of Tree-Structured Expectation Propagation for Channel Decoding Proceedings Article
En: Neural Information Processing Systems Foundation (NIPS), Granada, 2011.
@inproceedings{Olmos2011a,
title = {An Application of Tree-Structured Expectation Propagation for Channel Decoding},
author = {Pablo M Olmos and Luis Salamanca and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
year = {2011},
date = {2011-01-01},
booktitle = {Neural Information Processing Systems Foundation (NIPS)},
address = {Granada},
abstract = {We show an application of a tree structure for approximate inference in graphical models using the expectation propagation algorithm. These approximations are typically used over graphs with short-range cycles. We demonstrate that these approximations also help in sparse graphs with long-range loops, as the ones used in coding theory to approach channel capacity. For asymptotically large sparse graph, the expectation propagation algorithm together with the tree structure yields a completely disconnected approximation to the graphical model but, for for finite-length practical sparse graphs, the tree structure approximation to the code graph provides accurate estimates for the marginal of each variable. Furthermore, we propose a new method for constructing the tree structure on the fly that might be more amenable for sparse graphs with general factors.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We show an application of a tree structure for approximate inference in graphical models using the expectation propagation algorithm. These approximations are typically used over graphs with short-range cycles. We demonstrate that these approximations also help in sparse graphs with long-range loops, as the ones used in coding theory to approach channel capacity. For asymptotically large sparse graph, the expectation propagation algorithm together with the tree structure yields a completely disconnected approximation to the graphical model but, for for finite-length practical sparse graphs, the tree structure approximation to the code graph provides accurate estimates for the marginal of each variable. Furthermore, we propose a new method for constructing the tree structure on the fly that might be more amenable for sparse graphs with general factors.
Olmos, Pablo M; Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando
Capacity Achieving LDPC Ensembles for the TEP Decoder in Erasure Channels Proceedings Article
En: 2011 IEEE International Symposium on Information Theory Proceedings, pp. 2398–2402, IEEE, St. Petersburg, 2011, ISSN: 2157-8095.
Resumen | Enlaces | BibTeX | Etiquetas: BP threshold, Complexity theory, Decoding, Differential equations, erasure channels, fixed-rate code, Iterative decoding, LDPC, low-density parity-check codes, MAP capacity, MAP threshold, optimisation, Optimization, optimization problem, parity check codes, TEP decoder, tree-expectation propagation decoder
@inproceedings{Olmos2011b,
title = {Capacity Achieving LDPC Ensembles for the TEP Decoder in Erasure Channels},
author = {Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6033993},
issn = {2157-8095},
year = {2011},
date = {2011-01-01},
booktitle = {2011 IEEE International Symposium on Information Theory Proceedings},
pages = {2398--2402},
publisher = {IEEE},
address = {St. Petersburg},
abstract = {In this work we address the design of degree distributions (DD) of low-density parity-check (LDPC) codes for the tree-expectation propagation (TEP) decoder. The optimization problem to find distributions to maximize the TEP decoding threshold for a fixed-rate code can not be analytically solved. We derive a simplified optimization problem that can be easily solved since it is based in the analytic expressions of the peeling decoder. Two kinds of solutions are obtained from this problem: we either design LDPC ensembles for which the BP threshold equals the MAP threshold or we get LDPC ensembles for which the TEP threshold outperforms the BP threshold, even achieving the MAP capacity in some cases. Hence, we proved that there exist ensembles for which the MAP solution can be obtained with linear complexity even though the BP threshold does not achieve the MAP threshold.},
keywords = {BP threshold, Complexity theory, Decoding, Differential equations, erasure channels, fixed-rate code, Iterative decoding, LDPC, low-density parity-check codes, MAP capacity, MAP threshold, optimisation, Optimization, optimization problem, parity check codes, TEP decoder, tree-expectation propagation decoder},
pubstate = {published},
tppubtype = {inproceedings}
}
In this work we address the design of degree distributions (DD) of low-density parity-check (LDPC) codes for the tree-expectation propagation (TEP) decoder. The optimization problem to find distributions to maximize the TEP decoding threshold for a fixed-rate code can not be analytically solved. We derive a simplified optimization problem that can be easily solved since it is based in the analytic expressions of the peeling decoder. Two kinds of solutions are obtained from this problem: we either design LDPC ensembles for which the BP threshold equals the MAP threshold or we get LDPC ensembles for which the TEP threshold outperforms the BP threshold, even achieving the MAP capacity in some cases. Hence, we proved that there exist ensembles for which the MAP solution can be obtained with linear complexity even though the BP threshold does not achieve the MAP threshold.
Asyhari, Taufiq A; Koch, Tobias; i Fàbregas, Albert Guillén
Nearest Neighbour Decoding and Pilot-Aided Channel Estimation in Stationary Gaussian Flat-Fading Channels Proceedings Article
En: 2011 IEEE International Symposium on Information Theory Proceedings, pp. 2786–2790, IEEE, St. Petersburg, 2011, ISSN: 2157-8095.
Resumen | Enlaces | BibTeX | Etiquetas: Channel estimation, Decoding, Fading, fading channels, Gaussian channels, MIMO, MIMO communication, MISO, multiple-input multiple-output, nearest neighbour decoding, noncoherent multiple-input single-output, pilot-aided channel estimation, Receiving antennas, Signal to noise ratio, signal-to-noise ratio, SNR, stationary Gaussian flat-fading channels, Wireless communication
@inproceedings{Asyhari2011,
title = {Nearest Neighbour Decoding and Pilot-Aided Channel Estimation in Stationary Gaussian Flat-Fading Channels},
author = {Taufiq A Asyhari and Tobias Koch and Albert Guill\'{e}n i F\`{a}bregas},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6034081},
issn = {2157-8095},
year = {2011},
date = {2011-01-01},
booktitle = {2011 IEEE International Symposium on Information Theory Proceedings},
pages = {2786--2790},
publisher = {IEEE},
address = {St. Petersburg},
abstract = {We study the information rates of non-coherent, stationary, Gaussian, multiple-input multiple-output (MIMO) flat-fading channels that are achievable with nearest neighbour decoding and pilot-aided channel estimation. In particular, we analyse the behaviour of these achievable rates in the limit as the signal-to-noise ratio (SNR) tends to infinity. We demonstrate that nearest neighbour decoding and pilot-aided channel estimation achieves the capacity pre-log-which is defined as the limiting ratio of the capacity to the logarithm of SNR as the SNR tends to infinity-of non-coherent multiple-input single-output (MISO) flat-fading channels, and it achieves the best so far known lower bound on the capacity pre-log of non-coherent MIMO flat-fading channels.},
keywords = {Channel estimation, Decoding, Fading, fading channels, Gaussian channels, MIMO, MIMO communication, MISO, multiple-input multiple-output, nearest neighbour decoding, noncoherent multiple-input single-output, pilot-aided channel estimation, Receiving antennas, Signal to noise ratio, signal-to-noise ratio, SNR, stationary Gaussian flat-fading channels, Wireless communication},
pubstate = {published},
tppubtype = {inproceedings}
}
We study the information rates of non-coherent, stationary, Gaussian, multiple-input multiple-output (MIMO) flat-fading channels that are achievable with nearest neighbour decoding and pilot-aided channel estimation. In particular, we analyse the behaviour of these achievable rates in the limit as the signal-to-noise ratio (SNR) tends to infinity. We demonstrate that nearest neighbour decoding and pilot-aided channel estimation achieves the capacity pre-log-which is defined as the limiting ratio of the capacity to the logarithm of SNR as the SNR tends to infinity-of non-coherent multiple-input single-output (MISO) flat-fading channels, and it achieves the best so far known lower bound on the capacity pre-log of non-coherent MIMO flat-fading channels.
Maiz, Cristina S; Miguez, Joaquin
On the Optimization of Transportation Routes with Multiple Destinations in Random Networks Proceedings Article
En: 2011 IEEE Statistical Signal Processing Workshop (SSP), pp. 349–352, IEEE, Nice, 2011, ISBN: 978-1-4577-0569-4.
Resumen | Enlaces | BibTeX | Etiquetas: Approximation algorithms, communication networks, Estimation, graph theory, Histograms, intelligent transportation, Monte Carlo algorithm, Monte Carlo methods, multiple destinations, optimisation, Optimization, random networks, route optimization, routing, Sequential Monte Carlo, Signal processing algorithms, stochastic graph, Stochastic processes, telecommunication network routing, time-varying graph, transportation routes
@inproceedings{Maiz2011,
title = {On the Optimization of Transportation Routes with Multiple Destinations in Random Networks},
author = {Cristina S Maiz and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5967701},
isbn = {978-1-4577-0569-4},
year = {2011},
date = {2011-01-01},
booktitle = {2011 IEEE Statistical Signal Processing Workshop (SSP)},
pages = {349--352},
publisher = {IEEE},
address = {Nice},
abstract = {Various practical problems in transportation research and routing in communication networks can be reduced to the computation of the best path that traverses a certain graph and visits a set of D specified destination nodes. Simple versions of this problem have received attention in the literature. Optimal solutions exist for the cases in which (a) D \>; 1 and the graph is deterministic or (b) D = 1 and the graph is stochastic (and possibly time-dependent). Here, we address the general problem in which both D \>; 1 and the costs of the edges in the graph are stochastic and time-varying. We tackle this complex global optimization problem by first converting it into an equivalent estimation problem and then computing a numerical solution using a sequential Monte Carlo algorithm. The advantage of the proposed technique over some standard methods (devised for graphs with time-invariant statistics) is illustrated by way of computer simulations.},
keywords = {Approximation algorithms, communication networks, Estimation, graph theory, Histograms, intelligent transportation, Monte Carlo algorithm, Monte Carlo methods, multiple destinations, optimisation, Optimization, random networks, route optimization, routing, Sequential Monte Carlo, Signal processing algorithms, stochastic graph, Stochastic processes, telecommunication network routing, time-varying graph, transportation routes},
pubstate = {published},
tppubtype = {inproceedings}
}
Various practical problems in transportation research and routing in communication networks can be reduced to the computation of the best path that traverses a certain graph and visits a set of D specified destination nodes. Simple versions of this problem have received attention in the literature. Optimal solutions exist for the cases in which (a) D >; 1 and the graph is deterministic or (b) D = 1 and the graph is stochastic (and possibly time-dependent). Here, we address the general problem in which both D >; 1 and the costs of the edges in the graph are stochastic and time-varying. We tackle this complex global optimization problem by first converting it into an equivalent estimation problem and then computing a numerical solution using a sequential Monte Carlo algorithm. The advantage of the proposed technique over some standard methods (devised for graphs with time-invariant statistics) is illustrated by way of computer simulations.
Salamanca, Luis; Olmos, Pablo M; Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando
MAP Decoding for LDPC Codes over the Binary Erasure Channel Proceedings Article
En: 2011 IEEE Information Theory Workshop, pp. 145–149, IEEE, Paraty, 2011, ISBN: 978-1-4577-0437-6.
Resumen | Enlaces | BibTeX | Etiquetas: binary erasure channel, Channel Coding, computational complexity, Decoding, generalized peeling decoder, generalized tree-structured expectation propagatio, graphical models, Iterative decoding, LDPC codes, MAP decoding, MAP decoding algorithm, Maximum likelihood decoding, parity check codes, TEP decoder, tree graph theory, Tree graphs, tree-structured expectation propagation, trees (mathematics)
@inproceedings{Salamanca2011a,
title = {MAP Decoding for LDPC Codes over the Binary Erasure Channel},
author = {Luis Salamanca and Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6089364},
isbn = {978-1-4577-0437-6},
year = {2011},
date = {2011-01-01},
booktitle = {2011 IEEE Information Theory Workshop},
pages = {145--149},
publisher = {IEEE},
address = {Paraty},
abstract = {In this paper, we propose a decoding algorithm for LDPC codes that achieves the MAP solution over the BEC. This algorithm, denoted as generalized tree-structured expectation propagation (GTEP), extends the idea of our previous work, the TEP decoder. The GTEP modifies the graph by eliminating a check node of any degree and merging this information with the remaining graph. The GTEP decoder upon completion either provides the unique MAP solution or a tree graph in which the number of parent nodes indicates the multiplicity of the MAP solution. This algorithm can be easily described for the BEC, and it can be cast as a generalized peeling decoder. The GTEP naturally optimizes the complexity of the decoder, by looking for checks nodes of minimum degree to be eliminated first.},
keywords = {binary erasure channel, Channel Coding, computational complexity, Decoding, generalized peeling decoder, generalized tree-structured expectation propagatio, graphical models, Iterative decoding, LDPC codes, MAP decoding, MAP decoding algorithm, Maximum likelihood decoding, parity check codes, TEP decoder, tree graph theory, Tree graphs, tree-structured expectation propagation, trees (mathematics)},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose a decoding algorithm for LDPC codes that achieves the MAP solution over the BEC. This algorithm, denoted as generalized tree-structured expectation propagation (GTEP), extends the idea of our previous work, the TEP decoder. The GTEP modifies the graph by eliminating a check node of any degree and merging this information with the remaining graph. The GTEP decoder upon completion either provides the unique MAP solution or a tree graph in which the number of parent nodes indicates the multiplicity of the MAP solution. This algorithm can be easily described for the BEC, and it can be cast as a generalized peeling decoder. The GTEP naturally optimizes the complexity of the decoder, by looking for checks nodes of minimum degree to be eliminated first.
Achutegui, Katrin; Miguez, Joaquin
A Parallel Resampling Scheme and its Application to Distributed Particle Filtering in Wireless Networks Proceedings Article
En: 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 81–84, IEEE, San Juan, 2011, ISBN: 978-1-4577-2105-2.
Resumen | Enlaces | BibTeX | Etiquetas: Approximation algorithms, Approximation methods, Artificial neural networks, distributed resampling, DRNA technique, Markov processes, nonproportional allocation algorithm, parallel resampling scheme, PF, quantization, Signal processing, Vectors, Wireless sensor network, Wireless Sensor Networks, WSN
@inproceedings{Achutegui2011,
title = {A Parallel Resampling Scheme and its Application to Distributed Particle Filtering in Wireless Networks},
author = {Katrin Achutegui and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6136051},
isbn = {978-1-4577-2105-2},
year = {2011},
date = {2011-01-01},
booktitle = {2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)},
pages = {81--84},
publisher = {IEEE},
address = {San Juan},
abstract = {We address the design of a particle filter (PF) that can be implemented in a distributed manner over a network of wireless sensor nodes, each of them collecting their own local data. This is a problem that has received considerable attention lately and several methods based on consensus, the transmission of likelihood information, the truncation and/or the quantization of data have been proposed. However, all existing schemes suffer from limitations related either to the amount of required communications among the nodes or the accuracy of the filter outputs. In this work we propose a novel distributed PF that is built around the distributed resampling with non-proportional allocation (DRNA) algorithm. This scheme guarantees the properness of the particle approximations produced by the filter and has been shown to be both efficient and accurate when compared with centralized PFs. The standard DRNA technique, however, places stringent demands on the communications among nodes that turn out impractical for a typical wireless sensor network (WSN). In this paper we investigate how to reduce this communication load by using (i) a random model for the spread of data over the WSN and (ii) methods that enable the out-of-sequence processing of sensor observations. A simple numerical illustration of the performance of the new algorithm compared with a centralized PF is provided.},
keywords = {Approximation algorithms, Approximation methods, Artificial neural networks, distributed resampling, DRNA technique, Markov processes, nonproportional allocation algorithm, parallel resampling scheme, PF, quantization, Signal processing, Vectors, Wireless sensor network, Wireless Sensor Networks, WSN},
pubstate = {published},
tppubtype = {inproceedings}
}
We address the design of a particle filter (PF) that can be implemented in a distributed manner over a network of wireless sensor nodes, each of them collecting their own local data. This is a problem that has received considerable attention lately and several methods based on consensus, the transmission of likelihood information, the truncation and/or the quantization of data have been proposed. However, all existing schemes suffer from limitations related either to the amount of required communications among the nodes or the accuracy of the filter outputs. In this work we propose a novel distributed PF that is built around the distributed resampling with non-proportional allocation (DRNA) algorithm. This scheme guarantees the properness of the particle approximations produced by the filter and has been shown to be both efficient and accurate when compared with centralized PFs. The standard DRNA technique, however, places stringent demands on the communications among nodes that turn out impractical for a typical wireless sensor network (WSN). In this paper we investigate how to reduce this communication load by using (i) a random model for the spread of data over the WSN and (ii) methods that enable the out-of-sequence processing of sensor observations. A simple numerical illustration of the performance of the new algorithm compared with a centralized PF is provided.
Asyhari, Taufiq A; Koch, Tobias; i Fabregas, Albert Guillen
Nearest Neighbour Decoding with Pilot-Assisted Channel Estimation for Fading Multiple-Access Channels Proceedings Article
En: 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pp. 1686–1693, IEEE, Allerton, 2011, ISBN: 978-1-4577-1818-2.
Resumen | Enlaces | BibTeX | Etiquetas: Channel estimation, Decoding, Fading, fading channels, fading multiple-access channels, MIMO, MIMO communication, multi-access systems, multiple-input multiple-output channel, nearest-neighbour decoding, noncoherent MIMO fading MAC channel, pilot-assisted channel estimation, Receiving antennas, Signal to noise ratio, signal-to-noise ratio, Time division multiple access, Vectors
@inproceedings{Asyhari2011a,
title = {Nearest Neighbour Decoding with Pilot-Assisted Channel Estimation for Fading Multiple-Access Channels},
author = {Taufiq A Asyhari and Tobias Koch and Albert Guillen i Fabregas},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6120371},
isbn = {978-1-4577-1818-2},
year = {2011},
date = {2011-01-01},
booktitle = {2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton)},
pages = {1686--1693},
publisher = {IEEE},
address = {Allerton},
abstract = {This paper studies a noncoherent multiple-input multiple-output (MIMO) fading multiple-access channel (MAC). The rate region that is achievable with nearest neighbour decoding and pilot-assisted channel estimation is analysed and the corresponding pre-log region, defined as the limiting ratio of the rate region to the logarithm of the signal-to-noise ratio (SNR) as the SNR tends to infinity, is determined.},
keywords = {Channel estimation, Decoding, Fading, fading channels, fading multiple-access channels, MIMO, MIMO communication, multi-access systems, multiple-input multiple-output channel, nearest-neighbour decoding, noncoherent MIMO fading MAC channel, pilot-assisted channel estimation, Receiving antennas, Signal to noise ratio, signal-to-noise ratio, Time division multiple access, Vectors},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper studies a noncoherent multiple-input multiple-output (MIMO) fading multiple-access channel (MAC). The rate region that is achievable with nearest neighbour decoding and pilot-assisted channel estimation is analysed and the corresponding pre-log region, defined as the limiting ratio of the rate region to the logarithm of the signal-to-noise ratio (SNR) as the SNR tends to infinity, is determined.
Ruiz, Francisco J R; Perez-Cruz, Fernando
Zero-Error Codes for the Noisy-Typewriter Channel Proceedings Article
En: Summer Research Institute (SuRi), Lausanne, 2011.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Ruiz2011a,
title = {Zero-Error Codes for the Noisy-Typewriter Channel},
author = {Francisco J R Ruiz and Fernando Perez-Cruz},
url = {http://suri.epfl.ch/past/2011},
year = {2011},
date = {2011-01-01},
booktitle = {Summer Research Institute (SuRi)},
address = {Lausanne},
abstract = {In this paper we propose nontrivial codes that achieve a non-zero zero-error rate for several odd-letter noisy-typewriter channels. Some of these codes (specifically those which are defined for a number of letters of the channel of the form 2^n+1) achieve the best-known lower bound on the zero-error capacity. We build the codes using linear codes over rings as we do not require the multiplicative inverse to build the codes.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we propose nontrivial codes that achieve a non-zero zero-error rate for several odd-letter noisy-typewriter channels. Some of these codes (specifically those which are defined for a number of letters of the channel of the form 2^n+1) achieve the best-known lower bound on the zero-error capacity. We build the codes using linear codes over rings as we do not require the multiplicative inverse to build the codes.
Shan, Gong; Artés-Rodríguez, Antonio
Fast Background Elimination in Fluorescence Microbiology Images: Comparison of Four Algorithms Proceedings Article
En: 7th Artificial Intelligence Applications and Innovations Conference, pp. 285 – 290, Corfú, 2011.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Shan2011,
title = {Fast Background Elimination in Fluorescence Microbiology Images: Comparison of Four Algorithms},
author = {Gong Shan and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://link.springer.com/chapter/10.1007/978-3-642-23960-1_34},
year = {2011},
date = {2011-01-01},
booktitle = {7th Artificial Intelligence Applications and Innovations Conference},
pages = {285 -- 290},
address = {Corf\'{u}},
abstract = {In this work, we investigate a fast background elimination front-end of an automatic bacilli detection system. This background eliminating system consists of a feature descriptor followed by a linear-SVMs classifier. Four state-of-the-art feature extraction algorithms are analyzed and modified. Extensive experiments have been made on real sputum fluorescence images and the results reveal that 96.92% of the background content can be correctly removed from one image with an acceptable computational complexity.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this work, we investigate a fast background elimination front-end of an automatic bacilli detection system. This background eliminating system consists of a feature descriptor followed by a linear-SVMs classifier. Four state-of-the-art feature extraction algorithms are analyzed and modified. Extensive experiments have been made on real sputum fluorescence images and the results reveal that 96.92% of the background content can be correctly removed from one image with an acceptable computational complexity.
Koblents, Eugenia; Miguez, Joaquin
A Population Monte Carlo Method for Bayesian Inference and its Application to Stochastic Kinetic Models Proceedings Article
En: EUSIPCO 2011, Barcelona, 2011.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Koblents2011,
title = {A Population Monte Carlo Method for Bayesian Inference and its Application to Stochastic Kinetic Models},
author = {Eugenia Koblents and Joaquin Miguez},
url = {http://www.eurasip.org/Proceedings/Eusipco/Eusipco2011/papers/1569427761.pdf},
year = {2011},
date = {2011-01-01},
booktitle = {EUSIPCO 2011},
address = {Barcelona},
abstract = {We introduce an extension of the population Monte Carlo (PMC) methodology to address the problem of Bayesian in- ference in high dimensional models. Specifically, we intro- duce a technique for the selection and update of importance functions based on the construction of Gaussian Bayesian networks. The structure of the latter graphical model en- ables a sequential sampling procedure that requires draw- ing only from unidimensional conditional distributions an d leads to very efficient PMC algorithms. In order to illus- trate the potential of the new technique we have consid- ered the estimation of rate parameters in stochastic kineti c models (SKMs). SKMs are multivariate systems that model molecular interactions in biological and chemical problem s. We present some numerical results based on a simple SKM known as predator-prey mode},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We introduce an extension of the population Monte Carlo (PMC) methodology to address the problem of Bayesian in- ference in high dimensional models. Specifically, we intro- duce a technique for the selection and update of importance functions based on the construction of Gaussian Bayesian networks. The structure of the latter graphical model en- ables a sequential sampling procedure that requires draw- ing only from unidimensional conditional distributions an d leads to very efficient PMC algorithms. In order to illus- trate the potential of the new technique we have consid- ered the estimation of rate parameters in stochastic kineti c models (SKMs). SKMs are multivariate systems that model molecular interactions in biological and chemical problem s. We present some numerical results based on a simple SKM known as predator-prey mode
Barbosa, J L; Luengo, David
Distributed Target Detection in Centralized Wireless Sensor Networks with Communication Constraints Proceedings Article
En: 19th European Signal Processing Conference (EUSIPCO), Barcelona, 2011.
BibTeX | Etiquetas:
@inproceedings{Barbosa2011,
title = {Distributed Target Detection in Centralized Wireless Sensor Networks with Communication Constraints},
author = {J L Barbosa and David Luengo},
year = {2011},
date = {2011-01-01},
booktitle = {19th European Signal Processing Conference (EUSIPCO)},
address = {Barcelona},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Crisan, Dan; Miguez, Joaquin
Particle Aproximation of the Filtering Density and Its Derivates in General State. Space Models Proceedings Article
En: Bayesian Inference and Stochastic Processes (BISP 7), Getafe, 2011.
BibTeX | Etiquetas:
@inproceedings{Crisan2011,
title = {Particle Aproximation of the Filtering Density and Its Derivates in General State. Space Models},
author = {Dan Crisan and Joaquin Miguez},
year = {2011},
date = {2011-01-01},
booktitle = {Bayesian Inference and Stochastic Processes (BISP 7)},
address = {Getafe},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Plata-Chaves, Jorge; Lazaro, Marcelino; Artés-Rodríguez, Antonio
Optimal Neyman-Pearson Fusion in Two-Dimensional Densor Networks with Serial Architecture and Dependent Observations Proceedings Article
En: Information Fusion (FUSION), 2011 Proceedings of the 14th International Conference on, pp. 1–6, Chicago, 2011, ISBN: 978-1-4577-0267-9.
Resumen | Enlaces | BibTeX | Etiquetas: Bayesian methods, binary distributed detection problem, decision theory, dependent observations, Joints, local decision rule, Measurement uncertainty, Network topology, Neyman-Pearson criterion, optimal Neyman-Pearson fusion, optimum distributed detection, Parallel architectures, Performance evaluation, Probability density function, sensor dependent observations, sensor fusion, serial architecture, serial network topology, two-dimensional sensor networks, Wireless Sensor Networks
@inproceedings{Plata-Chaves2011bb,
title = {Optimal Neyman-Pearson Fusion in Two-Dimensional Densor Networks with Serial Architecture and Dependent Observations},
author = {Jorge Plata-Chaves and Marcelino Lazaro and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5977545\&searchWithin%3Dartes+rodriguez%26sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A5977431%29},
isbn = {978-1-4577-0267-9},
year = {2011},
date = {2011-01-01},
booktitle = {Information Fusion (FUSION), 2011 Proceedings of the 14th International Conference on},
pages = {1--6},
address = {Chicago},
abstract = {In this correspondence, we consider a sensor network with serial architecture. When solving a binary distributed detection problem where the sensor observations are dependent under each one of the two possible hypothesis, each fusion stage of the network applies a local decision rule. We assume that, based on the information available at each fusion stage, the decision rules provide a binary message regarding the presence or absence of an event of interest. Under this scenario and under a Neyman-Pearson formulation, we derive the optimal decision rules associated with each fusion stage. As it happens when the sensor observations are independent, we are able to show that, under the Neyman-Pearson criterion, the optimal fusion rules of a serial configuration with dependent observations also match optimal Neyman-Pearson tests.},
keywords = {Bayesian methods, binary distributed detection problem, decision theory, dependent observations, Joints, local decision rule, Measurement uncertainty, Network topology, Neyman-Pearson criterion, optimal Neyman-Pearson fusion, optimum distributed detection, Parallel architectures, Performance evaluation, Probability density function, sensor dependent observations, sensor fusion, serial architecture, serial network topology, two-dimensional sensor networks, Wireless Sensor Networks},
pubstate = {published},
tppubtype = {inproceedings}
}
In this correspondence, we consider a sensor network with serial architecture. When solving a binary distributed detection problem where the sensor observations are dependent under each one of the two possible hypothesis, each fusion stage of the network applies a local decision rule. We assume that, based on the information available at each fusion stage, the decision rules provide a binary message regarding the presence or absence of an event of interest. Under this scenario and under a Neyman-Pearson formulation, we derive the optimal decision rules associated with each fusion stage. As it happens when the sensor observations are independent, we are able to show that, under the Neyman-Pearson criterion, the optimal fusion rules of a serial configuration with dependent observations also match optimal Neyman-Pearson tests.
Parviainen, Jussi; Kirkko-Jaakkola, Martti; Davidson, Pavel; Vázquez, Manuel A; Collin, Jussi
Doppler radar and MEMS gyro augmented DGPS for large vehicle navigation Proceedings Article
En: 2011 International Conference on Localization and GNSS (ICL-GNSS), pp. 140-145, 2011.
@inproceedings{5955285,
title = {Doppler radar and MEMS gyro augmented DGPS for large vehicle navigation},
author = {Jussi Parviainen and Martti Kirkko-Jaakkola and Pavel Davidson and Manuel A V\'{a}zquez and Jussi Collin},
doi = {10.1109/ICL-GNSS.2011.5955285},
year = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
booktitle = {2011 International Conference on Localization and GNSS (ICL-GNSS)},
pages = {140-145},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2010
López-Valcarce, Roberto; Vazquez-Vilar, Gonzalo; Sala, Josep
Multiantenna spectrum sensing for Cognitive Radio: overcoming noise uncertainty Proceedings Article
En: The 2nd International Workshop on Cognitive Information Processing (CIP 2010), Elba Island (Tuscany), Italy, 2010, (Invited).
BibTeX | Etiquetas:
@inproceedings{cip2010,
title = {Multiantenna spectrum sensing for Cognitive Radio: overcoming noise uncertainty},
author = {Roberto L\'{o}pez-Valcarce and Gonzalo Vazquez-Vilar and Josep Sala},
year = {2010},
date = {2010-06-01},
booktitle = {The 2nd International Workshop on Cognitive Information Processing (CIP 2010)},
address = {Elba Island (Tuscany), Italy},
note = {Invited},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
El-Howayek, Georges; Jayaweera, Sudharman K; Hakim, Kamrul; Vazquez-Vilar, Gonzalo; Mosquera, Carlos
Dynamic Spectrum Leasing (DSL) in Dynamic Channels Proceedings Article
En: ICC'10 Workshop on Cognitive Radio Interfaces and Signal Processing (ICC'10 Workshop CRISP), Cape Town, South Africa, 2010.
BibTeX | Etiquetas:
@inproceedings{crisp2010,
title = {Dynamic Spectrum Leasing (DSL) in Dynamic Channels},
author = {Georges El-Howayek and Sudharman K Jayaweera and Kamrul Hakim and Gonzalo Vazquez-Vilar and Carlos Mosquera},
year = {2010},
date = {2010-05-01},
booktitle = {ICC'10 Workshop on Cognitive Radio Interfaces and Signal Processing (ICC'10 Workshop CRISP)},
address = {Cape Town, South Africa},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Vazquez-Vilar, Gonzalo; López-Valcarce, Roberto; Mosquera, Carlos; González-Prelcic, Nuria
Wideband Spectral Estimation from Compressed Measurements Exploiting Spectral a priori Information in Cognitive Radio Systems Proceedings Article
En: 2010 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010), Dallas, U.S.A., 2010.
BibTeX | Etiquetas:
@inproceedings{icassp2010,
title = {Wideband Spectral Estimation from Compressed Measurements Exploiting Spectral a priori Information in Cognitive Radio Systems},
author = {Gonzalo Vazquez-Vilar and Roberto L\'{o}pez-Valcarce and Carlos Mosquera and Nuria Gonz\'{a}lez-Prelcic},
year = {2010},
date = {2010-03-01},
booktitle = {2010 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010)},
address = {Dallas, U.S.A.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Vazquez, Manuel A; Miguez, Joaquin
Adaptive MLSD for MIMO Transmission Systems with Unknown Subchannel Orders Proceedings Article
En: 2010 7th International Symposium on Wireless Communication Systems, pp. 451–455, IEEE, York, 2010, ISSN: 2154-0217.
Resumen | Enlaces | BibTeX | Etiquetas: Bit error rate, Channel estimation, channel impulse response, computational complexity, Estimation, frequency-selective multiple-input multiple-output, maximum likelihood sequence detection, maximum likelihood sequence estimation, MIMO, MIMO communication, MIMO transmission systems, multiple subchannels, per survivor processing methodology, pilot data, Receivers, Signal to noise ratio, Time frequency analysis, time selective MIMO channel
@inproceedings{Vazquez2010,
title = {Adaptive MLSD for MIMO Transmission Systems with Unknown Subchannel Orders},
author = {Manuel A Vazquez and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5624335},
issn = {2154-0217},
year = {2010},
date = {2010-01-01},
booktitle = {2010 7th International Symposium on Wireless Communication Systems},
pages = {451--455},
publisher = {IEEE},
address = {York},
abstract = {In the equalization of frequency-selective multiple-input multiple-output (MIMO) channels it is usually assumed that the length of the channel impulse response (CIR), also referred to as the channel order, is known. However, this is not true in most practical situations and, in order to avoid the serious performance degradation that occurs when the CIR length is underestimated, a channel with "more than enough" taps is usually considered. This very frequently leads to overestimating the channel order, which increases the computational complexity of any maximum likelihood sequence detection (MLSD) algorithm, while degrading its performance at the same time. The problem of estimating a single channel order for a time and frequency selective MIMO channel has recently been tackled. However, this is an idealized approach, since a MIMO channel comprises multiple subchannels (as many as the number of inputs times that of the outputs), each of them possibly with its own order. In this paper, we introduce an algorithm for MLSD that incorporates the full estimation of the MIMO CIR parameters, including one channel order per output. The proposed technique is based on the per survivor processing (PSP) methodology, it admits both blind and semiblind implementations, depending on the availability of pilot data, and it is designed to work with time-selective channels. Besides the analytical derivation of the algorithm, we provide computer simulation results that illustrate the effectiveness of the resulting receiver.},
keywords = {Bit error rate, Channel estimation, channel impulse response, computational complexity, Estimation, frequency-selective multiple-input multiple-output, maximum likelihood sequence detection, maximum likelihood sequence estimation, MIMO, MIMO communication, MIMO transmission systems, multiple subchannels, per survivor processing methodology, pilot data, Receivers, Signal to noise ratio, Time frequency analysis, time selective MIMO channel},
pubstate = {published},
tppubtype = {inproceedings}
}
In the equalization of frequency-selective multiple-input multiple-output (MIMO) channels it is usually assumed that the length of the channel impulse response (CIR), also referred to as the channel order, is known. However, this is not true in most practical situations and, in order to avoid the serious performance degradation that occurs when the CIR length is underestimated, a channel with "more than enough" taps is usually considered. This very frequently leads to overestimating the channel order, which increases the computational complexity of any maximum likelihood sequence detection (MLSD) algorithm, while degrading its performance at the same time. The problem of estimating a single channel order for a time and frequency selective MIMO channel has recently been tackled. However, this is an idealized approach, since a MIMO channel comprises multiple subchannels (as many as the number of inputs times that of the outputs), each of them possibly with its own order. In this paper, we introduce an algorithm for MLSD that incorporates the full estimation of the MIMO CIR parameters, including one channel order per output. The proposed technique is based on the per survivor processing (PSP) methodology, it admits both blind and semiblind implementations, depending on the availability of pilot data, and it is designed to work with time-selective channels. Besides the analytical derivation of the algorithm, we provide computer simulation results that illustrate the effectiveness of the resulting receiver.
Valera, Isabel; Sieskul, B T; Zheng, F; Kaiser, T
A Hybrid SS-ToA Wireless Ge- olocation Based on Path Attenuation under Imperfect Path Loss Exponent Proceedings Article
En: 18th European Signal Processing Conference (EUSIPCO-2010), Aalborg, 2010.
Resumen | Enlaces | BibTeX | Etiquetas: hood estimator, maximum likeli-, Path loss exponent, Time-of-arrival estimation
@inproceedings{Valera2010,
title = {A Hybrid SS-ToA Wireless Ge- olocation Based on Path Attenuation under Imperfect Path Loss Exponent},
author = {Isabel Valera and B T Sieskul and F Zheng and T Kaiser},
url = {http://www.eurasip.org/Proceedings/Eusipco/Eusipco2010/Contents/papers/1569292415.pdf},
year = {2010},
date = {2010-01-01},
booktitle = {18th European Signal Processing Conference (EUSIPCO-2010)},
address = {Aalborg},
abstract = {We consider the wireless geolocationusing the time of arrival (ToA) of radio signals in a cellular setting. The main concern in this paper involves the effects of the error knowledge of the path loss exponent (PLE). We derive the asymptotic error performance of the maximum likelihood (ML) estimator un- der the imperfect PLE. We point out that a previous method provides inaccurate performance prediction and then present a new method based on the Taylor series expansion. Numer- ical examples illustrate that the Taylor analysis captures the bias and the error variance of the ML estimator under the im- perfect PLE better than the conventional method. Simulation results also illustrate that in the threshold region, the ML es- timator outperforms the MC estimator even in the presence of the PLE error. However, in the asymptotic region the MC estimator and the ML estimator with the perfect PLE outper- form the ML estimator under the imperfect PLE.},
keywords = {hood estimator, maximum likeli-, Path loss exponent, Time-of-arrival estimation},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider the wireless geolocationusing the time of arrival (ToA) of radio signals in a cellular setting. The main concern in this paper involves the effects of the error knowledge of the path loss exponent (PLE). We derive the asymptotic error performance of the maximum likelihood (ML) estimator un- der the imperfect PLE. We point out that a previous method provides inaccurate performance prediction and then present a new method based on the Taylor series expansion. Numer- ical examples illustrate that the Taylor analysis captures the bias and the error variance of the ML estimator under the im- perfect PLE better than the conventional method. Simulation results also illustrate that in the threshold region, the ML es- timator outperforms the MC estimator even in the presence of the PLE error. However, in the asymptotic region the MC estimator and the ML estimator with the perfect PLE outper- form the ML estimator under the imperfect PLE.
Martino, Luca; Miguez, Joaquin
A Rejection Sampling Scheme for Posterior Probability Distributions via the Ratio-of-Uniforms Method Proceedings Article
En: 18th European Signal Processing Conference (EUSIPCO-2010), Aalborg, 2010.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Martino2010,
title = {A Rejection Sampling Scheme for Posterior Probability Distributions via the Ratio-of-Uniforms Method},
author = {Luca Martino and Joaquin Miguez},
url = {http://www.academia.edu/2355638/A_rejection_sampling_scheme_for_posterior_probability_distributions_via_the_ratio-of-uniforms_method},
year = {2010},
date = {2010-01-01},
booktitle = {18th European Signal Processing Conference (EUSIPCO-2010)},
address = {Aalborg},
abstract = {Accept/reject sampling is a well-known method to generaterandom samples from arbitrary target probability distribu-tions. It demands the design of a suitable proposal probabil-ity density function (pdf) from which candidate samples canbe drawn. The main limitation to the use of RS is the needto find an adequate upper bound for the ratio of the targetpdf over the proposal pdf from which the samples are gener-ated. There are no general methods to analytically find thisbound, except when the target pdf is log-concave. In thispaper we introduce a novel procedure using the ratio of uni-forms method to efficiently perform rejection sampling fora large class of target densities. The candidate samples aregenerated using only two independent uniform random vari-ables. In order to illustrate the application of the proposedtechnique, we provide a numerical example},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Accept/reject sampling is a well-known method to generaterandom samples from arbitrary target probability distribu-tions. It demands the design of a suitable proposal probabil-ity density function (pdf) from which candidate samples canbe drawn. The main limitation to the use of RS is the needto find an adequate upper bound for the ratio of the targetpdf over the proposal pdf from which the samples are gener-ated. There are no general methods to analytically find thisbound, except when the target pdf is log-concave. In thispaper we introduce a novel procedure using the ratio of uni-forms method to efficiently perform rejection sampling fora large class of target densities. The candidate samples aregenerated using only two independent uniform random vari-ables. In order to illustrate the application of the proposedtechnique, we provide a numerical example
Ruiz, Manuel Martinez; Artés-Rodríguez, Antonio; Diaz-Rico, Jose Antonio; Fuentes, Jose Blanco
New Initiatives for Imagery Transmission over a Tactical Data Link. A Case Study: JPEG2000 Compressed Images Transmitted in a Link-16 Network. Method and Results Proceedings Article
En: 2010 - MILCOM 2010 MILITARY COMMUNICATIONS CONFERENCE, pp. 1163–1168, IEEE, San Jose, 2010, ISSN: 2155-7578.
Resumen | Enlaces | BibTeX | Etiquetas: Bit rate, code stream, data stream, Decoding, discrete wavelet transforms, Image coding, image compression, imagery transmission, JPEG-2000, JPEG2000 compressed images, link-16, Link-16 Enhance Throughput, Link-16 tactical network, MIDS-LVT, military communication, multirresolution, operational requirement, packing limit, PSNR, Security, Streaming media, tactical data link, time slot, Transform coding, wavelet discrete transforms, wavelets
@inproceedings{Martinez-Ruiz2010,
title = {New Initiatives for Imagery Transmission over a Tactical Data Link. A Case Study: JPEG2000 Compressed Images Transmitted in a Link-16 Network. Method and Results},
author = {Manuel Martinez Ruiz and Antonio Art\'{e}s-Rodr\'{i}guez and Jose Antonio Diaz-Rico and Jose Blanco Fuentes},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5680102},
issn = {2155-7578},
year = {2010},
date = {2010-01-01},
booktitle = {2010 - MILCOM 2010 MILITARY COMMUNICATIONS CONFERENCE},
pages = {1163--1168},
publisher = {IEEE},
address = {San Jose},
abstract = {This paper presents the results of an initiative to transmit imagery content through a Link-16 tactical network using a multirresolution approach based on wavelets to compress images. Firstly, we identify the operational requirements. Secondly, we justify why JPEG2000 is our choice for coding still images. Thirdly, we propose a method to map the JPEG2000 code-stream into Link-16 free-text messages. We propose to send the most important part of the JPEG2000 compressed image in a more error resistant Link-16 packed structure and the remaining of the image in less robust data structures but at higher data rates. Finally, we present our results based on software simulations and laboratory tests with real Link-16 terminals including a comparative analysis with Link-16 enhance throughput. A configuration using two MIDS-LVTs has being set up, along with JPEG2000 coding and decoding software tools.},
keywords = {Bit rate, code stream, data stream, Decoding, discrete wavelet transforms, Image coding, image compression, imagery transmission, JPEG-2000, JPEG2000 compressed images, link-16, Link-16 Enhance Throughput, Link-16 tactical network, MIDS-LVT, military communication, multirresolution, operational requirement, packing limit, PSNR, Security, Streaming media, tactical data link, time slot, Transform coding, wavelet discrete transforms, wavelets},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents the results of an initiative to transmit imagery content through a Link-16 tactical network using a multirresolution approach based on wavelets to compress images. Firstly, we identify the operational requirements. Secondly, we justify why JPEG2000 is our choice for coding still images. Thirdly, we propose a method to map the JPEG2000 code-stream into Link-16 free-text messages. We propose to send the most important part of the JPEG2000 compressed image in a more error resistant Link-16 packed structure and the remaining of the image in less robust data structures but at higher data rates. Finally, we present our results based on software simulations and laboratory tests with real Link-16 terminals including a comparative analysis with Link-16 enhance throughput. A configuration using two MIDS-LVTs has being set up, along with JPEG2000 coding and decoding software tools.
Koch, Tobias; Lapidoth, Amos
Increased Capacity per Unit-Cost by Oversampling Proceedings Article
En: 2010 IEEE 26-th Convention of Electrical and Electronics Engineers in Israel, pp. 000684–000688, IEEE, Eliat, 2010, ISBN: 978-1-4244-8681-6.
Resumen | Enlaces | BibTeX | Etiquetas: AWGN, AWGN channels, bandlimited Gaussian channel, channel capacity, Gaussian channels, increased capacity per unit cost, Information rates, one bit output quantizer, oversampling, quantisation (signal), quantization, sampling rate recovery, signal sampling
@inproceedings{Koch2010,
title = {Increased Capacity per Unit-Cost by Oversampling},
author = {Tobias Koch and Amos Lapidoth},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5662127},
isbn = {978-1-4244-8681-6},
year = {2010},
date = {2010-01-01},
booktitle = {2010 IEEE 26-th Convention of Electrical and Electronics Engineers in Israel},
pages = {000684--000688},
publisher = {IEEE},
address = {Eliat},
abstract = {It is demonstrated that doubling the sampling rate recovers some of the loss in capacity incurred on the bandlimited Gaussian channel with a one-bit output quantizer.},
keywords = {AWGN, AWGN channels, bandlimited Gaussian channel, channel capacity, Gaussian channels, increased capacity per unit cost, Information rates, one bit output quantizer, oversampling, quantisation (signal), quantization, sampling rate recovery, signal sampling},
pubstate = {published},
tppubtype = {inproceedings}
}
It is demonstrated that doubling the sampling rate recovers some of the loss in capacity incurred on the bandlimited Gaussian channel with a one-bit output quantizer.
Murillo-Fuentes, Juan Jose; Olmos, Pablo M; Perez-Cruz, Fernando
Analyzing the Maxwell Decoder for LDPC Codes in Binary Erasure Channels Proceedings Article
En: Information Theory and Applications (ITA), San Diego, 2010.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Murillo-Fuentes2010,
title = {Analyzing the Maxwell Decoder for LDPC Codes in Binary Erasure Channels},
author = {Juan Jose Murillo-Fuentes and Pablo M Olmos and Fernando Perez-Cruz},
url = {http://ita.ucsd.edu/workshop/10/files/abstract/abstract_1462.txt},
year = {2010},
date = {2010-01-01},
booktitle = {Information Theory and Applications (ITA)},
address = {San Diego},
abstract = {The Maxwell decoder has been proposed for bridging the gap between the achievable capacity by belief propagation decoding and the maximum a posteriori decoder in binary erasure channels of LDPC codes. The Maxwell decoder, once the belief-propagation decoder gets stuck in a nonempty stopping set, guesses a bit and replicates any running copies of the decoding process. Density evolution and EXIT chart analyses of this iterative decoder show that MAP performance can be derived from the performance of the BP decoder. The complexity of the Maxwell decoder depends exponentially on the number of guesses and a priori we cannot bound the number of guesses, which limits its applicability as a LDPC decoder. In this paper, we adapt the expectation propagation algorithm for LDPC decoding. Our algorithm can be understood as a Maxwell decoder with a bounded complexity. For unbounded complexity it achieves maximum a posteriori decoding. In this paper, we analyze in detail the simplest version of the algorithm, whose complexity is identical to belief propagation, and we demonstrate that the achieved capacity is higher than that of the belief propagation decoder.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The Maxwell decoder has been proposed for bridging the gap between the achievable capacity by belief propagation decoding and the maximum a posteriori decoder in binary erasure channels of LDPC codes. The Maxwell decoder, once the belief-propagation decoder gets stuck in a nonempty stopping set, guesses a bit and replicates any running copies of the decoding process. Density evolution and EXIT chart analyses of this iterative decoder show that MAP performance can be derived from the performance of the BP decoder. The complexity of the Maxwell decoder depends exponentially on the number of guesses and a priori we cannot bound the number of guesses, which limits its applicability as a LDPC decoder. In this paper, we adapt the expectation propagation algorithm for LDPC decoding. Our algorithm can be understood as a Maxwell decoder with a bounded complexity. For unbounded complexity it achieves maximum a posteriori decoding. In this paper, we analyze in detail the simplest version of the algorithm, whose complexity is identical to belief propagation, and we demonstrate that the achieved capacity is higher than that of the belief propagation decoder.
Olmos, Pablo M; Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando
Tree-Structure Expectation Propagation for Decoding LDPC Codes over Binary Erasure Channels Proceedings Article
En: 2010 IEEE International Symposium on Information Theory, pp. 799–803, IEEE, Austin, TX, 2010, ISBN: 978-1-4244-7892-7.
Resumen | Enlaces | BibTeX | Etiquetas: belief propagation, binary erasure channels, Bipartite graph, BP decoder, Capacity planning, Channel Coding, codeword, computational complexity, Decoding, Finishing, graph theory, H infinity control, LDPC code decoding, LDPC Tanner graph, Maxwell decoder, parity check codes, Performance analysis, tree structure expectation propagation, trees (mathematics), Upper bound
@inproceedings{Olmos2010,
title = {Tree-Structure Expectation Propagation for Decoding LDPC Codes over Binary Erasure Channels},
author = {Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5513636},
isbn = {978-1-4244-7892-7},
year = {2010},
date = {2010-01-01},
booktitle = {2010 IEEE International Symposium on Information Theory},
pages = {799--803},
publisher = {IEEE},
address = {Austin, TX},
abstract = {Expectation Propagation is a generalization to Belief Propagation (BP) in two ways. First, it can be used with any exponential family distribution over the cliques in the graph. Second, it can impose additional constraints on the marginal distributions. We use this second property to impose pair-wise marginal distribution constraints in some check nodes of the LDPC Tanner graph. These additional constraints allow decoding the received codeword when the BP decoder gets stuck. In this paper, we first present the new decoding algorithm, whose complexity is identical to the BP decoder, and we then prove that it is able to decode codewords with a larger fraction of erasures, as the block size tends to infinity. The proposed algorithm can be also understood as a simplification of the Maxwell decoder, but without its computational complexity. We also illustrate that the new algorithm outperforms the BP decoder for finite block-size codes.},
keywords = {belief propagation, binary erasure channels, Bipartite graph, BP decoder, Capacity planning, Channel Coding, codeword, computational complexity, Decoding, Finishing, graph theory, H infinity control, LDPC code decoding, LDPC Tanner graph, Maxwell decoder, parity check codes, Performance analysis, tree structure expectation propagation, trees (mathematics), Upper bound},
pubstate = {published},
tppubtype = {inproceedings}
}
Expectation Propagation is a generalization to Belief Propagation (BP) in two ways. First, it can be used with any exponential family distribution over the cliques in the graph. Second, it can impose additional constraints on the marginal distributions. We use this second property to impose pair-wise marginal distribution constraints in some check nodes of the LDPC Tanner graph. These additional constraints allow decoding the received codeword when the BP decoder gets stuck. In this paper, we first present the new decoding algorithm, whose complexity is identical to the BP decoder, and we then prove that it is able to decode codewords with a larger fraction of erasures, as the block size tends to infinity. The proposed algorithm can be also understood as a simplification of the Maxwell decoder, but without its computational complexity. We also illustrate that the new algorithm outperforms the BP decoder for finite block-size codes.
Djuric, Petar M; Closas, Pau; Bugallo, Monica F; Miguez, Joaquin
Evaluation of a Method's Robustness Proceedings Article
En: 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3598–3601, IEEE, Dallas, 2010, ISSN: 1520-6149.
Resumen | Enlaces | BibTeX | Etiquetas: Electronic mail, Extraterrestrial measurements, Filtering, Gaussian processes, method's robustness, Random variables, robustness, sequential methods, Signal processing, statistical distributions, Telecommunications, uniform distribution, Wireless communication
@inproceedings{Djuric2010,
title = {Evaluation of a Method's Robustness},
author = {Petar M Djuric and Pau Closas and Monica F Bugallo and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5495921},
issn = {1520-6149},
year = {2010},
date = {2010-01-01},
booktitle = {2010 IEEE International Conference on Acoustics, Speech and Signal Processing},
pages = {3598--3601},
publisher = {IEEE},
address = {Dallas},
abstract = {In signal processing, it is typical to develop or use a method based on a given model. In practice, however, we almost never know the actual model and we hope that the assumed model is in the neighborhood of the true one. If deviations exist, the method may be more or less sensitive to them. Therefore, it is important to know more about this sensitivity, or in other words, how robust the method is to model deviations. To that end, it is useful to have a metric that can quantify the robustness of the method. In this paper we propose a procedure for developing a variety of metrics for measuring robustness. They are based on a discrete random variable that is generated from observed data and data generated according to past data and the adopted model. This random variable is uniform if the model is correct. When the model deviates from the true one, the distribution of the random variable deviates from the uniform distribution. One can then employ measures for differences between distributions in order to quantify robustness. In this paper we describe the proposed methodology and demonstrate it with simulated data.},
keywords = {Electronic mail, Extraterrestrial measurements, Filtering, Gaussian processes, method's robustness, Random variables, robustness, sequential methods, Signal processing, statistical distributions, Telecommunications, uniform distribution, Wireless communication},
pubstate = {published},
tppubtype = {inproceedings}
}
In signal processing, it is typical to develop or use a method based on a given model. In practice, however, we almost never know the actual model and we hope that the assumed model is in the neighborhood of the true one. If deviations exist, the method may be more or less sensitive to them. Therefore, it is important to know more about this sensitivity, or in other words, how robust the method is to model deviations. To that end, it is useful to have a metric that can quantify the robustness of the method. In this paper we propose a procedure for developing a variety of metrics for measuring robustness. They are based on a discrete random variable that is generated from observed data and data generated according to past data and the adopted model. This random variable is uniform if the model is correct. When the model deviates from the true one, the distribution of the random variable deviates from the uniform distribution. One can then employ measures for differences between distributions in order to quantify robustness. In this paper we describe the proposed methodology and demonstrate it with simulated data.
Salamanca, Luis; Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando
Bayesian BCJR for Channel Equalization and Decoding Proceedings Article
En: 2010 IEEE International Workshop on Machine Learning for Signal Processing, pp. 53–58, IEEE, Kittila, 2010, ISSN: 1551-2541.
Resumen | Enlaces | BibTeX | Etiquetas: a posteriori probability, Bayes methods, Bayesian BCJR, Bayesian methods, Bit error rate, channel decoding, channel estate information, Channel estimation, Decoding, digital communication, digital communications, equalisers, Equalizers, error statistics, Markov processes, Maximum likelihood decoding, maximum likelihood estimation, multipath channel, probabilistic channel equalization, Probability, single input single output model, SISO model, statistical information, Training
@inproceedings{Salamanca2010,
title = {Bayesian BCJR for Channel Equalization and Decoding},
author = {Luis Salamanca and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5589201},
issn = {1551-2541},
year = {2010},
date = {2010-01-01},
booktitle = {2010 IEEE International Workshop on Machine Learning for Signal Processing},
pages = {53--58},
publisher = {IEEE},
address = {Kittila},
abstract = {In this paper we focus on the probabilistic channel equalization in digital communications. We face the single input single output (SISO) model to show how the statistical information about the multipath channel can be exploited to further improve our estimation of the a posteriori probabilities (APP) during the equalization process. We consider not only the uncertainty due to the noise in the channel, but also in the estimate of the channel estate information (CSI). Thus, we resort to a Bayesian approach for the computation of the APP. This novel algorithm has the same complexity as the BCJR, exhibiting lower bit error rate at the output of the channel decoder than the standard BCJR that considers maximum likelihood (ML) to estimate the CSI.},
keywords = {a posteriori probability, Bayes methods, Bayesian BCJR, Bayesian methods, Bit error rate, channel decoding, channel estate information, Channel estimation, Decoding, digital communication, digital communications, equalisers, Equalizers, error statistics, Markov processes, Maximum likelihood decoding, maximum likelihood estimation, multipath channel, probabilistic channel equalization, Probability, single input single output model, SISO model, statistical information, Training},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we focus on the probabilistic channel equalization in digital communications. We face the single input single output (SISO) model to show how the statistical information about the multipath channel can be exploited to further improve our estimation of the a posteriori probabilities (APP) during the equalization process. We consider not only the uncertainty due to the noise in the channel, but also in the estimate of the channel estate information (CSI). Thus, we resort to a Bayesian approach for the computation of the APP. This novel algorithm has the same complexity as the BCJR, exhibiting lower bit error rate at the output of the channel decoder than the standard BCJR that considers maximum likelihood (ML) to estimate the CSI.
Vinuelas-Peris, Pablo; Artés-Rodríguez, Antonio
Bayesian Joint Recovery of Correlated Signals in Distributed Compressed Sensing Proceedings Article
En: 2010 2nd International Workshop on Cognitive Information Processing, pp. 382–387, IEEE, Elba, 2010, ISBN: 978-1-4244-6459-3.
Resumen | Enlaces | BibTeX | Etiquetas: Bayes methods, Bayesian joint recovery, Bayesian methods, correlated signal, Correlation, correlation methods, Covariance matrix, Dictionaries, distributed compressed sensing, matrix decomposition, Noise measurement, sensors, sparse component correlation coefficient
@inproceedings{Vinuelas-Peris2010,
title = {Bayesian Joint Recovery of Correlated Signals in Distributed Compressed Sensing},
author = {Pablo Vinuelas-Peris and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5604103},
isbn = {978-1-4244-6459-3},
year = {2010},
date = {2010-01-01},
booktitle = {2010 2nd International Workshop on Cognitive Information Processing},
pages = {382--387},
publisher = {IEEE},
address = {Elba},
abstract = {In this paper we address the problem of Distributed Compressed Sensing (DCS) of correlated signals. We model the correlation using the sparse components correlation coefficient of signals, a general and simple measure. We develop an sparse Bayesian learning method for this setting, that can be applied to both random and optimized projection matrices. As a result, we obtain a reduction of the number of measurements needed for a given recovery error that is dependent on the correlation coefficient, as shown by computer simulations in different scenarios.},
keywords = {Bayes methods, Bayesian joint recovery, Bayesian methods, correlated signal, Correlation, correlation methods, Covariance matrix, Dictionaries, distributed compressed sensing, matrix decomposition, Noise measurement, sensors, sparse component correlation coefficient},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we address the problem of Distributed Compressed Sensing (DCS) of correlated signals. We model the correlation using the sparse components correlation coefficient of signals, a general and simple measure. We develop an sparse Bayesian learning method for this setting, that can be applied to both random and optimized projection matrices. As a result, we obtain a reduction of the number of measurements needed for a given recovery error that is dependent on the correlation coefficient, as shown by computer simulations in different scenarios.
Achutegui, Katrin; Rodas, Javier; Escudero, Carlos J; Miguez, Joaquin
A Model-Switching Sequential Monte Carlo Algorithm for Indoor Tracking with Experimental RSS Data Proceedings Article
En: 2010 International Conference on Indoor Positioning and Indoor Navigation, pp. 1–8, IEEE, Zurich, 2010, ISBN: 978-1-4244-5862-2.
Resumen | Enlaces | BibTeX | Etiquetas: Approximation methods, Computational modeling, Data models, generalized IMM system, GIMM approach, indoor radio, Indoor tracking, Kalman filters, maneuvering target motion, Mathematical model, model switching sequential Monte Carlo algorithm, Monte Carlo methods, multipath propagation, multiple model interaction, propagation environment, radio receivers, radio tracking, radio transmitters, random processes, Rao-Blackwellized sequential Monte Carlo tracking, received signal strength, RSS data, sensors, state space model, target position dependent data, transmitter-to-receiver distance, wireless technology
@inproceedings{Achutegui2010,
title = {A Model-Switching Sequential Monte Carlo Algorithm for Indoor Tracking with Experimental RSS Data},
author = {Katrin Achutegui and Javier Rodas and Carlos J Escudero and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5648053},
isbn = {978-1-4244-5862-2},
year = {2010},
date = {2010-01-01},
booktitle = {2010 International Conference on Indoor Positioning and Indoor Navigation},
pages = {1--8},
publisher = {IEEE},
address = {Zurich},
abstract = {In this paper we address the problem of indoor tracking using received signal strength (RSS) as position-dependent data. This type of measurements are very appealing because they can be easily obtained with a variety of (inexpensive) wireless technologies. However, the extraction of accurate location information from RSS in indoor scenarios is not an easy task. Due to the multipath propagation, it is hard to adequately model the correspondence between the received power and the transmitter-to-receiver distance. For that reason, we propose the use of a compound model that combines several sub-models, whose parameters are adjusted to different propagation environments. This methodology, called Interacting Multiple Models (IMM), has been used in the past either for modeling the motion of maneuvering targets or the relationship between the target position and the observations. Here, we extend its application to handle both types of uncertainty simultaneously and we refer to the resulting state-space model as a generalized IMM (GIMM) system. The flexibility of the GIMM approach is attained at the expense of an increase in the number of random processes that must be accurately tracked. To overcome this difficulty, we introduce a Rao-Blackwellized sequential Monte Carlo tracking algorithm that exhibits good performance both with synthetic and experimental data.},
keywords = {Approximation methods, Computational modeling, Data models, generalized IMM system, GIMM approach, indoor radio, Indoor tracking, Kalman filters, maneuvering target motion, Mathematical model, model switching sequential Monte Carlo algorithm, Monte Carlo methods, multipath propagation, multiple model interaction, propagation environment, radio receivers, radio tracking, radio transmitters, random processes, Rao-Blackwellized sequential Monte Carlo tracking, received signal strength, RSS data, sensors, state space model, target position dependent data, transmitter-to-receiver distance, wireless technology},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we address the problem of indoor tracking using received signal strength (RSS) as position-dependent data. This type of measurements are very appealing because they can be easily obtained with a variety of (inexpensive) wireless technologies. However, the extraction of accurate location information from RSS in indoor scenarios is not an easy task. Due to the multipath propagation, it is hard to adequately model the correspondence between the received power and the transmitter-to-receiver distance. For that reason, we propose the use of a compound model that combines several sub-models, whose parameters are adjusted to different propagation environments. This methodology, called Interacting Multiple Models (IMM), has been used in the past either for modeling the motion of maneuvering targets or the relationship between the target position and the observations. Here, we extend its application to handle both types of uncertainty simultaneously and we refer to the resulting state-space model as a generalized IMM (GIMM) system. The flexibility of the GIMM approach is attained at the expense of an increase in the number of random processes that must be accurately tracked. To overcome this difficulty, we introduce a Rao-Blackwellized sequential Monte Carlo tracking algorithm that exhibits good performance both with synthetic and experimental data.
Helander, E; Silén, H; Miguez, Joaquin; Gabbouj, M
Maximum a Posteriori Voice Conversion Using Sequential Monte Carlo Methods Proceedings Article
En: Eleventh Annual Conference of the International Speech Communication Association (INTERSPEECH), Makuhari, Chiba, Japan, 2010.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Helander2010,
title = {Maximum a Posteriori Voice Conversion Using Sequential Monte Carlo Methods},
author = {E Helander and H Sil\'{e}n and Joaquin Miguez and M Gabbouj},
url = {http://www.isca-speech.org/archive/interspeech_2010/i10_1716.html},
year = {2010},
date = {2010-01-01},
booktitle = {Eleventh Annual Conference of the International Speech Communication Association (INTERSPEECH)},
address = {Makuhari, Chiba, Japan},
abstract = {Many voice conversion algorithms are based on frame-wise mapping from source features into target features. This ignores the inherent temporal continuity that is present in speech and can degrade the subjective quality. In this paper, we propose to optimize the speech feature sequence after a frame-based conversion algorithm has been applied. In particular, we select the sequence of speech features through the minimization of a cost function that involves both the conversion error and the smoothness of the sequence. The estimation problem is solved using sequential Monte Carlo methods. Both subjective and objective results show the effectiveness of the method.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Many voice conversion algorithms are based on frame-wise mapping from source features into target features. This ignores the inherent temporal continuity that is present in speech and can degrade the subjective quality. In this paper, we propose to optimize the speech feature sequence after a frame-based conversion algorithm has been applied. In particular, we select the sequence of speech features through the minimization of a cost function that involves both the conversion error and the smoothness of the sequence. The estimation problem is solved using sequential Monte Carlo methods. Both subjective and objective results show the effectiveness of the method.
Salamanca, Luis; Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando
Channel Decoding with a Bayesian Equalizer Proceedings Article
En: 2010 IEEE International Symposium on Information Theory, pp. 1998–2002, IEEE, Austin, TX, 2010, ISBN: 978-1-4244-7892-7.
Resumen | Enlaces | BibTeX | Etiquetas: a posteriori probability, Bayesian equalizer, Bayesian methods, BER, Bit error rate, Channel Coding, channel decoding, channel estate information, Communication channels, Decoding, equalisers, Equalizers, error statistics, low-density parity-check decoders, LPDC decoders, Maximum likelihood decoding, maximum likelihood detection, maximum likelihood estimation, Noise reduction, parity check codes, Probability, Uncertainty
@inproceedings{Salamanca2010a,
title = {Channel Decoding with a Bayesian Equalizer},
author = {Luis Salamanca and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5513348},
isbn = {978-1-4244-7892-7},
year = {2010},
date = {2010-01-01},
booktitle = {2010 IEEE International Symposium on Information Theory},
pages = {1998--2002},
publisher = {IEEE},
address = {Austin, TX},
abstract = {Low-density parity-check (LPDC) decoders assume the channel estate information (CSI) is known and they have the true a posteriori probability (APP) for each transmitted bit. But in most cases of interest, the CSI needs to be estimated with the help of a short training sequence and the LDPC decoder has to decode the received word using faulty APP estimates. In this paper, we study the uncertainty in the CSI estimate and how it affects the bit error rate (BER) output by the LDPC decoder. To improve these APP estimates, we propose a Bayesian equalizer that takes into consideration not only the uncertainty due to the noise in the channel, but also the uncertainty in the CSI estimate, reducing the BER after the LDPC decoder.},
keywords = {a posteriori probability, Bayesian equalizer, Bayesian methods, BER, Bit error rate, Channel Coding, channel decoding, channel estate information, Communication channels, Decoding, equalisers, Equalizers, error statistics, low-density parity-check decoders, LPDC decoders, Maximum likelihood decoding, maximum likelihood detection, maximum likelihood estimation, Noise reduction, parity check codes, Probability, Uncertainty},
pubstate = {published},
tppubtype = {inproceedings}
}
Low-density parity-check (LPDC) decoders assume the channel estate information (CSI) is known and they have the true a posteriori probability (APP) for each transmitted bit. But in most cases of interest, the CSI needs to be estimated with the help of a short training sequence and the LDPC decoder has to decode the received word using faulty APP estimates. In this paper, we study the uncertainty in the CSI estimate and how it affects the bit error rate (BER) output by the LDPC decoder. To improve these APP estimates, we propose a Bayesian equalizer that takes into consideration not only the uncertainty due to the noise in the channel, but also the uncertainty in the CSI estimate, reducing the BER after the LDPC decoder.
Alvarez, Mauricio; Luengo, David; Titsias, Michalis; Lawrence, Neil D
Efficient Multioutput Gaussian Processes Through Variational Inducing Kernels Proceedings Article
En: AISTATS 2010, Sardinia, 2010.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Alvarez2010,
title = {Efficient Multioutput Gaussian Processes Through Variational Inducing Kernels},
author = {Mauricio Alvarez and David Luengo and Michalis Titsias and Neil D Lawrence},
url = {http://eprints.pascal-network.org/archive/00006397/},
year = {2010},
date = {2010-01-01},
booktitle = {AISTATS 2010},
address = {Sardinia},
abstract = {Interest in multioutput kernel methods is increasing, whether under the guise of multitask learning, multisensor networks or structured output data. From the Gaussian process perspective a multioutput Mercer kernel is a covariance function over correlated output functions. One way of constructing such kernels is based on convolution processes (CP). A key problem for this approach is efficient inference. Alvarez and Lawrence recently presented a sparse approximation for CPs that enabled efficient inference. In this paper, we extend this work in two directions: we introduce the concept of variational inducing functions to handle potential non-smooth functions involved in the kernel CP construction and we consider an alternative approach to approximate inference based on variational methods, extending the work by Titsias (2009) to the multiple output case. We demonstrate our approaches on prediction of school marks, compiler performance and financial time series.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Interest in multioutput kernel methods is increasing, whether under the guise of multitask learning, multisensor networks or structured output data. From the Gaussian process perspective a multioutput Mercer kernel is a covariance function over correlated output functions. One way of constructing such kernels is based on convolution processes (CP). A key problem for this approach is efficient inference. Alvarez and Lawrence recently presented a sparse approximation for CPs that enabled efficient inference. In this paper, we extend this work in two directions: we introduce the concept of variational inducing functions to handle potential non-smooth functions involved in the kernel CP construction and we consider an alternative approach to approximate inference based on variational methods, extending the work by Titsias (2009) to the multiple output case. We demonstrate our approaches on prediction of school marks, compiler performance and financial time series.
Plata-Chaves, Jorge; Lazaro, Marcelino
Closed-Form Error Exponent for the Neyman-Pearson Fusion of Two-Dimensional Markov Local Decisions Proceedings Article
En: European Signal Processing Conference (EUSIPCO 2010), Aalborg, 2010.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Plata-Chaves2010,
title = {Closed-Form Error Exponent for the Neyman-Pearson Fusion of Two-Dimensional Markov Local Decisions},
author = {Jorge Plata-Chaves and Marcelino Lazaro},
url = {http://www.eurasip.org/Proceedings/Eusipco/Eusipco2010/Contents/papers/1569292447.pdf},
year = {2010},
date = {2010-01-01},
booktitle = {European Signal Processing Conference (EUSIPCO 2010)},
address = {Aalborg},
abstract = {We consider a distributed detection system formed by a large num- ber of local detectors and a fusion center that performs a Neyman- Pearson fusion of the binary quantizations of the sensor observa- tions. The aforementioned local decisions are taken with no kind of cooperation and transmitted to the fusion center over error free parallel access channels. Furthermore, the devices are located on a rectangular lattice so that sensors belonging to a specific row or column are equally spaced. For each hypothesis H 0 and H 1 , the correlation structure of the local decisions is modelled with a two- dimensional causal field where the rows and columns are outcomes of the same first-order binary Markov chain. Under this scenario, we derive a closed-form error exponent for the Neyman-Pearson fusion of the local decisions. Afterwards, using the derived error exponent we study the effect of different design parameters of the network on its overall detection performance},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider a distributed detection system formed by a large num- ber of local detectors and a fusion center that performs a Neyman- Pearson fusion of the binary quantizations of the sensor observa- tions. The aforementioned local decisions are taken with no kind of cooperation and transmitted to the fusion center over error free parallel access channels. Furthermore, the devices are located on a rectangular lattice so that sensors belonging to a specific row or column are equally spaced. For each hypothesis H 0 and H 1 , the correlation structure of the local decisions is modelled with a two- dimensional causal field where the rows and columns are outcomes of the same first-order binary Markov chain. Under this scenario, we derive a closed-form error exponent for the Neyman-Pearson fusion of the local decisions. Afterwards, using the derived error exponent we study the effect of different design parameters of the network on its overall detection performance
2009
Czink, Nicolai; Bandemer, Bernd; Vazquez-Vilar, Gonzalo; Jalloul, Louay; Oestges, Claude; Paulraj, Arogyaswami
Spatial Separation of Multi-User MIMO Channels Proceedings Article
En: 20th Personal, Indoor and Mobile Radio Communications Symposium 2009 (PIMRC 09), Tokyo, Japan, 2009.
BibTeX | Etiquetas:
@inproceedings{nczink2009,
title = {Spatial Separation of Multi-User MIMO Channels},
author = {Nicolai Czink and Bernd Bandemer and Gonzalo Vazquez-Vilar and Louay Jalloul and Claude Oestges and Arogyaswami Paulraj},
year = {2009},
date = {2009-09-01},
booktitle = {20th Personal, Indoor and Mobile Radio Communications Symposium 2009 (PIMRC 09)},
address = {Tokyo, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Bandemer, Bernd; Vazquez-Vilar, Gonzalo; Gamal, Abbas El
On the Sum Capacity of A Class of Cyclically Symmetric Deterministic Interference Channels Proceedings Article
En: 2009 IEEE International Symposium on Information Theory (ISIT 2009), Coex, Seoul, Korea, 2009.
BibTeX | Etiquetas:
@inproceedings{bbandemer2009,
title = {On the Sum Capacity of A Class of Cyclically Symmetric Deterministic Interference Channels},
author = {Bernd Bandemer and Gonzalo Vazquez-Vilar and Abbas El Gamal},
year = {2009},
date = {2009-06-01},
booktitle = {2009 IEEE International Symposium on Information Theory (ISIT 2009)},
address = {Coex, Seoul, Korea},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
López-Valcarce, Roberto; Vazquez-Vilar, Gonzalo; Álvarez-Díaz, Marcos
Multiantenna detection of multicarrier primary signals exploiting spectral a priori information Proceedings Article
En: 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications (Crowncom 2009), Hannover, Germany, 2009.
BibTeX | Etiquetas:
@inproceedings{crowncom2009,
title = {Multiantenna detection of multicarrier primary signals exploiting spectral a priori information},
author = {Roberto L\'{o}pez-Valcarce and Gonzalo Vazquez-Vilar and Marcos \'{A}lvarez-D\'{i}az},
year = {2009},
date = {2009-06-01},
booktitle = {4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications (Crowncom 2009)},
address = {Hannover, Germany},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
López-Valcarce, Roberto; Vazquez-Vilar, Gonzalo
Wideband Spectrum Sensing in Cognitive Radio: Joint Estimation of Noise Variance and Multiple Signal Levels Proceedings Article
En: 2009 IEEE International Workshop on Signal Processing Advances for Wireless Communications (Spawc 2009), Perugia, Italy, 2009.
BibTeX | Etiquetas:
@inproceedings{spawc2009,
title = {Wideband Spectrum Sensing in Cognitive Radio: Joint Estimation of Noise Variance and Multiple Signal Levels},
author = {Roberto L\'{o}pez-Valcarce and Gonzalo Vazquez-Vilar},
year = {2009},
date = {2009-06-01},
booktitle = {2009 IEEE International Workshop on Signal Processing Advances for Wireless Communications (Spawc 2009)},
address = {Perugia, Italy},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Olmos, Pablo M; Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando
Soft LDPC Decoding in Nonlinear Channels with Gaussian Processes for Classification Proceedings Article
En: European Signal Processing Conference (EUSIPCO), Glasgow, 2009.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Olmos2009,
title = {Soft LDPC Decoding in Nonlinear Channels with Gaussian Processes for Classification},
author = {Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://www.eurasip.org/Proceedings/Eusipco/Eusipco2009/contents/papers/1569186781.pdf},
year = {2009},
date = {2009-01-01},
booktitle = {European Signal Processing Conference (EUSIPCO)},
address = {Glasgow},
abstract = {In this paper, we propose a new approach for nonlinear equalization based on Gaussian processes for classification (GPC).We also measure the performance of the equalizer after a low-density parity-check channel decoder has detected the received sequence. Typically, most channel equalizers concentrate on reducing the bit error rate, instead of providing accurate posterior probability estimates. GPC is a Bayesian nonlinear classification tool that provides accurate posterior probability estimates with short training sequences. We show that the accuracy of these estimates is essential for optimal performance of the channel decoder and that the error rate outputted by the equalizer might be irrelevant to understand the performance of the overall communication receiver. We compare the proposed equalizers with state-ofthe- art solutions.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose a new approach for nonlinear equalization based on Gaussian processes for classification (GPC).We also measure the performance of the equalizer after a low-density parity-check channel decoder has detected the received sequence. Typically, most channel equalizers concentrate on reducing the bit error rate, instead of providing accurate posterior probability estimates. GPC is a Bayesian nonlinear classification tool that provides accurate posterior probability estimates with short training sequences. We show that the accuracy of these estimates is essential for optimal performance of the channel decoder and that the error rate outputted by the equalizer might be irrelevant to understand the performance of the overall communication receiver. We compare the proposed equalizers with state-ofthe- art solutions.
Bravo-Santos, Ángel M; Djuric, Petar M
Cooperative Relay Communications in Mesh Networks Proceedings Article
En: 2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications, pp. 499–503, IEEE, Perugia, 2009, ISBN: 978-1-4244-3695-8.
Resumen | Enlaces | BibTeX | Etiquetas: binary transmission, bit error probability, Bit error rate, cooperative relay communications, decode-and-forward relays, Detectors, error statistics, Maximum likelihood decoding, maximum likelihood detection, Mesh networks, mesh wireless networks, multi-hop networks, Network topology, optimal node decision rules, Peer to peer computing, radio networks, Relays, spread spectrum communication, telecommunication network topology, Wireless Sensor Networks
@inproceedings{Bravo-Santos2009,
title = {Cooperative Relay Communications in Mesh Networks},
author = {\'{A}ngel M Bravo-Santos and Petar M Djuric},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5161835},
isbn = {978-1-4244-3695-8},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications},
pages = {499--503},
publisher = {IEEE},
address = {Perugia},
abstract = {In previous literature on cooperative relay communications, the emphasis has been on the study of multi-hop networks. In this paper we address mesh wireless networks that use decode-and-forward relays for which we derive the optimal node decision rules in case of binary transmission. We also obtain the expression for the overall bit error probability. We compare the mesh networks with multi-hop networks and show the improvement in performance that can be achieved with them when both networks have the same number of nodes and equal number of hops.},
keywords = {binary transmission, bit error probability, Bit error rate, cooperative relay communications, decode-and-forward relays, Detectors, error statistics, Maximum likelihood decoding, maximum likelihood detection, Mesh networks, mesh wireless networks, multi-hop networks, Network topology, optimal node decision rules, Peer to peer computing, radio networks, Relays, spread spectrum communication, telecommunication network topology, Wireless Sensor Networks},
pubstate = {published},
tppubtype = {inproceedings}
}
In previous literature on cooperative relay communications, the emphasis has been on the study of multi-hop networks. In this paper we address mesh wireless networks that use decode-and-forward relays for which we derive the optimal node decision rules in case of binary transmission. We also obtain the expression for the overall bit error probability. We compare the mesh networks with multi-hop networks and show the improvement in performance that can be achieved with them when both networks have the same number of nodes and equal number of hops.
Bugallo, Monica F; Maiz, Cristina S; Miguez, Joaquin; Djuric, Petar M
Cost-Reference Particle Filters and Fusion of Information Proceedings Article
En: 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, pp. 286–291, IEEE, Marco Island, FL, 2009.
Resumen | Enlaces | BibTeX | Etiquetas: costs, distributed processing, Electronic mail, fusion, Information filtering, Information filters, information fusion, Measurement standards, probabilistic information, random measures, sensor fusion, smoothing methods, Weight measurement
@inproceedings{Bugallo2009,
title = {Cost-Reference Particle Filters and Fusion of Information},
author = {Monica F Bugallo and Cristina S Maiz and Joaquin Miguez and Petar M Djuric},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4785936},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop},
pages = {286--291},
publisher = {IEEE},
address = {Marco Island, FL},
abstract = {Cost-reference particle filtering is a methodology for tracking unknowns in a system without reliance on probabilistic information about the noises in the system. The methodology is based on analogous principles as the ones of standard particle filtering. Unlike the random measures of standard particle filters that are composed of particles and weights, the random measures of cost-reference particle filters contain particles and user-defined costs. In this paper, we discuss a few scenarios where we need to meld random measures of two or more cost-reference particle filters. The objective is to obtain a fused random measure that combines the information from the individual cost-reference particle filters.},
keywords = {costs, distributed processing, Electronic mail, fusion, Information filtering, Information filters, information fusion, Measurement standards, probabilistic information, random measures, sensor fusion, smoothing methods, Weight measurement},
pubstate = {published},
tppubtype = {inproceedings}
}
Cost-reference particle filtering is a methodology for tracking unknowns in a system without reliance on probabilistic information about the noises in the system. The methodology is based on analogous principles as the ones of standard particle filtering. Unlike the random measures of standard particle filters that are composed of particles and weights, the random measures of cost-reference particle filters contain particles and user-defined costs. In this paper, we discuss a few scenarios where we need to meld random measures of two or more cost-reference particle filters. The objective is to obtain a fused random measure that combines the information from the individual cost-reference particle filters.
Djuric, Petar M; Miguez, Joaquin
Model Assessment with Kolmogorov-Smirnov Statistics Proceedings Article
En: 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2973–2976, IEEE, Taipei, 2009, ISSN: 1520-6149.
Resumen | Enlaces | BibTeX | Etiquetas: Bayesian methods, Computer Simulation, Context modeling, Electronic mail, Filtering, ill-conditioned problem, Kolmogorov-Smirnov statistics, model assessment, modelling, Predictive models, Probability, statistical analysis, statistics, Testing
@inproceedings{Djuric2009,
title = {Model Assessment with Kolmogorov-Smirnov Statistics},
author = {Petar M Djuric and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4960248},
issn = {1520-6149},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE International Conference on Acoustics, Speech and Signal Processing},
pages = {2973--2976},
publisher = {IEEE},
address = {Taipei},
abstract = {One of the most basic problems in science and engineering is the assessment of a considered model. The model should describe a set of observed data and the objective is to find ways of deciding if the model should be rejected. It seems that this is an ill-conditioned problem because we have to test the model against all the possible alternative models. In this paper we use the Kolmogorov-Smirnov statistic to develop a test that shows if the model should be kept or it should be rejected. We explain how this testing can be implemented in the context of particle filtering. We demonstrate the performance of the proposed method by computer simulations.},
keywords = {Bayesian methods, Computer Simulation, Context modeling, Electronic mail, Filtering, ill-conditioned problem, Kolmogorov-Smirnov statistics, model assessment, modelling, Predictive models, Probability, statistical analysis, statistics, Testing},
pubstate = {published},
tppubtype = {inproceedings}
}
One of the most basic problems in science and engineering is the assessment of a considered model. The model should describe a set of observed data and the objective is to find ways of deciding if the model should be rejected. It seems that this is an ill-conditioned problem because we have to test the model against all the possible alternative models. In this paper we use the Kolmogorov-Smirnov statistic to develop a test that shows if the model should be kept or it should be rejected. We explain how this testing can be implemented in the context of particle filtering. We demonstrate the performance of the proposed method by computer simulations.
Maiz, Cristina S; Miguez, Joaquin; Djuric, Petar M
Particle Filtering in the Presence of Outliers Proceedings Article
En: 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, pp. 33–36, IEEE, Cardiff, 2009, ISBN: 978-1-4244-2709-3.
Resumen | Enlaces | BibTeX | Etiquetas: computer simulations, Degradation, Filtering, multidimensional random variates, Multidimensional signal processing, Multidimensional systems, Nonlinear tracking, Outlier detection, predictive distributions, Signal processing, signal processing tools, signal-power observations, spatial depth, statistical analysis, statistical distributions, statistics, Target tracking, Testing
@inproceedings{Maiz2009,
title = {Particle Filtering in the Presence of Outliers},
author = {Cristina S Maiz and Joaquin Miguez and Petar M Djuric},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5278645},
isbn = {978-1-4244-2709-3},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE/SP 15th Workshop on Statistical Signal Processing},
pages = {33--36},
publisher = {IEEE},
address = {Cardiff},
abstract = {Particle filters have become very popular signal processing tools for problems that involve nonlinear tracking of an unobserved signal of interest given a series of related observations. In this paper we propose a new scheme for particle filtering when the observed data are possibly contaminated with outliers. An outlier is an observation that has been generated by some (unknown) mechanism different from the assumed model of the data. Therefore, when handled in the same way as regular observations, outliers may drastically degrade the performance of the particle filter. To address this problem, we introduce an auxiliary particle filtering scheme that incorporates an outlier detection step. We propose to implement it by means of a test involving statistics of the predictive distributions of the observations. Specifically, we investigate the use of a proposed statistic called spatial depth that can easily be applied to multidimensional random variates. The performance of the resulting algorithm is assessed by computer simulations of target tracking based on signal-power observations.},
keywords = {computer simulations, Degradation, Filtering, multidimensional random variates, Multidimensional signal processing, Multidimensional systems, Nonlinear tracking, Outlier detection, predictive distributions, Signal processing, signal processing tools, signal-power observations, spatial depth, statistical analysis, statistical distributions, statistics, Target tracking, Testing},
pubstate = {published},
tppubtype = {inproceedings}
}
Particle filters have become very popular signal processing tools for problems that involve nonlinear tracking of an unobserved signal of interest given a series of related observations. In this paper we propose a new scheme for particle filtering when the observed data are possibly contaminated with outliers. An outlier is an observation that has been generated by some (unknown) mechanism different from the assumed model of the data. Therefore, when handled in the same way as regular observations, outliers may drastically degrade the performance of the particle filter. To address this problem, we introduce an auxiliary particle filtering scheme that incorporates an outlier detection step. We propose to implement it by means of a test involving statistics of the predictive distributions of the observations. Specifically, we investigate the use of a proposed statistic called spatial depth that can easily be applied to multidimensional random variates. The performance of the resulting algorithm is assessed by computer simulations of target tracking based on signal-power observations.
Martino, Luca; Miguez, Joaquin
A Novel Rejection Sampling Scheme for Posterior Probability Distributions Proceedings Article
En: 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2921–2924, IEEE, Taipei, 2009, ISSN: 1520-6149.
Resumen | Enlaces | BibTeX | Etiquetas: Additive noise, arbitrary target probability distributions, Bayes methods, Bayesian methods, Monte Carlo integration, Monte Carlo methods, Monte Carlo techniques, Overbounding, posterior probability distributions, Probability density function, Probability distribution, Proposals, Rejection sampling, rejection sampling scheme, Sampling methods, Signal processing algorithms, signal sampling, Upper bound
@inproceedings{Martino2009,
title = {A Novel Rejection Sampling Scheme for Posterior Probability Distributions},
author = {Luca Martino and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4960235},
issn = {1520-6149},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE International Conference on Acoustics, Speech and Signal Processing},
pages = {2921--2924},
publisher = {IEEE},
address = {Taipei},
abstract = {Rejection sampling (RS) is a well-known method to draw from arbitrary target probability distributions, which has important applications by itself or as a building block for more sophisticated Monte Carlo techniques. The main limitation to the use of RS is the need to find an adequate upper bound for the ratio of the target probability density function (pdf) over the proposal pdf from which the samples are generated. There are no general methods to analytically find this bound, except in the particular case in which the target pdf is log-concave. In this paper we adopt a Bayesian view of the problem and propose a general RS scheme to draw from the posterior pdf of a signal of interest using its prior density as a proposal function. The method enables the analytical calculation of the bound and can be applied to a large class of target densities. We illustrate its use with a simple numerical example.},
keywords = {Additive noise, arbitrary target probability distributions, Bayes methods, Bayesian methods, Monte Carlo integration, Monte Carlo methods, Monte Carlo techniques, Overbounding, posterior probability distributions, Probability density function, Probability distribution, Proposals, Rejection sampling, rejection sampling scheme, Sampling methods, Signal processing algorithms, signal sampling, Upper bound},
pubstate = {published},
tppubtype = {inproceedings}
}
Rejection sampling (RS) is a well-known method to draw from arbitrary target probability distributions, which has important applications by itself or as a building block for more sophisticated Monte Carlo techniques. The main limitation to the use of RS is the need to find an adequate upper bound for the ratio of the target probability density function (pdf) over the proposal pdf from which the samples are generated. There are no general methods to analytically find this bound, except in the particular case in which the target pdf is log-concave. In this paper we adopt a Bayesian view of the problem and propose a general RS scheme to draw from the posterior pdf of a signal of interest using its prior density as a proposal function. The method enables the analytical calculation of the bound and can be applied to a large class of target densities. We illustrate its use with a simple numerical example.
Achutegui, Katrin; Martino, Luca; Rodas, Javier; Escudero, Carlos J; Miguez, Joaquin
A Multi-Model Particle Filtering Algorithm for Indoor Tracking of Mobile Terminals Using RSS Data Proceedings Article
En: 2009 IEEE International Conference on Control Applications, pp. 1702–1707, IEEE, Saint Petersburg, 2009, ISBN: 978-1-4244-4601-8.
Resumen | Enlaces | BibTeX | Etiquetas: Bayesian methods, Control systems, Filtering algorithms, generalized interacting multiple model, GIMM, indoor radio, Indoor tracking, mobile radio, mobile terminal, Monte Carlo methods, multipath propagation, position-dependent data measurement, random process, random processes, Rao-Blackwellized sequential Monte Carlo tracking, received signal strength, RSS data, Sliding mode control, State-space methods, state-space model, Target tracking, tracking, transmitter-to-receiver distance, wireless network, wireless technology
@inproceedings{Achutegui2009,
title = {A Multi-Model Particle Filtering Algorithm for Indoor Tracking of Mobile Terminals Using RSS Data},
author = {Katrin Achutegui and Luca Martino and Javier Rodas and Carlos J Escudero and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5280960},
isbn = {978-1-4244-4601-8},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE International Conference on Control Applications},
pages = {1702--1707},
publisher = {IEEE},
address = {Saint Petersburg},
abstract = {In this paper we address the problem of indoor tracking using received signal strength (RSS) as a position-dependent data measurement. This type of measurements is very appealing because they can be easily obtained with a variety of wireless technologies which are relatively inexpensive. The extraction of accurate location information from RSS in indoor scenarios is not an easy task, though. Since RSS is highly influenced by multipath propagation, it turns out very hard to adequately model the correspondence between the received power and the transmitter-to-receiver distance. The measurement models proposed in the literature are site-specific and require a great deal of information regarding the structure of the building where the tracking will be performed and therefore are not useful for a general application. For that reason we propose the use of a compound model that combines several sub-models, whose parameters are adjusted to specific and different propagation environments. This methodology, is called interacting multiple models (IMM), has been used in the past for modeling the motion of maneuvering targets. Here, we extend its application to handle also the uncertainty in the RSS observations and we refer to the resulting state-space model as a generalized IMM (GIMM) system. The flexibility of the GIMM approach is attained at the expense of an increase in the number of random processes that must be accurately tracked. To overcome this difficulty, we introduce a Rao-Blackwellized sequential Monte Carlo tracking algorithm that exhibits good performance both with synthetic and experimental data.},
keywords = {Bayesian methods, Control systems, Filtering algorithms, generalized interacting multiple model, GIMM, indoor radio, Indoor tracking, mobile radio, mobile terminal, Monte Carlo methods, multipath propagation, position-dependent data measurement, random process, random processes, Rao-Blackwellized sequential Monte Carlo tracking, received signal strength, RSS data, Sliding mode control, State-space methods, state-space model, Target tracking, tracking, transmitter-to-receiver distance, wireless network, wireless technology},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we address the problem of indoor tracking using received signal strength (RSS) as a position-dependent data measurement. This type of measurements is very appealing because they can be easily obtained with a variety of wireless technologies which are relatively inexpensive. The extraction of accurate location information from RSS in indoor scenarios is not an easy task, though. Since RSS is highly influenced by multipath propagation, it turns out very hard to adequately model the correspondence between the received power and the transmitter-to-receiver distance. The measurement models proposed in the literature are site-specific and require a great deal of information regarding the structure of the building where the tracking will be performed and therefore are not useful for a general application. For that reason we propose the use of a compound model that combines several sub-models, whose parameters are adjusted to specific and different propagation environments. This methodology, is called interacting multiple models (IMM), has been used in the past for modeling the motion of maneuvering targets. Here, we extend its application to handle also the uncertainty in the RSS observations and we refer to the resulting state-space model as a generalized IMM (GIMM) system. The flexibility of the GIMM approach is attained at the expense of an increase in the number of random processes that must be accurately tracked. To overcome this difficulty, we introduce a Rao-Blackwellized sequential Monte Carlo tracking algorithm that exhibits good performance both with synthetic and experimental data.
Djuric, Petar M; Bugallo, Monica F; Closas, Pau; Miguez, Joaquin
Measuring the Robustness of Sequential Methods Proceedings Article
En: 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, pp. 29–32, IEEE, Aruba, Dutch Antilles, 2009, ISBN: 978-1-4244-5179-1.
Resumen | Enlaces | BibTeX | Etiquetas: Additive noise, cumulative distribution functions, data processing method, extended Kalman filtering, Extraterrestrial measurements, Filtering, Gaussian distribution, Gaussian noise, Kalman filters, Kolmogorov-Smirnov distance, Least squares approximation, Noise robustness, nonlinear filters, robustness, sequential methods, statistical distributions, telecommunication computing
@inproceedings{Djuric2009a,
title = {Measuring the Robustness of Sequential Methods},
author = {Petar M Djuric and Monica F Bugallo and Pau Closas and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5413275},
isbn = {978-1-4244-5179-1},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop},
pages = {29--32},
publisher = {IEEE},
address = {Aruba, Dutch Antilles},
abstract = {Whenever we apply methods for processing data, we make a number of model assumptions. In reality, these assumptions are not always correct. Robust methods can withstand model inaccuracies, that is, despite some incorrect assumptions they can still produce good results. We often want to know how robust employed methods are. To that end we need to have a yardstick for measuring robustness. In this paper, we propose an approach for constructing such metrics for sequential methods. These metrics are derived from the Kolmogorov-Smirnov distance between the cumulative distribution functions of the actual observations and the ones based on the assumed model. The use of the proposed metrics is demonstrated with simulation examples.},
keywords = {Additive noise, cumulative distribution functions, data processing method, extended Kalman filtering, Extraterrestrial measurements, Filtering, Gaussian distribution, Gaussian noise, Kalman filters, Kolmogorov-Smirnov distance, Least squares approximation, Noise robustness, nonlinear filters, robustness, sequential methods, statistical distributions, telecommunication computing},
pubstate = {published},
tppubtype = {inproceedings}
}
Whenever we apply methods for processing data, we make a number of model assumptions. In reality, these assumptions are not always correct. Robust methods can withstand model inaccuracies, that is, despite some incorrect assumptions they can still produce good results. We often want to know how robust employed methods are. To that end we need to have a yardstick for measuring robustness. In this paper, we propose an approach for constructing such metrics for sequential methods. These metrics are derived from the Kolmogorov-Smirnov distance between the cumulative distribution functions of the actual observations and the ones based on the assumed model. The use of the proposed metrics is demonstrated with simulation examples.
Martino, Luca; Miguez, Joaquin
New Accept/Reject Methods for Independent Sampling from Posterior Probability Distributions Proceedings Article
En: 17th European Signal Processing Conference (EUSIPCO 2009), Glasgow, 2009.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Martino2009a,
title = {New Accept/Reject Methods for Independent Sampling from Posterior Probability Distributions},
author = {Luca Martino and Joaquin Miguez},
url = {http://www.academia.edu/2355641/NEW_ACCEPT_REJECT_METHODS_FOR_INDEPENDENT_SAMPLING_FROM_POSTERIOR_PROBABILITY_DISTRIBUTIONS},
year = {2009},
date = {2009-01-01},
booktitle = {17th European Signal Processing Conference (EUSIPCO 2009)},
address = {Glasgow},
abstract = {Rejection sampling (RS) is a well-known method to generate(pseudo-)random samples from arbitrary probability distributionsthat enjoys important applications, either by itself or as a tool inmore sophisticated Monte Carlo techniques. Unfortunately, the useof RS techniques demands the calculation of tight upper bounds forthe ratio of the target probability density function (pdf) over theproposal density from which candidate samples are drawn. Exceptfor the class of log-concave target pdf’s, for which an efficientalgorithm exists, there are no general methods to analyticallydetermine this bound, which has to be derived from scratch foreach specific case. In this paper, we tackle the general problemof applying RS to draw from an arbitrary posterior pdf using theprior density as a proposal function. This is a scenario that appearsfrequently in Bayesian signal processing methods. We derive ageneral geometric procedure for the calculation of upper boundsthat can be used with a broad class of target pdf’s, includingscenarios with correlated observations, multimodal and/or mixturemeasurement noises. We provide some simple numerical examplesto illustrate the application of the proposed techniques},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Rejection sampling (RS) is a well-known method to generate(pseudo-)random samples from arbitrary probability distributionsthat enjoys important applications, either by itself or as a tool inmore sophisticated Monte Carlo techniques. Unfortunately, the useof RS techniques demands the calculation of tight upper bounds forthe ratio of the target probability density function (pdf) over theproposal density from which candidate samples are drawn. Exceptfor the class of log-concave target pdf’s, for which an efficientalgorithm exists, there are no general methods to analyticallydetermine this bound, which has to be derived from scratch foreach specific case. In this paper, we tackle the general problemof applying RS to draw from an arbitrary posterior pdf using theprior density as a proposal function. This is a scenario that appearsfrequently in Bayesian signal processing methods. We derive ageneral geometric procedure for the calculation of upper boundsthat can be used with a broad class of target pdf’s, includingscenarios with correlated observations, multimodal and/or mixturemeasurement noises. We provide some simple numerical examplesto illustrate the application of the proposed techniques
Perez-Cruz, Fernando; Kulkarni, S R
Distributed Least Square for Consensus Building in Sensor Networks Proceedings Article
En: 2009 IEEE International Symposium on Information Theory, pp. 2877–2881, IEEE, Seoul, 2009, ISBN: 978-1-4244-4312-3.
Resumen | Enlaces | BibTeX | Etiquetas: Change detection algorithms, Channel Coding, Distributed computing, distributed least square method, graphical models, Inference algorithms, Kernel, Least squares methods, nonparametric statistics, Parametric statistics, robustness, sensor-network learning, statistical analysis, Telecommunication network reliability, Wireless sensor network, Wireless Sensor Networks
@inproceedings{Perez-Cruz2009,
title = {Distributed Least Square for Consensus Building in Sensor Networks},
author = {Fernando Perez-Cruz and S R Kulkarni},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5205336},
isbn = {978-1-4244-4312-3},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE International Symposium on Information Theory},
pages = {2877--2881},
publisher = {IEEE},
address = {Seoul},
abstract = {We present a novel mechanism for consensus building in sensor networks. The proposed algorithm has three main properties that make it suitable for general sensor-network learning. First, the proposed algorithm is based on robust nonparametric statistics and thereby needs little prior knowledge about the network and the function that needs to be estimated. Second, the algorithm uses only local information about the network and it communicates only with nearby sensors. Third, the algorithm is completely asynchronous and robust. It does not need to coordinate the sensors to estimate the underlying function and it is not affected if other sensors in the network stop working. Therefore, the proposed algorithm is an ideal candidate for sensor networks deployed in remote and inaccessible areas, which might need to change their objective once they have been set up.},
keywords = {Change detection algorithms, Channel Coding, Distributed computing, distributed least square method, graphical models, Inference algorithms, Kernel, Least squares methods, nonparametric statistics, Parametric statistics, robustness, sensor-network learning, statistical analysis, Telecommunication network reliability, Wireless sensor network, Wireless Sensor Networks},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a novel mechanism for consensus building in sensor networks. The proposed algorithm has three main properties that make it suitable for general sensor-network learning. First, the proposed algorithm is based on robust nonparametric statistics and thereby needs little prior knowledge about the network and the function that needs to be estimated. Second, the algorithm uses only local information about the network and it communicates only with nearby sensors. Third, the algorithm is completely asynchronous and robust. It does not need to coordinate the sensors to estimate the underlying function and it is not affected if other sensors in the network stop working. Therefore, the proposed algorithm is an ideal candidate for sensor networks deployed in remote and inaccessible areas, which might need to change their objective once they have been set up.