@inproceedings{Read2013,

title = {Eficient Monte Carlo Optimization for Multi-Label Classifier Chains},

author = {Jesse Read and Luca Martino and David Luengo},

year  = {2013},

date = {2013-01-01},

booktitle = {ICASSP 2013: The 38th International Conference on Acoustics, Speech, and Signal Processing},

address = {Vancouver},

abstract = {Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest- performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for nding a good chain sequence and performing ecient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.},

keywords = {Bayesian inference, Classifier chains, Monte Carlo methods, Multi-dimensional classification, Multi-label classification},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Luengo2013,

title = {Cross-Products LASSO},

author = {David Luengo and Javier Via and Sandra Monzon and Tom Trigano and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6638840},

issn = {1520-6149},

year  = {2013},

date = {2013-01-01},

booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing},

pages = {6118--6122},

publisher = {IEEE},

address = {Vancouver},

abstract = {Negative co-occurrence is a common phenomenon in many signal processing applications. In some cases the signals involved are sparse, and this information can be exploited to recover them. In this paper, we present a sparse learning approach that explicitly takes into account negative co-occurrence. This is achieved by adding a novel penalty term to the LASSO cost function based on the cross-products between the reconstruction coefficients. Although the resulting optimization problem is non-convex, we develop a new and efficient method for solving it based on successive convex approximations. Results on synthetic data, for both complete and overcomplete dictionaries, are provided to validate the proposed approach.},

keywords = {Approximation methods, approximation theory, concave programming, convex programming, Cost function, cross-product LASSO cost function, Dictionaries, dictionary, Encoding, LASSO, learning (artificial intelligence), negative co-occurrence, negative cooccurrence phenomenon, nonconvex optimization problem, Signal processing, signal processing application, signal reconstruction, sparse coding, sparse learning approach, Sparse matrices, sparsity-aware learning, successive convex approximation, Vectors},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Olmos2013a,

title = {A Closed-Form Scaling Law for Convolutional LDPC Codes Over the BEC},

author = {Pablo M Olmos and Rudiger Urbanke},

url = {http://itw2013.tsc.uc3m.es/authors},

year  = {2013},

date = {2013-01-01},

booktitle = {2013 IEEE Information Theory Workshop},

address = {Seville},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Vazquez2013,

title = {Automated Detection of Paroxysmal Gamma Waves in Meditation EEG},

author = {Manuel A Vazquez and Jing Jin and Justin Dauwels and Francois B Vialatte},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6637839},

issn = {1520-6149},

year  = {2013},

date = {2013-01-01},

booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing},

pages = {1192--1196},

publisher = {IEEE},

address = {Vancouver},

abstract = {Meditation is a fascinating topic, yet has received limited attention in the neuroscience and signal processing community so far. A few studies have investigated electroencephalograms (EEG) recorded during meditation. Strong EEG activity has been observed in the left temporal lobe of meditators. Meditators exhibit more paroxysmal gamma waves (PGWs) in active regions of the brain. In this paper, a method is proposed to automatically detect PGWs from meditation EEG. The proposed algorithm is able to identify multiple sources in the brain that generate PGWs, and the sources associated with different types of PGWs can be distinguished. The effectiveness of the proposed method is assessed on 3 subjects possessing different degrees of expertise in practicing a yoga type meditation known as Bhramari Pranayama.},

keywords = {automated detection, Bhramari Pranayama, Blind source separation, brain active region, brain multiple source identification, Detectors, EEG activity, Electroencephalogram, Electroencephalography, left temporal lobe, medical signal detection, Meditation, meditation EEG, meditator, neurophysiology, neuroscience, Paroxysmal gamma wave, paroxysmal gamma waves, PGW, Principal component analysis, Sensitivity, signal processing community, Spike detection, Temporal lobe, yoga type meditation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Yang2013a,

title = {Quasi-Static SIMO Fading Channels at Finite Blocklength},

author = {Wei Yang and Giuseppe Durisi and Tobias Koch and Yury Polyanskiy},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6620483},

issn = {2157-8095},

year  = {2013},

date = {2013-01-01},

booktitle = {2013 IEEE International Symposium on Information Theory},

pages = {1531--1535},

publisher = {IEEE},

address = {Istanbul},

abstract = {We investigate the maximal achievable rate for a given blocklength and error probability over quasi-static single-input multiple-output (SIMO) fading channels. Under mild conditions on the channel gains, it is shown that the channel dispersion is zero regardless of whether the fading realizations are available at the transmitter and/or the receiver. The result follows from computationally and analytically tractable converse and achievability bounds. Through numerical evaluation, we verify that, in some scenarios, zero dispersion indeed entails fast convergence to outage capacity as the blocklength increases. In the example of a particular 1×2 SIMO Rician channel, the blocklength required to achieve 90% of capacity is about an order of magnitude smaller compared to the blocklength required for an AWGN channel with the same capacity.},

keywords = {achievability bounds, AWGN channel, AWGN channels, channel capacity, channel dispersion, channel gains, Dispersion, error probability, error statistics, Fading, fading channels, fading realizations, fast convergence, finite blocklength, maximal achievable rate, numerical evaluation, outage capacity, quasistatic SIMO fading channels, Random variables, Receivers, SIMO Rician channel, single-input multiple-output, Transmitters, zero dispersion},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Bifet2013a,

title = {Efficient Data Stream Classification via Probabilistic Adaptive Windows},

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

url = {http://dl.acm.org/citation.cfm?id=2480362.2480516},

isbn = {9781450316569},

year  = {2013},

date = {2013-01-01},

booktitle = {Proceedings of the 28th Annual ACM Symposium on Applied Computing - SAC '13},

publisher = {ACM Press},

address = {Coimbra},

abstract = {In the context of a data stream, a classifier must be able to learn from a theoretically-infinite stream of examples using limited time and memory, while being able to predict at any point. Many methods deal with this problem by basing their model on a window of examples. We introduce a probabilistic adaptive window (PAW) for data-stream learning, which improves this windowing technique with a mechanism to include older examples as well as the most recent ones, thus maintaining information on past concept drifts while being able to adapt quickly to new ones. We exemplify PAW with lazy learning methods in two variations: one to handle concept drift explicitly, and the other to add classifier diversity using an ensemble. Along with the standard measures of accuracy and time and memory use, we compare classifiers against state-of-the-art classifiers from the data-stream literature.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Koblents2013a,

title = {A Population Monte Carlo Scheme for Computational Inference in High Dimensional Spaces},

author = {Eugenia Koblents and Joaquin Miguez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6638881},

issn = {1520-6149},

year  = {2013},

date = {2013-01-01},

booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing},

pages = {6318--6322},

publisher = {IEEE},

address = {Vancouver},

abstract = {In this paper we address the Monte Carlo approximation of integrals with respect to probability distributions in high-dimensional spaces. In particular, we investigate the population Monte Carlo (PMC) scheme, which is based on an iterative importance sampling (IS) approach. Both IS and PMC suffer from the well known problem of degeneracy of the importance weights (IWs), which is closely related to the curse-of-dimensionality, and limits their applicability in large-scale practical problems. In this paper we investigate a novel PMC scheme that consists in performing nonlinear transformations of the IWs in order to smooth their variations and avoid degeneracy. We apply the modified IS scheme to the well-known mixture-PMC (MPMC) algorithm, which constructs the importance functions as mixtures of kernels. We present numerical results that show how the modified version of MPMC clearly outperforms the original scheme.},

keywords = {Approximation methods, computational inference, degeneracy of importance weights, high dimensional spaces, Importance sampling, importance weights, iterative importance sampling, iterative methods, mixture-PMC, mixture-PMC algorithm, Monte Carlo methods, MPMC, nonlinear transformations, population Monte Carlo, population Monte Carlo scheme, Probability density function, probability distributions, Proposals, Sociology, Standards},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Alvarado2013a,

title = {On the Asymptotic Optimality of Gray Codes for BICM and One-Dimensional Constellations},

author = {Alex Alvarado and Fredrik Br\"{a}nnstr\"{o}m and Erik Agrell and Tobias Koch},

year  = {2013},

date = {2013-01-01},

booktitle = {IEEE Communication Theory Workshop},

address = {Phuket},

keywords = {ALCIT},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Bifet2013b,

title = {Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them},

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

year  = {2013},

date = {2013-01-01},

booktitle = {ECML 2013: 24th European Conference on Machine Learning},

keywords = {COMPREHENSION},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Gopalan2013,

title = {Bayesian Nonparametric Poisson Factorization for Recommendation Systems},

author = {Prem Gopalan and Francisco J R Ruiz and Rajesh Ranganath and David M Blei},

year  = {2013},

date = {2013-01-01},

booktitle = {Workshop on Probabilistic Models for Big Data at Neural Information Processing Systems Conference 2013 (NIPS 2013)},

address = {Lake Tahoe},

keywords = {ALCIT},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Olmos2013c,

title = {A Finite Length Performance Analysis of LDPC Codes Constructed by Connecting Spatially Coupled Chains},

author = {Pablo M Olmos and David G M Mitchell and Dimitri Truhachev and Daniel J Costello},

url = {http://itw2013.tsc.uc3m.es/authors},

year  = {2013},

date = {2013-01-01},

booktitle = {2013 IEEE Information Theory Workshop},

address = {Seville},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Ruiz2013b,

title = {Infinite Continuous Feature Model for Psychiatric Comorbidity Analysis},

author = {Francisco J R Ruiz and Isabel Valera and Pablo M Olmos and Carlos Blanco and Fernando Perez-Cruz},

url = {https://googledrive.com/host/0B0TBaU3UgQ0Da3A2S2VWNTRzc1E/3.pdf},

year  = {2013},

date = {2013-01-01},

booktitle = {Workshop in Machine Learning for Clinical Data Analysis and Healthcare at Neural Information Processing Systems Conference 2013 (NIPS2013).},

address = {Lake Tahoe},

abstract = {Comorbidity analysis becomes particularly relevant in the field of psychiatry, where clinical ex- perience and several studies suggest that the relation among the psychiatric disorders may have etiological and treatment implications. Several studies have focused on the search of the underlying interrelationships among psychiatric disorders, which can be useful to analyze the structure of the diagnostic classification system, and guide treatment approaches for each disorder [1]. Motivated by this relevance, in this paper we aim at finding the latent structure behind a database of psychiatric disorders. In particular, making use of the database extracted from the analysis of the National Epi- demiologic Survey on Alcohol and Related Conditions 1 (NESARC) in [1], we focus on the analysis of 20 common psychiatric disorders, including substance abuse, mood and personality disorders. Our goal is to find comorbidity patterns in the database, allowing us to seek hidden causes and to provide a tool for detecting those subjects with a high risk of suffering from these disorders.},

keywords = {ALCIT},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Ruiz2013a,

title = {Bayesian Nonparametric Comorbidity Analysis of Psychiatric Disorders},

author = {Francisco J R Ruiz and Isabel Valera and Carlos Blanco and Fernando Perez-Cruz},

year  = {2013},

date = {2013-01-01},

booktitle = {9th Conference on Bayesian Nonparametrics},

address = {Amsterdam},

keywords = {ALCIT},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{allerton2012,

title = {Converse Bounds for Finite-Length Joint Source-Channel Coding},

author = {Adria Tauste Campo and Gonzalo Vazquez-Vilar and Albert Guill\'{e}n i F\`{a}bregas and Tobias Koch and Alfonso Martinez},

year  = {2012},

date = {2012-10-01},

booktitle = {50th Annual Allerton Conference on Communication, Control and Computing (Allerton 2012)},

address = {Allerton, IL, USA},

note = {Invited},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{iccasp2012,

title = {Design of universal multicoset sampling patterns for compressed sensing of multiband sparse signals},

author = {Mar\'{i}a Elena Dom\'{i}nguez-Jim\'{e}nez and Nuria Gonz\'{a}lez-Prelcic and Gonzalo Vazquez-Vilar and Roberto L\'{o}pez-Valcarce},

year  = {2012},

date = {2012-03-01},

booktitle = {2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)},

address = {Kyoto, Japan},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Salamanca2012,

title = {Tree-Structured Expectation Propagation for LDPC Decoding over the AWGN Channel},

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

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6349716},

issn = {1551-2541},

year  = {2012},

date = {2012-01-01},

booktitle = {2012 IEEE International Workshop on Machine Learning for Signal Processing},

pages = {1--6},

publisher = {IEEE},

address = {Santander},

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 floor.},

keywords = {additive white Gaussian noise channel, Approximation algorithms, Approximation methods, approximation theory, AWGN channel, AWGN channels, belief propagation solution, Bit error rate, Decoding, error floor reduction, finite-length regime, Gain, Joints, LDPC decoding, low-density parity-check decoding, pairwise marginal constraint, parity check codes, TEP decoder, tree-like approximation, tree-structured expectation propagation, trees (mathematics)},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Zhong2012,

title = {Low-Complexity Noise-Resilient Recovery of Phase and Amplitude from Defocused Intensity Images},

author = {Jingshan Zhong and Justin Dauwels and Manuel A Vazquez and Laura Waller},

url = {http://www.opticsinfobase.org/abstract.cfm?URI=COSI-2012-CTu4B.1},

isbn = {1-55752-947-7},

year  = {2012},

date = {2012-01-01},

booktitle = {Imaging and Applied Optics Technical Papers},

pages = {CTu4B.1},

publisher = {OSA},

address = {Washington, D.C.},

abstract = {A low-complexity augmented Kalman filter is proposed to efficiently recover the phase from a series of noisy intensity images. The proposed method is robust to noise, has low complexity, and may enable real-time phase recovery.},

keywords = {Image reconstruction techniques, Phase retrieval, Wave propagation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Koch2012,

title = {The Capacity Loss of Dense Constellations},

author = {Tobias Koch and Alfonso Martinez and Albert Guillen i Fabregas},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6283482},

issn = {2157-8095},

year  = {2012},

date = {2012-01-01},

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

pages = {572--576},

publisher = {IEEE},

address = {Cambridge, MA},

abstract = {We determine the loss in capacity incurred by using signal constellations with a bounded support over general complex-valued additive-noise channels for suitably high signal-to-noise ratio. Our expression for the capacity loss recovers the power loss of 1.53 dB for square signal constellations.},

keywords = {capacity loss, channel capacity, Constellation diagram, dense constellations, Entropy, general complex-valued additive-noise channels, high signal-to-noise ratio, loss 1.53 dB, power loss, Quadrature amplitude modulation, Random variables, signal constellations, Signal processing, Signal to noise ratio, square signal constellations, Upper bound},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{O'Mahony2012,

title = {A novel Sequential Bayesian Approach to GPS Acquisition},

author = {Niamh O'Mahony and Fernando Perez-Cruz},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6232921},

isbn = {978-1-4673-1878-5},

year  = {2012},

date = {2012-01-01},

booktitle = {2012 3rd International Workshop on Cognitive Information Processing (CIP)},

pages = {1--6},

publisher = {IEEE},

address = {Baiona},

abstract = {In this work, a novel online learning algorithm is presented for the synchronization of Global Positioning System (GPS) signal parameters at the acquisition, or coarse synchronization, stage. The algorithm is based on a Bayesian approach, which has, to date, not been exploited for the acquisition problem. Simulated results are presented to illustrate the algorithm performance, in terms of accuracy and acquisition time, along with results from the acquisition of signals from live GPS satellites using both the new algorithm and a state-of-the-art approach for comparison.},

keywords = {Bayes methods, coarse synchronization, Correlators, data acquisition, Delay, Doppler effect, Global Positioning System, GPS acquisition, GPS signal parameters, learning (artificial intelligence), online learning algorithm, Receivers, Satellites, sequential Bayesian approach, signal acquisition, signal detection, Synchronization},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Perez-Cruz2012,

title = {New Tools to Generate Predictive Models for Attempts Suicide},

author = {Fernando Perez-Cruz},

year  = {2012},

date = {2012-01-01},

booktitle = {National Conference on Psychiatry},

address = {Bilbao},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Olmos2012,

title = {Finite-Length Performance of Spatially-Coupled LDPC Codes under TEP Decoding},

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

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6404722},

isbn = {978-1-4673-0223-4},

year  = {2012},

date = {2012-01-01},

booktitle = {2012 IEEE Information Theory Workshop},

pages = {1--6},

publisher = {IEEE},

address = {Lausanne},

keywords = {asymptotic limit, belief propagation decoding, Complexity theory, convolutional codes, convolutional LDPC codes, Decoding, decoding latency, decoding threshold, erasure channel, Error analysis, error rates, finite-length analysis, finite-length performance, maximum a posteriori threshold, maximum likelihood estimation, parity check codes, regular sparse codes, spatially-coupled LDPC codes, TEP decoding, tree-structured expectation propagation, underlying regular code, very large code length, window-sliding scheme},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Read2012,

title = {Advances in Intelligent Data Analysis XI},

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

editor = {Jaakko Hollm\'{e}n and Frank Klawonn and Allan Tucker},

url = {http://www.springerlink.com/index/10.1007/978-3-642-34156-4},

isbn = {978-3-642-34155-7},

year  = {2012},

date = {2012-01-01},

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

publisher = {Springer Berlin Heidelberg},

address = {Helsinki},

series = {Lecture Notes in Computer Science},

abstract = {Many real world problems involve the challenging context of data streams, where classifiers must be incremental: able to learn from a theoretically-infinite stream of examples using limited time and memory, while being able to predict at any point. Two approaches dominate the literature: batch-incremental methods that gather examples in batches to train models; and instance-incremental methods that learn from each example as it arrives. Typically, papers in the literature choose one of these approaches, but provide insufficient evidence or references to justify their choice. We provide a first in-depth analysis comparing both approaches, including how they adapt to concept drift, and an extensive empirical study to compare several different versions of each approach. Our results reveal the respective advantages and disadvantages of the methods, which we discuss in detail.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Florentino-Liano2012c,

title = {Hierarchical Dynamic Model for Human Daily Activity Recognition},

author = {Blanca Florentino-Lia\~{n}o and Niamh O'Mahony and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://www.biosignals.biostec.org/Abstracts/2012/BIOSIGNALS_2012_Abstracts.htm},

year  = {2012},

date = {2012-01-01},

booktitle = {BIOSIGNALS 2012 (BIOSTEC)},

volume = {85},

address = {Vilamoura},

abstract = {This work deals with the task of human daily activity recognition using miniature inertial sensors. The proposed method is based on the development of a hierarchical dynamic model, incorporating both inter-activity and intra-activity dynamics, thereby exploiting the inherently dynamic nature of the problem to aid the classification task. The method uses raw acceleration and angular velocity signals, directly recorded by inertial sensors, bypassing commonly used feature extraction and selection techniques and, thus, keeping all information regarding the dynamics of the signals. Classification results show a competitive performance compared to state-of-the-art methods.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Taborda2012,

title = {Derivative of the Relative Entropy over the Poisson and Binomial Channel},

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

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6404699},

isbn = {978-1-4673-0223-4},

year  = {2012},

date = {2012-01-01},

booktitle = {2012 IEEE Information Theory Workshop},

pages = {386--390},

publisher = {IEEE},

address = {Lausanne},

abstract = {In this paper it is found that, regardless of the statistics of the input, the derivative of the relative entropy over the Binomial channel can be seen as the expectation of a function that has as argument the mean of the conditional distribution that models the channel. Based on this relationship we formulate a similar expression for the mutual information concept. In addition to this, using the connection between the Binomial and Poisson distribution we develop similar results for the Poisson channel. Novelty of the results presented here lies on the fact that, expressions obtained can be applied to a wide range of scenarios.},

keywords = {binomial channel, binomial distribution, Channel estimation, conditional distribution, Entropy, Estimation, function expectation, Mutual information, mutual information concept, Poisson channel, Poisson distribution, Random variables, relative entropy derivative, similar expression},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Florentino-Liano2012b,

title = {Long Term Human Activity Recognition with Automatic Orientation Estimation},

author = {Blanca Florentino-Lia\~{n}o and Niamh O'Mahony and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6349789},

issn = {1551-2541},

year  = {2012},

date = {2012-01-01},

booktitle = {2012 IEEE International Workshop on Machine Learning for Signal Processing},

pages = {1--6},

publisher = {IEEE},

address = {Santander},

abstract = {This work deals with the elimination of sensitivity to sensor orientation in the task of human daily activity recognition using a single miniature inertial sensor. The proposed method detects time intervals of walking, automatically estimating the orientation in these intervals and transforming the observed signals to a “virtual” sensor orientation. Classification results show that excellent performance, in terms of both precision and recall (up to 100%), is achieved, for long-term recordings in real-life settings.},

keywords = {Acceleration, Activity recognition, automatic orientation estimation, biomedical equipment, Estimation, Gravity, Hidden Markov models, human daily activity recognition, Humans, Legged locomotion, long term human activity recognition, medical signal processing, object recognition, orientation estimation, sensors, single miniature inertial sensor, time intervals, Vectors, virtual sensor orientation, wearable sensors},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Durisi2012,

title = {Diversity Versus Channel Knowledge at Finite Block-Length},

author = {Giuseppe Durisi and Tobias Koch and Yury Polyanskiy},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6404740},

isbn = {978-1-4673-0223-4},

year  = {2012},

date = {2012-01-01},

booktitle = {2012 IEEE Information Theory Workshop},

pages = {572--576},

publisher = {IEEE},

address = {Lausanne},

abstract = {We study the maximal achievable rate R*(n, ∈) for a given block-length n and block error probability o over Rayleigh block-fading channels in the noncoherent setting and in the finite block-length regime. Our results show that for a given block-length and error probability, R*(n, ∈) is not monotonic in the channel's coherence time, but there exists a rate maximizing coherence time that optimally trades between diversity and cost of estimating the channel.},

keywords = {Approximation methods, block error probability, channel coherence time, Channel estimation, channel knowledge, Coherence, diversity, diversity reception, error statistics, Fading, finite block-length, maximal achievable rate, noncoherent setting, Rayleigh block-fading channels, Rayleigh channels, Receivers, Signal to noise ratio, Upper bound},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Florentino-Liano2012a,

title = {Human Activity Recognition Using Inertial Sensors with Invariance to Sensor Orientation},

author = {Blanca Florentino-Lia\~{n}o and Niamh O'Mahony and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6232914},

isbn = {978-1-4673-1878-5},

year  = {2012},

date = {2012-01-01},

booktitle = {2012 3rd International Workshop on Cognitive Information Processing (CIP)},

pages = {1--6},

publisher = {IEEE},

address = {Baiona},

abstract = {This work deals with the task of human daily activity recognition using miniature inertial sensors. The proposed method reduces sensitivity to the position and orientation of the sensor on the body, which is inherent in traditional methods, by transforming the observed signals to a “virtual” sensor orientation. By means of this computationally low-cost transform, the inputs to the classification algorithm are made invariant to sensor orientation, despite the signals being recorded from arbitrary sensor placements. Classification results show that improved performance, in terms of both precision and recall, is achieved with the transformed signals, relative to classification using raw sensor signals, and the algorithm performs competitively compared to the state-of-the-art. Activity recognition using data from a sensor with completely unknown orientation is shown to perform very well over a long term recording in a real-life setting.},

keywords = {Acceleration, Accelerometers, biomechanics, classification algorithm, Gyroscopes, Hidden Markov models, human daily activity recognition, inertial measurement unit, Legged locomotion, miniature inertial sensors, raw sensor signal classification, sensor orientation invariance, sensor orientation sensitivity, sensor placement, sensor position sensitivity, sensors, signal classification, signal transformation, Training, triaxial accelerometer, triaxial gyroscope, virtual sensor orientation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Garcia-Moreno2012,

title = {A Hold-out Method to Correct PCA Variance Inflation},

author = {Pablo Garcia-Moreno and Antonio Art\'{e}s-Rodr\'{i}guez and Lars Kai Hansen},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6232926},

isbn = {978-1-4673-1878-5},

year  = {2012},

date = {2012-01-01},

booktitle = {2012 3rd International Workshop on Cognitive Information Processing (CIP)},

pages = {1--6},

publisher = {IEEE},

address = {Baiona},

abstract = {In this paper we analyze the problem of variance inflation experienced by the PCA algorithm when working in an ill-posed scenario where the dimensionality of the training set is larger than its sample size. In an earlier article a correction method based on a Leave-One-Out (LOO) procedure was introduced. We propose a Hold-out procedure whose computational cost is lower and, unlike the LOO method, the number of SVD's does not scale with the sample size. We analyze its properties from a theoretical and empirical point of view. Finally we apply it to a real classification scenario.},

keywords = {Approximation methods, classification scenario, computational complexity, computational cost, Computational efficiency, correction method, hold-out method, hold-out procedure, leave-one-out procedure, LOO method, LOO procedure, Mathematical model, PCA algorithm, PCA variance inflation, Principal component analysis, singular value decomposition, Standards, SVD, Training},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Montoya-Martinez2012,

title = {Structured Sparsity Regularization Approach to the EEG Inverse Problem},

author = {Jair Montoya-Martinez and Antonio Art\'{e}s-Rodr\'{i}guez and Lars Kai Hansen and Massimiliano Pontil},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6232898},

isbn = {978-1-4673-1878-5},

year  = {2012},

date = {2012-01-01},

booktitle = {2012 3rd International Workshop on Cognitive Information Processing (CIP)},

pages = {1--6},

publisher = {IEEE},

address = {Baiona},

abstract = {Localization of brain activity involves solving the EEG inverse problem, which is an undetermined ill-posed problem. We propose a novel approach consisting in estimating, using structured sparsity regularization techniques, the Brain Electrical Sources (BES) matrix directly in the spatio-temporal source space. We use proximal splitting optimization methods, which are efficient optimization techniques, with good convergence rates and with the ability to handle large nonsmooth convex problems, which is the typical scenario in the EEG inverse problem. We have evaluated our approach under a simulated scenario, consisting in estimating a synthetic BES matrix with 5124 sources. We report results using ℓ1 (LASSO), ℓ1/ℓ2 (Group LASSO) and ℓ1 + ℓ1/ℓ2 (Sparse Group LASSO) regularizers.},

keywords = {BES, brain electrical sources matrix, Brain modeling, EEG inverse problem, Electrodes, Electroencephalography, good convergence, Inverse problems, large nonsmooth convex problems, medical signal processing, optimisation, Optimization, proximal splitting optimization methods, Sparse matrices, spatio-temporal source space, structured sparsity regularization approach, undetermined ill-posed problem},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Monzon2012,

title = {Sparse Spectral Analysis of Atrial Fibrillation Electrograms.},

author = {Sandra Monzon and Tom Trigano and David Luengo and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6349721},

issn = {1551-2541},

year  = {2012},

date = {2012-01-01},

booktitle = {2012 IEEE International Workshop on Machine Learning for Signal Processing},

pages = {1--6},

publisher = {IEEE},

address = {Santander},

abstract = {Atrial fibrillation (AF) is a common heart disorder. One of the most prominent hypothesis about its initiation and maintenance considers multiple uncoordinated activation foci inside the atrium. However, the implicit assumption behind all the signal processing techniques used for AF, such as dominant frequency and organization analysis, is the existence of a single regular component in the observed signals. In this paper we take into account the existence of multiple foci, performing a spectral analysis to detect their number and frequencies. In order to obtain a cleaner signal on which the spectral analysis can be performed, we introduce sparsity-aware learning techniques to infer the spike trains corresponding to the activations. The good performance of the proposed algorithm is demonstrated both on synthetic and real data.},

keywords = {Algorithm design and analysis, atrial fibrillation, atrial fibrillation electrogram, biomedical signal processing, dominant frequency, Doped fiber amplifiers, electrocardiography, Harmonic analysis, Heart, heart disorder, Indexes, Mathematical model, medical signal processing, multiple foci, multiple uncoordinated activation foci, signal processing technique, sparse spectral analysis, sparsity-aware learning, sparsity-aware learning technique, spectral analysis, spike train},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Koblents2012,

title = {Importance Sampling with Transformed Weights},

author = {Eugenia Koblents and Joaquin Miguez},

url = {http://www.oxford-man.ox.ac.uk/sites/default/files/events/Mon_24_JoaquinMiguez_06FINAL.pdf},

year  = {2012},

date = {2012-01-01},

booktitle = {Data Assimilation Workshop, Oxford\textendashMan Institute},

address = {Oxford},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Olmos2012a,

title = {Finite-Length Analysis of the TEP Decoder for LDPC Ensembles over the BEC},

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

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6283932},

issn = {2157-8095},

year  = {2012},

date = {2012-01-01},

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

pages = {2346--2350},

publisher = {IEEE},

address = {Cambridge, MA},

abstract = {In this work, we analyze the finite-length performance of low-density parity check (LDPC) ensembles decoded over the binary erasure channel (BEC) using the tree-expectation propagation (TEP) algorithm. In a previous paper, we showed that the TEP improves the BP performance for decoding regular and irregular short LDPC codes, but the perspective was mainly empirical. In this work, given the degree-distribution of an LDPC ensemble, we explain and predict the range of code lengths for which the TEP improves the BP solution. In addition, for LDPC ensembles that present a single critical point, we propose a scaling law to accurately predict the performance in the waterfall region. These results are of critical importance to design practical LDPC codes for the TEP decoder.},

keywords = {Approximation methods, BEC, binary codes, binary erasure channel, Decoding, Error analysis, error probability, finite-length analysis, LDPC ensembles, low-density parity check ensembles, parity check codes, TEP decoder, Trajectory, tree-expectation propagation algorithm, waterfall region},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Pastore2012,

title = {Improved Capacity Lower Bounds for Fading Channels with Imperfect CSI Using Rate Splitting},

author = {Adriano Pastore and Tobias Koch and Javier Rodriguez Fonollosa},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6377031},

isbn = {978-1-4673-4681-8},

year  = {2012},

date = {2012-01-01},

booktitle = {2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel},

pages = {1--5},

publisher = {IEEE},

address = {Eilat},

abstract = {As shown by Medard (“The effect upon channel capacity in wireless communications of perfect and imperfect knowledge of the channel,” IEEE Trans. Inform. Theory, May 2000), the capacity of fading channels with imperfect channel-state information (CSI) can be lower-bounded by assuming a Gaussian channel input X, and by upper-bounding the conditional entropy h(XY, Ĥ), conditioned on the channel output Y and the CSI Ĥ, by the entropy of a Gaussian random variable with variance equal to the linear minimum mean-square error in estimating X from (Y, Ĥ). We demonstrate that, by using a rate-splitting approach, this lower bound can be sharpened: we show that by expressing the Gaussian input X as as the sum of two independent Gaussian variables X(1) and X(2), and by applying Medard's lower bound first to analyze the mutual information between X(1) and Y conditioned on Ĥ while treating X(2) as noise, and by applying the lower bound then to analyze the mutual information between X(2) and Y conditioned on (X(1), Ĥ), we obtain a lower bound on the capacity that is larger than Medard's lower bound.},

keywords = {channel capacity, channel capacity lower bounds, conditional entropy, Decoding, Entropy, Fading, fading channels, Gaussian channel, Gaussian channels, Gaussian random variable, imperfect channel-state information, imperfect CSI, independent Gaussian variables, linear minimum mean-square error, mean square error methods, Medard lower bound, Mutual information, Random variables, rate splitting approach, Resource management, Upper bound, wireless communications},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Ruiz2012,

title = {Bayesian Nonparametric Modeling of Suicide Attempts},

author = {Francisco J R Ruiz and Isabel Valera and Carlos Blanco and Fernando Perez-Cruz},

url = {http://nips.cc/Conferences/2012/Program/event.php?ID=3582},

year  = {2012},

date = {2012-01-01},

booktitle = {Advances in Neural Information Processing Systems 25},

address = {Lake Tahoe},

abstract = {The National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) database contains a large amount of information, regarding the way of life, medical conditions, depression, etc., of a representative sample of the U.S. population. In the present paper, we are interested in seeking the hidden causes behind the suicide attempts, for which we propose to model the subjects using a nonparametric latent model based on the Indian Buffet Process (IBP). Due to the nature of the data, we need to adapt the observation model for discrete random variables. We propose a generative model in which the observations are drawn from a multinomial-logit distribution given the IBP matrix. The implementation of an efficient Gibbs sampler is accomplished using the Laplace approximation, which allows us to integrate out the weighting factors of the multinomial-logit likelihood model. Finally, the experiments over the NESARC database show that our model properly captures some of the hidden causes that model suicide attempts.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Zhong2012a,

title = {Efficient Gaussian Inference Algorithms for Phase Imaging},

author = {Jingshan Zhong and Justin Dauwels and Manuel A Vazquez and Laura Waller},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6287959},

issn = {1520-6149},

year  = {2012},

date = {2012-01-01},

booktitle = {2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

pages = {617--620},

publisher = {IEEE},

address = {Kyoto},

abstract = {Novel efficient algorithms are developed to infer the phase of a complex optical field from a sequence of intensity images taken at different defocus distances. The non-linear observation model is approximated by a linear model. The complex optical field is inferred by iterative Kalman smoothing in the Fourier domain: forward and backward sweeps of Kalman recursions are alternated, and in each such sweep, the approximate linear model is refined. By limiting the number of iterations, one can trade off accuracy vs. complexity. The complexity of each iteration in the proposed algorithm is in the order of N logN, where N is the number of pixels per image. The storage required scales linearly with N. In contrast, the complexity of existing phase inference algorithms scales with N3 and the required storage with N2. The proposed algorithms may enable real-time estimation of optical fields from noisy intensity images.},

keywords = {biomedical optical imaging, complex optical field, computational complexity, defocus distances, Fourier domain, Gaussian inference algorithms, image sequences, inference mechanisms, intensity image sequence, iterative Kalman smoothing, iterative methods, Kalman filter, Kalman filters, Kalman recursions, linear model, Manganese, Mathematical model, medical image processing, Noise, noisy intensity image, nonlinear observation model, Optical imaging, Optical sensors, Phase imaging, phase inference algorithms, smoothing methods},

pubstate = {published},

tppubtype = {inproceedings}

}

@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 F\`{a}bregas 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 Csisz\'{a}r'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 \'{A}ngel 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 L\'{o}pez-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 Guill\'{e}n i F\`{a}bregas 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 L\'{o}pez-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 L\'{o}pez-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}

}