@inproceedings{Rodrigues2008,

title = {Multiple-Input Multiple-Output Gaussian Channels: Optimal Covariance for Non-Gaussian Inputs},

author = {Miguel R D Rodrigues and Fernando Perez-Cruz and Sergio Verdu},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4578704},

isbn = {978-1-4244-2269-2},

year  = {2008},

date = {2008-01-01},

booktitle = {2008 IEEE Information Theory Workshop},

pages = {445--449},

publisher = {IEEE},

address = {Porto},

abstract = {We investigate the input covariance that maximizes the mutual information of deterministic multiple-input multipleo-utput (MIMO) Gaussian channels with arbitrary (not necessarily Gaussian) input distributions, by capitalizing on the relationship between the gradient of the mutual information and the minimum mean-squared error (MMSE) matrix. We show that the optimal input covariance satisfies a simple fixed-point equation involving key system quantities, including the MMSE matrix. We also specialize the form of the optimal input covariance to the asymptotic regimes of low and high snr. We demonstrate that in the low-snr regime the optimal covariance fully correlates the inputs to better combat noise. In contrast, in the high-snr regime the optimal covariance is diagonal with diagonal elements obeying the generalized mercury/waterfilling power allocation policy. Numerical results illustrate that covariance optimization may lead to significant gains with respect to conventional strategies based on channel diagonalization followed by mercury/waterfilling or waterfilling power allocation, particularly in the regimes of medium and high snr.},

keywords = {Binary phase shift keying, covariance matrices, Covariance matrix, deterministic MIMO Gaussian channel, fixed-point equation, Gaussian channels, Gaussian noise, Information rates, intersymbol interference, least mean squares methods, Magnetic recording, mercury-waterfilling power allocation policy, MIMO, MIMO communication, minimum mean-squared error, MMSE, MMSE matrix, multiple-input multiple-output system, Multiple-Input Multiple-Output Systems, Mutual information, Optimal Input Covariance, Optimization, Telecommunications},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Vazquez2008a,

title = {A Per-Survivor Processing Algorithm for Maximum Likelihood Equalization of MIMO Channels with Unknown Order},

author = {Manuel A Vazquez and Joaquin Miguez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4475587},

isbn = {978-1-4244-1756-8},

year  = {2008},

date = {2008-01-01},

booktitle = {2008 International ITG Workshop on Smart Antennas},

pages = {387--391},

publisher = {IEEE},

address = {Vienna},

abstract = {In the equalization of frequency-selective multiple-input multiple-output (MIMO) channels it is usually assumed that the length of the channel impulse response (CIR), also referred to as the channel order, is known. However, this is not true in most practical situations and, in order to avoid the serious performance degradation that occurs when the CIR length is underestimated, a channel with "more than enough" taps is usually considered. This possibly means overestimating the channel order, and is not desirable since the computational complexity of maximum likelihood sequence detection (MLSD) in frequency-selective channels grows exponentially with the channel order. In addition to that, the higher the channel order considered, the more the number of channel coefficients that need to be estimated from the same set of observations. In this paper, we introduce an algorithm for MLSD that incorporates the full estimation of the MIMO CIR parameters, including its order. The proposed technique is based on the per survivor processing (PSP) methodology, it admits both blind and semiblind implementations, depending on the availability of pilot data, and is designed to work with time-selective channels. Besides the analytical derivation of the algorithm, we provide computer simulation results that illustrate the effectiveness of the resulting receiver.},

keywords = {Channel estimation, channel impulse response, computational complexity, Computer science education, Computer Simulation, Degradation, Frequency, frequency-selective multiple-input multiple-output, maximum likelihood detection, maximum likelihood equalization, maximum likelihood estimation, maximum likelihood sequence detection, maximum likelihood sequence estimation, MIMO, MIMO channels, MIMO communication, per-survivor processing algorithm, time-selective channels, Transmitting antennas},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Leiva-Murillo2008a,

title = {Algorithms for Gaussian Bandwidth Selection in Kernel Density Estimators},

author = {Jose M Leiva-Murillo and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://www.researchgate.net/publication/228859873_Algorithms_for_gaussian_bandwidth_selection_in_kernel_density_estimators},

year  = {2008},

date = {2008-01-01},

booktitle = {NIPS 2008, Workshop on Optimization for Machine Learning Vancouver},

address = {Vancouver},

abstract = {In this paper we study the classical statistical problem of choos-ing an appropriate bandwidth for Kernel Density Estimators. For the special case of Gaussian kernel, two algorithms are proposed for the spherical covariance matrix and for the general case, respec-tively. These methods avoid the unsatisfactory procedure of tuning the bandwidth while evaluating the likelihood, which is impractical with multivariate data in the general case. The convergence con-ditions are provided together with the algorithms proposed. We measure the accuracy of the models obtained by a set of classifica-tion experiments.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Mariode-Prado-Cumplido2008,

title = {SVM Discovery of Causation Direction by Machine Learning Techniques},

author = {Mario Mario de-Prado-Cumplido and Antonio Art\'{e}s-Rodr\'{i}guez},

year  = {2008},

date = {2008-01-01},

booktitle = {NIPS’08, Workshop on Causality},

address = {Vancouver},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{MartinezRuiz2008,

title = {Progressive Still Image Transmission over a Tactical Data Link Network},

author = {Manuel Martinez Ruiz and Antonio Art\'{e}s-Rodr\'{i}guez and R Sabatini},

year  = {2008},

date = {2008-01-01},

booktitle = {RTO 2008 Information Systems Technology Panel (IST) Symposium},

address = {Praga},

abstract = {Future military communications will be required to provide higher data capacity and wideband in real time, greater flexibility, reliability, robustness and seamless networking capabilities. The next generation of communication systems and standards should be able to outperform in a littoral combat environment with a high density of civilian emissions and “ad-hoc” spot jammers. In this operational context it is extremely important to ensure the proper performance of the information grid and to provide not all the available but only the required information in real time either by broadcasting or upon demand, with the best possible “quality of service”. Existing tactical data link systems and standards have being designed to convey mainly textual information such as surveillance and identification data, electronic warfare parameters, aircraft control information, coded voice. The future tactical data link systems and standards should take into consideration the multimedia nature of most of the dispersed and “fuzzy” information available in the battlefield to correlate the ISR components in a way to better contribute to the Network Centric Operations. For this to be accomplished new wideband coalition waveforms should be developed and new coding and image compression standards should be taken into account, such as MPEG-7 (Multimedia Content Description Interface), MPEG-21, JPEG2000 and many others. In the meantime it is important to find new applications for the current tactical data links in order to better exploit their capabilities and to overcome or minimize their limitations.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Bravo-Santos2008,

title = {Multireception Systems in Mobile Environments},

author = {\'{A}ngel M Bravo-Santos},

year  = {2008},

date = {2008-01-01},

booktitle = {2008 International Workshop on Advances in Communications},

address = {Victoria BC},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Plata-Chaves2008,

title = {Decentralized Detection in a Dense Wireless Sensor Network with Correlated Observations},

author = {Jorge Plata-Chaves and Marcelino L\'{a}zaro and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://www.dcc.fc.up.pt/wits08/wits-advance-program.pdf},

year  = {2008},

date = {2008-01-01},

booktitle = {International Workshop on Information Theory for Sensor Networks (WITS 2008)},

address = {Santorini},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Santiago-Mozos2008,

title = {On the Uncertainty in Sequential Hypothesis Testing},

author = {Ricardo Santiago-Mozos and R Fernandez-Lorenzana and Fernando Perez-Cruz and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=4541223},

isbn = {978-1-4244-2002-5},

year  = {2008},

date = {2008-01-01},

booktitle = {2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro},

pages = {1223--1226},

publisher = {IEEE},

address = {Paris},

abstract = {We consider the problem of sequential hypothesis testing when the exact pdfs are not known but instead a set of iid samples are used to describe the hypotheses. We modify the classical test by introducing a likelihood ratio interval which accommodates the uncertainty in the pdfs. The test finishes when the whole likelihood ratio interval crosses one of the thresholds and reduces to the classical test as the number of samples to describe the hypotheses tend to infinity. We illustrate the performance of this test in a medical image application related to tuberculosis diagnosis. We show in this example how the test confidence level can be accurately determined.},

keywords = {binary hypothesis test, Biomedical imaging, Detectors, H infinity control, likelihood ratio, Medical diagnostic imaging, medical image application, medical image processing, Medical tests, patient diagnosis, Probability, Random variables, Sequential analysis, sequential hypothesis testing, sequential probability ratio test, Signal processing, Testing, tuberculosis diagnosis, Uncertainty},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Vila-Forcen2008,

title = {Compressive Sensing Detection of Stochastic Signals},

author = {J E Vila-Forcen and Antonio Art\'{e}s-Rodr\'{i}guez and J Garcia-Frias},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4558656},

isbn = {978-1-4244-2246-3},

year  = {2008},

date = {2008-01-01},

booktitle = {2008 42nd Annual Conference on Information Sciences and Systems},

pages = {956--960},

publisher = {IEEE},

address = {Princeton},

abstract = {Inspired by recent work in compressive sensing, we propose a framework for the detection of stochastic signals from optimized projections. In order to generate a good projection matrix, we use dimensionality reduction techniques based on the maximization of the mutual information between the projected signals and their corresponding class labels. In addition, classification techniques based on support vector machines (SVMs) are applied for the final decision process. Simulation results show that the realizations of the stochastic process are detected with higher accuracy and lower complexity than a scheme performing signal reconstruction first, followed by detection based on the reconstructed signal.},

keywords = {Additive white noise, AWGN, compressive sensing detection, dimensionality reduction techniques, Distortion measurement, Gaussian noise, matrix algebra, Mutual information, optimized projections, projection matrix, signal detection, Signal processing, signal reconstruction, Stochastic processes, stochastic signals, Support vector machine classification, Support vector machines, SVM},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Perez-Cruz2008b,

title = {Estimation of Information Theoretic Measures for Continuous Random Variables},

author = {Fernando Perez-Cruz},

url = {http://papers.nips.cc/paper/3417-estimation-of-information-theoretic-measures-for-continuous-random-variables},

year  = {2008},

date = {2008-01-01},

booktitle = {Advances in Neural Information Processing Systems},

pages = {1257--1264},

address = {Vancouver},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}