2014
Alvarado, Alex; Brannstrom, Fredrik; Agrell, Erik; Koch, Tobias
High-SNR Asymptotics of Mutual Information for Discrete Constellations With Applications to BICM Artículo de revista
En: IEEE Transactions on Information Theory, vol. 60, no 2, pp. 1061–1076, 2014, ISSN: 0018-9448.
Resumen | Enlaces | BibTeX | Etiquetas: additive white Gaussian noise channel, Anti-Gray code, bit-interleaved coded modulation, discrete constellations, Entropy, Gray code, high-SNR asymptotics, IP networks, Labeling, minimum-mean square error, Modulation, Mutual information, Signal to noise ratio, Vectors
@article{Alvarado2014,
title = {High-SNR Asymptotics of Mutual Information for Discrete Constellations With Applications to BICM},
author = {Alex Alvarado and Fredrik Brannstrom and Erik Agrell and Tobias Koch},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6671479
http://www.tsc.uc3m.es/~koch/files/IEEE_TIT_60%282%29.pdf},
issn = {0018-9448},
year = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {60},
number = {2},
pages = {1061--1076},
abstract = {Asymptotic expressions of the mutual information between any discrete input and the corresponding output of the scalar additive white Gaussian noise channel are presented in the limit as the signal-to-noise ratio (SNR) tends to infinity. Asymptotic expressions of the symbol-error probability (SEP) and the minimum mean-square error (MMSE) achieved by estimating the channel input given the channel output are also developed. It is shown that for any input distribution, the conditional entropy of the channel input given the output, MMSE, and SEP have an asymptotic behavior proportional to the Gaussian Q-function. The argument of the Q-function depends only on the minimum Euclidean distance (MED) of the constellation and the SNR, and the proportionality constants are functions of the MED and the probabilities of the pairs of constellation points at MED. The developed expressions are then generalized to study the high-SNR behavior of the generalized mutual information (GMI) for bit-interleaved coded modulation (BICM). By means of these asymptotic expressions, the long-standing conjecture that Gray codes are the binary labelings that maximize the BICM-GMI at high SNR is proven. It is further shown that for any equally spaced constellation whose size is a power of two, there always exists an anti-Gray code giving the lowest BICM-GMI at high SNR.},
keywords = {additive white Gaussian noise channel, Anti-Gray code, bit-interleaved coded modulation, discrete constellations, Entropy, Gray code, high-SNR asymptotics, IP networks, Labeling, minimum-mean square error, Modulation, Mutual information, Signal to noise ratio, Vectors},
pubstate = {published},
tppubtype = {article}
}
Cespedes, Javier; Olmos, Pablo M; Sanchez-Fernandez, Matilde; Perez-Cruz, Fernando
Expectation Propagation Detection for High-order High-dimensional MIMO Systems Artículo de revista
En: IEEE Transactions on Communications, vol. PP, no 99, pp. 1–1, 2014, ISSN: 0090-6778.
Resumen | Enlaces | BibTeX | Etiquetas: Approximation methods, computational complexity, Detectors, MIMO, Signal to noise ratio, Vectors
@article{Cespedes2014,
title = {Expectation Propagation Detection for High-order High-dimensional MIMO Systems},
author = {Javier Cespedes and Pablo M Olmos and Matilde Sanchez-Fernandez and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6841617},
issn = {0090-6778},
year = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Communications},
volume = {PP},
number = {99},
pages = {1--1},
abstract = {Modern communications systems use multiple-input multiple-output (MIMO) and high-order QAM constellations for maximizing spectral efficiency. However, as the number of antennas and the order of the constellation grow, the design of efficient and low-complexity MIMO receivers possesses big technical challenges. For example, symbol detection can no longer rely on maximum likelihood detection or sphere-decoding methods, as their complexity increases exponentially with the number of transmitters/receivers. In this paper, we propose a low-complexity high-accuracy MIMO symbol detector based on the Expectation Propagation (EP) algorithm. EP allows approximating iteratively at polynomial-time the posterior distribution of the transmitted symbols. We also show that our EP MIMO detector outperforms classic and state-of-the-art solutions reducing the symbol error rate at a reduced computational complexity.},
keywords = {Approximation methods, computational complexity, Detectors, MIMO, Signal to noise ratio, Vectors},
pubstate = {published},
tppubtype = {article}
}
Read, Jesse; Bielza, Concha; Larranaga, Pedro
Multi-Dimensional Classification with Super-Classes Artículo de revista
En: IEEE Transactions on Knowledge and Data Engineering, vol. 26, no 7, pp. 1720–1733, 2014, ISSN: 1041-4347.
Resumen | Enlaces | BibTeX | Etiquetas: Accuracy, Bayes methods, Classification, COMPRHENSION, conditional dependence, Context, core goals, data instance, evaluation metrics, Integrated circuit modeling, modeling class dependencies, multi-dimensional, Multi-dimensional classification, multidimensional classification problem, multidimensional datasets, multidimensional learners, multilabel classification, multilabel research, multiple class variables, ordinary class, pattern classification, problem transformation, recently-popularized task, super classes, super-class partitions, tractable running time, Training, Vectors
@article{Read2014bb,
title = {Multi-Dimensional Classification with Super-Classes},
author = {Jesse Read and Concha Bielza and Pedro Larranaga},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6648319},
issn = {1041-4347},
year = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Knowledge and Data Engineering},
volume = {26},
number = {7},
pages = {1720--1733},
publisher = {IEEE},
abstract = {The multi-dimensional classification problem is a generalisation of the recently-popularised task of multi-label classification, where each data instance is associated with multiple class variables. There has been relatively little research carried out specific to multi-dimensional classification and, although one of the core goals is similar (modelling dependencies among classes), there are important differences; namely a higher number of possible classifications. In this paper we present method for multi-dimensional classification, drawing from the most relevant multi-label research, and combining it with important novel developments. Using a fast method to model the conditional dependence between class variables, we form super-class partitions and use them to build multi-dimensional learners, learning each super-class as an ordinary class, and thus explicitly modelling class dependencies. Additionally, we present a mechanism to deal with the many class values inherent to super-classes, and thus make learning efficient. To investigate the effectiveness of this approach we carry out an empirical evaluation on a range of multi-dimensional datasets, under different evaluation metrics, and in comparison with high-performing existing multi-dimensional approaches from the literature. Analysis of results shows that our approach offers important performance gains over competing methods, while also exhibiting tractable running time.},
keywords = {Accuracy, Bayes methods, Classification, COMPRHENSION, conditional dependence, Context, core goals, data instance, evaluation metrics, Integrated circuit modeling, modeling class dependencies, multi-dimensional, Multi-dimensional classification, multidimensional classification problem, multidimensional datasets, multidimensional learners, multilabel classification, multilabel research, multiple class variables, ordinary class, pattern classification, problem transformation, recently-popularized task, super classes, super-class partitions, tractable running time, Training, Vectors},
pubstate = {published},
tppubtype = {article}
}
Cespedes, Javier; Olmos, Pablo M; Sanchez-Fernandez, Matilde; Perez-Cruz, Fernando
Improved Performance of LDPC-Coded MIMO Systems with EP-based Soft-Decisions Proceedings Article
En: 2014 IEEE International Symposium on Information Theory, pp. 1997–2001, IEEE, Honolulu, 2014, ISBN: 978-1-4799-5186-4.
Resumen | Enlaces | BibTeX | Etiquetas: Approximation algorithms, Approximation methods, approximation theory, Channel Coding, channel decoder, communication complexity, complexity, Complexity theory, Detectors, encoding scheme, EP soft bit probability, EP-based soft decision, error statistics, expectation propagation, expectation-maximisation algorithm, expectation-propagation algorithm, Gaussian approximation, Gaussian channels, LDPC, LDPC coded MIMO system, Low Complexity receiver, MIMO, MIMO communication, MIMO communication systems, MIMO receiver, modern communication system, multiple input multiple output, parity check codes, per-antenna soft bit probability, posterior marginalization problem, posterior probability computation, QAM constellation, Quadrature amplitude modulation, radio receivers, signaling, spectral analysis, spectral efficiency maximization, symbol detection, telecommunication signalling, Vectors
@inproceedings{Cespedes2014b,
title = {Improved Performance of LDPC-Coded MIMO Systems with EP-based Soft-Decisions},
author = {Javier Cespedes and Pablo M Olmos and Matilde Sanchez-Fernandez and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6875183},
isbn = {978-1-4799-5186-4},
year = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE International Symposium on Information Theory},
pages = {1997--2001},
publisher = {IEEE},
address = {Honolulu},
abstract = {Modern communications systems use efficient encoding schemes, multiple-input multiple-output (MIMO) and high-order QAM constellations for maximizing spectral efficiency. However, as the dimensions of the system grow, the design of efficient and low-complexity MIMO receivers possesses technical challenges. Symbol detection can no longer rely on conventional approaches for posterior probability computation due to complexity. Marginalization of this posterior to obtain per-antenna soft-bit probabilities to be fed to a channel decoder is computationally challenging when realistic signaling is used. In this work, we propose to use Expectation Propagation (EP) algorithm to provide an accurate low-complexity Gaussian approximation to the posterior, easily solving the posterior marginalization problem. EP soft-bit probabilities are used in an LDPC-coded MIMO system, achieving outstanding performance improvement compared to similar approaches in the literature for low-complexity LDPC MIMO decoding.},
keywords = {Approximation algorithms, Approximation methods, approximation theory, Channel Coding, channel decoder, communication complexity, complexity, Complexity theory, Detectors, encoding scheme, EP soft bit probability, EP-based soft decision, error statistics, expectation propagation, expectation-maximisation algorithm, expectation-propagation algorithm, Gaussian approximation, Gaussian channels, LDPC, LDPC coded MIMO system, Low Complexity receiver, MIMO, MIMO communication, MIMO communication systems, MIMO receiver, modern communication system, multiple input multiple output, parity check codes, per-antenna soft bit probability, posterior marginalization problem, posterior probability computation, QAM constellation, Quadrature amplitude modulation, radio receivers, signaling, spectral analysis, spectral efficiency maximization, symbol detection, telecommunication signalling, Vectors},
pubstate = {published},
tppubtype = {inproceedings}
}
Stinner, Markus; Olmos, Pablo M
Analyzing Finite-length Protograph-Based Spatially Coupled LDPC Codes Proceedings Article
En: 2014 IEEE International Symposium on Information Theory, pp. 891–895, IEEE, Honolulu, 2014, ISBN: 978-1-4799-5186-4.
Resumen | Enlaces | BibTeX | Etiquetas: binary erasure channel, covariance analysis, covariance evolution, Decoding, degree-one check nodes, Error analysis, finite-length protograph, mean evolution, Monte Carlo methods, parity check codes, peeling decoding, protograph-based SC-LDPC codes, spatially coupled low-density parity-check codes, stable decoding phase, Steady-state, Vectors
@inproceedings{Stinner2014,
title = {Analyzing Finite-length Protograph-Based Spatially Coupled LDPC Codes},
author = {Markus Stinner and Pablo M Olmos},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6874961},
isbn = {978-1-4799-5186-4},
year = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE International Symposium on Information Theory},
pages = {891--895},
publisher = {IEEE},
address = {Honolulu},
abstract = {The peeling decoding for spatially coupled low-density parity-check (SC-LDPC) codes is analyzed for a binary erasure channel. An analytical calculation of the mean evolution of degree-one check nodes of protograph-based SC-LDPC codes is given and an estimate for the covariance evolution of degree-one check nodes is proposed in the stable decoding phase where the decoding wave propagates along the chain of coupled codes. Both results are verified numerically. Protograph-based SC-LDPC codes turn out to have a more robust behavior than unstructured random SC-LDPC codes. Using the analytically calculated parameters, the finite-length scaling laws for these constructions are given and verified by numerical simulations.},
keywords = {binary erasure channel, covariance analysis, covariance evolution, Decoding, degree-one check nodes, Error analysis, finite-length protograph, mean evolution, Monte Carlo methods, parity check codes, peeling decoding, protograph-based SC-LDPC codes, spatially coupled low-density parity-check codes, stable decoding phase, Steady-state, Vectors},
pubstate = {published},
tppubtype = {inproceedings}
}
2013
Luengo, David; Via, Javier; Monzon, Sandra; Trigano, Tom; Artés-Rodríguez, Antonio
Cross-Products LASSO Proceedings Article
En: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6118–6122, IEEE, Vancouver, 2013, ISSN: 1520-6149.
Resumen | Enlaces | BibTeX | Etiquetas: 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
@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}
}
Vazquez, Manuel A; Miguez, Joaquin
User Activity Tracking in DS-CDMA Systems Artículo de revista
En: IEEE Transactions on Vehicular Technology, vol. 62, no 7, pp. 3188–3203, 2013, ISSN: 0018-9545.
Resumen | Enlaces | BibTeX | Etiquetas: Activity detection, activity tracking, Bayes methods, Bayesian framework, Channel estimation, code division multiple access, code-division multiple access (CDMA), computer simulations, data detection, direct sequence code division multiple-access, DS-CDMA systems, Equations, joint channel and data estimation, joint channel estimation, Joints, MAP equalizers, Mathematical model, maximum a posteriori, MIMO communication, Multiaccess communication, multiple-input-multiple-output communication chann, multiuser communication systems, per-survivor processing (PSP), radio receivers, Receivers, sequential Monte Carlo (SMC) methods, time-varying number, time-varying parameter, Vectors, wireless channels
@article{Vazquez2013a,
title = {User Activity Tracking in DS-CDMA Systems},
author = {Manuel A Vazquez and Joaquin Miguez},
url = {http://www.tsc.uc3m.es/~jmiguez/papers/P39_2013_User Activity Tracking in DS-CDMA Systems.pdf http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6473922},
issn = {0018-9545},
year = {2013},
date = {2013-01-01},
journal = {IEEE Transactions on Vehicular Technology},
volume = {62},
number = {7},
pages = {3188--3203},
abstract = {In modern multiuser communication systems, users are allowed to enter or leave the system at any given time. Thus, the number of active users is an unknown and time-varying parameter, and the performance of the system depends on how accurately this parameter is estimated over time. The so-called problem of user identification, which consists of determining the number and identities of users transmitting in a communication system, is usually solved prior to, and hence independently of, that posed by the detection of the transmitted data. Since both problems are tightly connected, a joint solution is desirable. In this paper, we focus on direct-sequence (DS) code-division multiple-access (CDMA) systems and derive, within a Bayesian framework, different receivers that cope with an unknown and time-varying number of users while performing joint channel estimation and data detection. The main feature of these receivers, compared with other recently proposed schemes for user activity detection, is that they are natural extensions of existing maximum a posteriori (MAP) equalizers for multiple-input-multiple-output communication channels. We assess the validity of the proposed receivers, including their reliability in detecting the number and identities of active users, by way of computer simulations.},
keywords = {Activity detection, activity tracking, Bayes methods, Bayesian framework, Channel estimation, code division multiple access, code-division multiple access (CDMA), computer simulations, data detection, direct sequence code division multiple-access, DS-CDMA systems, Equations, joint channel and data estimation, joint channel estimation, Joints, MAP equalizers, Mathematical model, maximum a posteriori, MIMO communication, Multiaccess communication, multiple-input-multiple-output communication chann, multiuser communication systems, per-survivor processing (PSP), radio receivers, Receivers, sequential Monte Carlo (SMC) methods, time-varying number, time-varying parameter, Vectors, wireless channels},
pubstate = {published},
tppubtype = {article}
}
Bravo-Santos, Ángel M
Polar Codes for Gaussian Degraded Relay Channels Artículo de revista
En: IEEE Communications Letters, vol. 17, no 2, pp. 365–368, 2013, ISSN: 1089-7798.
Resumen | Enlaces | BibTeX | Etiquetas: channel capacity, Channel Coding, Decoding, Encoding, Gaussian channels, Gaussian degraded relay channel, Gaussian noise, Gaussian-degraded relay channels, log-likelihood expression, Markov coding, Noise, parity check codes, polar code detector, polar codes, relay-destination link, Relays, Vectors
@article{Bravo-Santos2013,
title = {Polar Codes for Gaussian Degraded Relay Channels},
author = {\'{A}ngel M Bravo-Santos},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6412681},
issn = {1089-7798},
year = {2013},
date = {2013-01-01},
journal = {IEEE Communications Letters},
volume = {17},
number = {2},
pages = {365--368},
publisher = {IEEE},
abstract = {In this paper we apply polar codes for the Gaussian degraded relay channel. We study the conditions to be satisfied by the codes and provide an efficient method for constructing them. The relay-destination link is special because the noise is the sum of two components: the Gaussian noise and the signals from the source. We study this link and provide the log-likelihood expression to be used by the polar code detector. We perform simulations of the channel and the results show that polar codes of high rate and large codeword length are closer to the theoretical limit than other good codes.},
keywords = {channel capacity, Channel Coding, Decoding, Encoding, Gaussian channels, Gaussian degraded relay channel, Gaussian noise, Gaussian-degraded relay channels, log-likelihood expression, Markov coding, Noise, parity check codes, polar code detector, polar codes, relay-destination link, Relays, Vectors},
pubstate = {published},
tppubtype = {article}
}
2012
Florentino-Liaño, Blanca; O'Mahony, Niamh; Artés-Rodríguez, Antonio
Long Term Human Activity Recognition with Automatic Orientation Estimation Proceedings Article
En: 2012 IEEE International Workshop on Machine Learning for Signal Processing, pp. 1–6, IEEE, Santander, 2012, ISSN: 1551-2541.
Resumen | Enlaces | BibTeX | Etiquetas: 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
@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}
}
Maiz, Cristina S; Molanes-Lopez, Elisa M; Miguez, Joaquin; Djuric, Petar M
A Particle Filtering Scheme for Processing Time Series Corrupted by Outliers Artículo de revista
En: IEEE Transactions on Signal Processing, vol. 60, no 9, pp. 4611–4627, 2012, ISSN: 1053-587X.
Resumen | Enlaces | BibTeX | Etiquetas: Kalman filters, Mathematical model, nonlinear state space model, Outlier detection, prediction theory, predictive distribution, Probability density function, State-space methods, state-space models, statistical distributions, Target tracking, time serie processing, Vectors, Yttrium
@article{Maiz2012,
title = {A Particle Filtering Scheme for Processing Time Series Corrupted by Outliers},
author = {Cristina S Maiz and Elisa M Molanes-Lopez and Joaquin Miguez and Petar M Djuric},
url = {http://www.tsc.uc3m.es/~jmiguez/papers/P34_2012_A Particle Filtering Scheme for Processing Time Series Corrupted by Outliers.pdf http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6203606},
issn = {1053-587X},
year = {2012},
date = {2012-01-01},
journal = {IEEE Transactions on Signal Processing},
volume = {60},
number = {9},
pages = {4611--4627},
abstract = {The literature in engineering and statistics is abounding in techniques for detecting and properly processing anomalous observations in the data. Most of these techniques have been developed in the framework of static models and it is only in recent years that we have seen attempts that address the presence of outliers in nonlinear time series. For a target tracking problem described by a nonlinear state-space model, we propose the online detection of outliers by including an outlier detection step within the standard particle filtering algorithm. The outlier detection step is implemented by a test involving a statistic of the predictive distribution of the observations, such as a concentration measure or an extreme upper quantile. We also provide asymptotic results about the convergence of the particle approximations of the predictive distribution (and its statistics) and assess the performance of the resulting algorithms by computer simulations of target tracking problems with signal power observations.},
keywords = {Kalman filters, Mathematical model, nonlinear state space model, Outlier detection, prediction theory, predictive distribution, Probability density function, State-space methods, state-space models, statistical distributions, Target tracking, time serie processing, Vectors, Yttrium},
pubstate = {published},
tppubtype = {article}
}
2011
Ruiz, Francisco J R; Perez-Cruz, Fernando
Zero-Error Codes for the Noisy-Typewriter Channel Proceedings Article
En: 2011 IEEE Information Theory Workshop, pp. 495–497, IEEE, Paraty, 2011, ISBN: 978-1-4577-0437-6.
Resumen | Enlaces | BibTeX | Etiquetas: 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
@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}
}
Achutegui, Katrin; Miguez, Joaquin
A Parallel Resampling Scheme and its Application to Distributed Particle Filtering in Wireless Networks Proceedings Article
En: 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 81–84, IEEE, San Juan, 2011, ISBN: 978-1-4577-2105-2.
Resumen | Enlaces | BibTeX | Etiquetas: Approximation algorithms, Approximation methods, Artificial neural networks, distributed resampling, DRNA technique, Markov processes, nonproportional allocation algorithm, parallel resampling scheme, PF, quantization, Signal processing, Vectors, Wireless sensor network, Wireless Sensor Networks, WSN
@inproceedings{Achutegui2011,
title = {A Parallel Resampling Scheme and its Application to Distributed Particle Filtering in Wireless Networks},
author = {Katrin Achutegui and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6136051},
isbn = {978-1-4577-2105-2},
year = {2011},
date = {2011-01-01},
booktitle = {2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)},
pages = {81--84},
publisher = {IEEE},
address = {San Juan},
abstract = {We address the design of a particle filter (PF) that can be implemented in a distributed manner over a network of wireless sensor nodes, each of them collecting their own local data. This is a problem that has received considerable attention lately and several methods based on consensus, the transmission of likelihood information, the truncation and/or the quantization of data have been proposed. However, all existing schemes suffer from limitations related either to the amount of required communications among the nodes or the accuracy of the filter outputs. In this work we propose a novel distributed PF that is built around the distributed resampling with non-proportional allocation (DRNA) algorithm. This scheme guarantees the properness of the particle approximations produced by the filter and has been shown to be both efficient and accurate when compared with centralized PFs. The standard DRNA technique, however, places stringent demands on the communications among nodes that turn out impractical for a typical wireless sensor network (WSN). In this paper we investigate how to reduce this communication load by using (i) a random model for the spread of data over the WSN and (ii) methods that enable the out-of-sequence processing of sensor observations. A simple numerical illustration of the performance of the new algorithm compared with a centralized PF is provided.},
keywords = {Approximation algorithms, Approximation methods, Artificial neural networks, distributed resampling, DRNA technique, Markov processes, nonproportional allocation algorithm, parallel resampling scheme, PF, quantization, Signal processing, Vectors, Wireless sensor network, Wireless Sensor Networks, WSN},
pubstate = {published},
tppubtype = {inproceedings}
}
Asyhari, Taufiq A; Koch, Tobias; i Fabregas, Albert Guillen
Nearest Neighbour Decoding with Pilot-Assisted Channel Estimation for Fading Multiple-Access Channels Proceedings Article
En: 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pp. 1686–1693, IEEE, Allerton, 2011, ISBN: 978-1-4577-1818-2.
Resumen | Enlaces | BibTeX | Etiquetas: Channel estimation, Decoding, Fading, fading channels, fading multiple-access channels, MIMO, MIMO communication, multi-access systems, multiple-input multiple-output channel, nearest-neighbour decoding, noncoherent MIMO fading MAC channel, pilot-assisted channel estimation, Receiving antennas, Signal to noise ratio, signal-to-noise ratio, Time division multiple access, Vectors
@inproceedings{Asyhari2011a,
title = {Nearest Neighbour Decoding with Pilot-Assisted Channel Estimation for Fading Multiple-Access Channels},
author = {Taufiq A Asyhari and Tobias Koch and Albert Guillen i Fabregas},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6120371},
isbn = {978-1-4577-1818-2},
year = {2011},
date = {2011-01-01},
booktitle = {2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton)},
pages = {1686--1693},
publisher = {IEEE},
address = {Allerton},
abstract = {This paper studies a noncoherent multiple-input multiple-output (MIMO) fading multiple-access channel (MAC). The rate region that is achievable with nearest neighbour decoding and pilot-assisted channel estimation is analysed and the corresponding pre-log region, defined as the limiting ratio of the rate region to the logarithm of the signal-to-noise ratio (SNR) as the SNR tends to infinity, is determined.},
keywords = {Channel estimation, Decoding, Fading, fading channels, fading multiple-access channels, MIMO, MIMO communication, multi-access systems, multiple-input multiple-output channel, nearest-neighbour decoding, noncoherent MIMO fading MAC channel, pilot-assisted channel estimation, Receiving antennas, Signal to noise ratio, signal-to-noise ratio, Time division multiple access, Vectors},
pubstate = {published},
tppubtype = {inproceedings}
}
2007
Leiva-Murillo, Jose M; Artés-Rodríguez, Antonio
Maximization of Mutual Information for Supervised Linear Feature Extraction Artículo de revista
En: IEEE Transactions on Neural Networks, vol. 18, no 5, pp. 1433–1441, 2007, ISSN: 1045-9227.
Resumen | Enlaces | BibTeX | Etiquetas: Algorithms, Artificial Intelligence, Automated, component-by-component gradient-ascent method, Computer Simulation, Data Mining, Entropy, Feature extraction, gradient methods, gradient-based entropy, Independent component analysis, Information Storage and Retrieval, information theory, Iron, learning (artificial intelligence), Linear discriminant analysis, Linear Models, Mutual information, Optimization methods, Pattern recognition, Reproducibility of Results, Sensitivity and Specificity, supervised linear feature extraction, Vectors
@article{Leiva-Murillo2007,
title = {Maximization of Mutual Information for Supervised Linear Feature Extraction},
author = {Jose M Leiva-Murillo and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=4298118},
issn = {1045-9227},
year = {2007},
date = {2007-01-01},
journal = {IEEE Transactions on Neural Networks},
volume = {18},
number = {5},
pages = {1433--1441},
publisher = {IEEE},
abstract = {In this paper, we present a novel scheme for linear feature extraction in classification. The method is based on the maximization of the mutual information (MI) between the features extracted and the classes. The sum of the MI corresponding to each of the features is taken as an heuristic that approximates the MI of the whole output vector. Then, a component-by-component gradient-ascent method is proposed for the maximization of the MI, similar to the gradient-based entropy optimization used in independent component analysis (ICA). The simulation results show that not only is the method competitive when compared to existing supervised feature extraction methods in all cases studied, but it also remarkably outperform them when the data are characterized by strongly nonlinear boundaries between classes.},
keywords = {Algorithms, Artificial Intelligence, Automated, component-by-component gradient-ascent method, Computer Simulation, Data Mining, Entropy, Feature extraction, gradient methods, gradient-based entropy, Independent component analysis, Information Storage and Retrieval, information theory, Iron, learning (artificial intelligence), Linear discriminant analysis, Linear Models, Mutual information, Optimization methods, Pattern recognition, Reproducibility of Results, Sensitivity and Specificity, supervised linear feature extraction, Vectors},
pubstate = {published},
tppubtype = {article}
}