@inproceedings{Campo2012,

title = {Random Coding Bounds that Attain the Joint Source-Channel Exponent},

author = {Adria Tauste Campo and Gonzalo Vazquez-Vilar and Albert Guillen i Fàbregas and Tobias Koch and Alfonso Martinez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6310910},

isbn = {978-1-4673-3140-1},

year  = {2012},

date = {2012-01-01},

booktitle = {2012 46th Annual Conference on Information Sciences and Systems (CISS)},

pages = {1--5},

publisher = {IEEE},

address = {Princeton},

abstract = {This paper presents a random-coding upper bound on the average error probability of joint source-channel coding that attains Csiszár's error exponent. The bound is based on a code construction for which source messages are assigned to disjoint subsets (classes), and codewords are generated according to a distribution that depends on the class of the source message. For a single class, the bound recovers Gallager's exponent; identifying the classes with source type classes, it recovers Csiszár's exponent. Moreover, it is shown that as a two appropriately designed classes are sufficient to attain Csiszár's exponent.},

keywords = {code construction, combined source-channel coding, Csiszár error exponent, Ducts, error probability, error statistics, Gallager exponent, joint source-channel coding, joint source-channel exponent, random codes, random-coding upper bound, Yttrium},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Campo2012a,

title = {Achieving Csiszár's Source-Channel Coding Exponent with Product Distributions},

author = {Adria Tauste Campo and Gonzalo Vazquez-Vilar and Albert Guillen i Fabregas and Tobias Koch and Alfonso Martinez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6283524},

issn = {2157-8095},

year  = {2012},

date = {2012-01-01},

booktitle = {2012 IEEE International Symposium on Information Theory Proceedings},

pages = {1548--1552},

publisher = {IEEE},

address = {Cambridge, MA},

abstract = {We derive a random-coding upper bound on the average probability of error of joint source-channel coding that recovers Csiszár's error exponent when used with product distributions over the channel inputs. Our proof technique for the error probability analysis employs a code construction for which source messages are assigned to subsets and codewords are generated with a distribution that depends on the subset.},

keywords = {average probability of error, Channel Coding, code construction, codewords, Csiszár's source-channel coding, Decoding, Encoding, error probability, error statistics, Joints, Manganese, product distributions, random codes, random-coding upper bound, source coding, source messages, Upper bound},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Taborda2012a,

title = {Mutual Information and Relative Entropy over the Binomial and Negative Binomial Channels},

author = {Camilo G Taborda and Fernando Perez-Cruz},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6284304},

issn = {2157-8095},

year  = {2012},

date = {2012-01-01},

booktitle = {2012 IEEE International Symposium on Information Theory Proceedings},

pages = {696--700},

publisher = {IEEE},

address = {Cambridge, MA},

abstract = {We study the relation of the mutual information and relative entropy over the Binomial and Negative Binomial channels with estimation theoretical quantities, in which we extend already known results for Gaussian and Poisson channels. We establish general expressions for these information theory concepts with a direct connection with estimation theory through the conditional mean estimation and a particular loss function.},

keywords = {Channel estimation, conditional mean estimation, Entropy, Estimation, estimation theoretical quantity, estimation theory, Gaussian channel, Gaussian channels, information theory concept, loss function, mean square error methods, Mutual information, negative binomial channel, Poisson channel, Random variables, relative entropy},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Salamanca2012a,

title = {Tree-Structured Expectation Propagation for LDPC Decoding in AWGN Channels},

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

url = {http://www.researchgate.net/publication/236006591_Tree-structured_expectation_propagation_for_LDPC_decoding_in_AWGN_channels},

year  = {2012},

date = {2012-01-01},

booktitle = {Proceeding of: Information Theory and Applications Workshop (ITA)},

address = {San Diego},

abstract = {In this paper, we propose the tree-structured expectation propagation (TEP) algorithm for low-density parity-check (LDPC) decoding over the additive white Gaussian noise (AWGN) channel. By imposing a tree-like approximation over the graphical model of the code, this algorithm introduces pairwise marginal constraints over pairs of variables, which provide joint information of the variables related. Thanks to this, the proposed TEP decoder improves the performance of the standard belief propagation (BP) solution. An efficient way of constructing the tree-like structure is also described. The simulation results illustrate the TEP decoder gain in the finite-length regime, compared to the standard BP solution. For code lengths shorter than n = 512, the gain in the waterfall region achieves up to 0.25 dB. We also notice a remarkable reduction of the error floo},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Henao-Mazo2012,

title = {Finding Diverse Shortest Paths for the Routing Task in Wireless Sensor Networks},

author = {W Henao-Mazo and Ángel M Bravo-Santos},

url = {http://www.iaria.org/conferences2012/ProgramICSNC12.html},

year  = {2012},

date = {2012-01-01},

booktitle = {ICSNC 2012. The Seventh International Conference on Systems and Networks Communications},

address = {Lisboa},

abstract = {Wireless Sensor Networks are deployed with the idea of collecting field information of different variables like temperature, position, humidity, etc., from several resourceconstrained sensor nodes, and then relay those data to a sink node or base station. Therefore, the path finding for routing must be carried out with strategies that make it possible to manage efficiently the network limited resources, whilst at the same time the network throughput is kept within appreciable levels. Many routing schemes search for one path, with low power dissipation that may not be convenient to increase the network lifetime and long-term connectivity. In an attempt to overcome such eventualities, we proposed a scenario for relaying that uses multiple diverse paths obtained considering the links among network nodes, that could provide reliable data transmission. When data is transmitted across various diverse paths in the network that offer low retransmission rates, the battery demand can be decreased and network lifetime is extended. We show, by using simulations, that the reliability in packets reception and the power dissipation that our scheme offers compare favourably with similar literature implementations.},

keywords = {Diverse Paths., K Shortest, Paths, Wireless Sensor Networks},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Perez-Cruz2012a,

title = {Coding and Approximate Inference},

author = {Fernando Perez-Cruz},

url = {http://mlss2012.tsc.uc3m.es/},

year  = {2012},

date = {2012-01-01},

booktitle = {Machine Learning Summer School (MLSS)},

address = {La Palma},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Read2012b,

title = {Batch-Incremental versus Instance-Incremental Learning in Dynamic and Evolving Data},

author = {Jesse Read and Albert Bifet and Bernhard Pfahringer and Geoff Holmes},

year  = {2012},

date = {2012-01-01},

booktitle = {The Eleventh International Symposium on Intelligent Data Analysis (IDA 2012).},

address = {Helsinki},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{cogart2011,

title = {Spatial rank estimation in Cognitive Radio Networks with uncalibrated multiple antennas},

author = {Gonzalo Vazquez-Vilar and David Ramirez and Roberto López-Valcarce and Javier Via and Ignacio Santamaria},

year  = {2011},

date = {2011-10-01},

booktitle = {4th International Conference on Cognitive Radio and Advanced Spectrum Management (CogART 2011)},

address = {Barcelona, Spain},

note = {Invited},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{isit2011,

title = {Random-Coding Joint Source-Channel Bounds},

author = {Adria Tauste Campo and Gonzalo Vazquez-Vilar and Albert Guillén i Fàbregas and Alfonso Martinez},

year  = {2011},

date = {2011-07-01},

booktitle = {2011 IEEE International Symposium on Information Theory (ISIT 2011)},

address = {Saint Petersburg, Russia},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{iccasp2011a,

title = {Detection diversity of multiantenna spectrum sensors},

author = {Gonzalo Vazquez-Vilar and Roberto López-Valcarce and Ashish Pandharipande},

year  = {2011},

date = {2011-05-01},

booktitle = {2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)},

address = {Prague, Czech Republic},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{iccasp2011b,

title = {Multiantenna Detection under Noise uncertainty and primary user's spatial structure},

author = {David Ramirez and Gonzalo Vazquez-Vilar and Roberto López-Valcarce and Javier Via and Ignacio Santamaria},

year  = {2011},

date = {2011-05-01},

booktitle = {2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)},

address = {Prague, Czech Republic},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Salamanca2011,

title = {Reduced Complexity MAP Decoder for LDPC Codes over the BEC Using Tree-Structure Expectation Propagation},

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

url = {http://www.researchgate.net/publication/236006584_Reduced_Complexity_MAP_decoder_for_LDPC_codes_over_the_BEC_using_Tree-Structure_Expectation_Propagation},

year  = {2011},

date = {2011-01-01},

booktitle = {Information Theory and Applications (ITA)},

address = {San Diego},

abstract = {In this paper, we propose an algorithm that achieves the MAP solution to decode LDPC codes over the binary erasure channel (BEC). This algorithm, denoted as generalized tree-structured expectation propagation (GTEP), extends the idea of our previous work: the TEP decoder. Both proposals borrow from the tree-structured expectation propagation algorithm, which imposes a tree-like approximation over the original graphical model. However, whereas the TEP decoder only considers up to degree two check nodes, the proposed GTEP modifies the graph by eliminating a check node of any degree and merging this information with the remaining graph. The decoder builds a tree graph of relations between the erased variable nodes with respect to some parent variables. The GTEP algorithm 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 decoder can look for checks nodes of minimum degree to be eliminated first, optimizing the complexity of the decoder. Furthermore, this procedure yields an upper bound for the complexity of the MAP decoder. We include an analysis of the computational complexity of this novel decoder to show that it is a function of the erasure value of the channel, the length of the codeword and the ensemble of the code. We illustrate the proposed algorithm with regular codes, which do not present error floors and achieve capacity when the number of ones per column increases.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Balasingam2011,

title = {Efficient Distributed Resampling for Particle Filters},

author = {Balakumar Balasingam and Miodrag Bolic and Petar M Djuric and Joaquin Miguez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5947172},

issn = {1520-6149},

year  = {2011},

date = {2011-01-01},

booktitle = {2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

pages = {3772--3775},

publisher = {IEEE},

address = {Prague},

abstract = {In particle filtering, resampling is the only step that cannot be fully parallelized. Recently, we have proposed algorithms for distributed resampling implemented on architectures with concurrent processing elements (PEs). The objective of distributed resampling is to reduce the communication among the PEs while not compromising the performance of the particle filter. An additional objective for implementation is to reduce the communication among the PEs. In this paper, we report an improved version of the distributed resampling algorithm that optimally selects the particles for communication between the PEs of the distributed scheme. Computer simulations are provided that demonstrate the improved performance of the proposed algorithm.},

keywords = {Approximation algorithms, Copper, Covariance matrix, distributed resampling, Markov processes, Probability density function, Sequential Monte-Carlo methods, Signal processing, Signal processing algorithms},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Ruiz2011,

title = {Zero-Error Codes for the Noisy-Typewriter Channel},

author = {Francisco J R Ruiz and Fernando Perez-Cruz},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6089510},

isbn = {978-1-4577-0437-6},

year  = {2011},

date = {2011-01-01},

booktitle = {2011 IEEE Information Theory Workshop},

pages = {495--497},

publisher = {IEEE},

address = {Paraty},

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 2n + 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 = {channel capacity, Channel Coding, Equations, Linear code, Noise measurement, noisy-typewriter channel, nontrivial codes, nonzero zero-error rate, odd-letter noisy-typewriter channels, Upper bound, Vectors, zero-error capacity, zero-error codes},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Koch2011,

title = {Asymmetric Quantizers are Better at Low SNR},

author = {Tobias Koch and Amos Lapidoth},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6034037},

issn = {2157-8095},

year  = {2011},

date = {2011-01-01},

booktitle = {2011 IEEE International Symposium on Information Theory Proceedings},

pages = {2592--2596},

publisher = {IEEE},

address = {St. Petersburg},

abstract = {We study the behavior of channel capacity when a one-bit quantizer is employed at the output of the discrete-time average-power-limited Gaussian channel. We focus on the low signal-to-noise ratio regime, where communication at very low spectral efficiencies takes place, as in Spread-Spectrum and Ultra-Wideband communications. It is well known that, in this regime, a symmetric one-bit quantizer reduces capacity by 2/$pi$, which translates to a power loss of approximately two decibels. Here we show that if an asymmetric one-bit quantizer is employed, and if asymmetric signal constellations are used, then these two decibels can be recovered in full.},

keywords = {asymmetric one-bit quantizer, asymmetric signal constellations, channel capacity, Channel Coding, Constellation diagram, Decoding, discrete-time average-power-limited Gaussian chann, Gaussian channels, quantization, Signal to noise ratio, signal-to-noise ratio, SNR, spread spectrum communication, spread-spectrum communications, ultra wideband communication, ultrawideband communications, Upper bound},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@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}

}

@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}

}

@inproceedings{Leiva-Murillo2011,

title = {Visualization and Prediction of Disease Interactions with Continuous-Time Hidden Markov Models},

author = {Jose M Leiva-Murillo and Antonio Artés-Rodríguez and Enrique Baca-García},

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}

}

@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}

}

@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}

}

@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 Guillén i Fàbregas},

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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@inproceedings{Shan2011,

title = {Fast Background Elimination in Fluorescence Microbiology Images: Comparison of Four Algorithms},

author = {Gong Shan and Antonio Artés-Rodríguez},

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 = {Corfú},

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}

}

@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}

}

@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}

}

@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}

}

@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 Artés-Rodríguez},

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}

}

@inproceedings{cip2010,

title = {Multiantenna spectrum sensing for Cognitive Radio: overcoming noise uncertainty},

author = {Roberto López-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}

}

@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}

}

@inproceedings{icassp2010,

title = {Wideband Spectral Estimation from Compressed Measurements Exploiting Spectral a priori Information in Cognitive Radio Systems},

author = {Gonzalo Vazquez-Vilar and Roberto López-Valcarce and Carlos Mosquera and Nuria González-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}

}

@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}

}

@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}

}

@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}

}

@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 Artés-Rodríguez 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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@inproceedings{Vinuelas-Peris2010,

title = {Bayesian Joint Recovery of Correlated Signals in Distributed Compressed Sensing},

author = {Pablo Vinuelas-Peris and Antonio Artés-Rodríguez},

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}

}

@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}

}

@inproceedings{Helander2010,

title = {Maximum a Posteriori Voice Conversion Using Sequential Monte Carlo Methods},

author = {E Helander and H Silén 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}

}

@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}

}

@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}

}

@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}

}