«The paper «Multiantenna GLR Detection of Rank-One Signals with a Known Power Spectral Shape under Spatially Uncorrelated Noise» by J. Sala-Alvarez, G. Vazquez-Vilar, R. Lopez-Valcarce, S. Sedighi and A. Taherpour has been accepted for publication in the IEEE Transactions on Signal Processing. Abstract: We establish the generalized likelihood ratio (GLR) test for a Gaussian signal of known power spectral shape and…
The following paper from the group has been published in the Proceedings of the IEEE: G. Durisi, T. Koch, and P. Popovski, «Towards massive, ultrareliable, and low-latency wireless communication with short packets,» Proceedings of the IEEE, Vol. 104, No. 9, September 2016. Abstract: Most of the recent advances in the design of high-speed wireless systems…
Join us for an invited talk with Bernhard Geiger (TU Munich, Germany) Title: “Information-Theory for Markov Aggregation and Clustering” Event Date: September 6, 12:00-13:00. Location: Room 4.2.E03; Torres Quevedo Building; Leganés Campus; Universidad Carlos III de Madrid. Abstract: In many scientific disciplines, Markov models are too large to allow efficient simulation or parameter estimation –…
The paper “Effective Sample Size for Importance Sampling Based on the Discrepancy Measures” by L. Martino, V. Elvira, and F. Louzada has been accepted for publication in Signal Processing. Abstract: The Effective Sample Size (ESS) is an important measure of efficiency of Monte Carlo methods such as Markov Chain Monte Carlo (MCMC) and Importance Sampling (IS) techniques.…
The paper “Heretical Multiple Importance Sampling” by V. Elvira, L. Martino, D. Luengo, and M. Bugallo has been accepted for publication in IEEE Signal Processing Letters. Abstract: Multiple Importance Sampling (MIS) methods approximate moments of complicated distributions by drawing samples from a set of proposal distributions. Several ways to compute the importance weights assigned to each…
The paper “Orthogonal Parallel MCMC Methods for Sampling and Optimization” by L. Martino, V. Elvira, D. Luengo, J. Corander, and F. Louzada has been accepted for publication in Digital Signal Processing. Abstract: Monte Carlo (MC) methods are widely used in statistics, signal processing and machine learning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC)…
The paper “Improving Population Monte Carlo: Alternative Weighting and Resampling Schemes” by V. Elvira, L. Martino, D. Luengo, and M. Bugallo has been accepted for publication in Signal Processing Abstract: Population Monte Carlo (PMC) sampling methods are powerful tools for approximating distributions of static unknowns given a set of observations. These methods are iterative in nature: at…
Join us for an invited talk with Elaheh Moradi (Tampere University of Technology, Finland) Title: “Machine learning approaches for structural Brain MRI” Event Date: July, 5. 12:00-13:00. Location: 7.1.J07 Room; Juan Benet Building; Leganés Campus; Universidad Carlos III de Madrid. Abstract: For investigating brain abnormalities in various brain regions, machine learning approaches are used extensively in…
The paper “Multi-Class Source-Channel Coding” by I. E. Bocharova, A. Guillen i Fabregas, B. D. Kudryashov, A. Martinez, A. Tauste Campo and G. Vazquez-Vilar has been accepted for publication in the IEEE Transactions on Information Theory. Abstract: This paper studies an almost-lossless source-channel coding scheme in which source messages are assigned to different classes and encoded with…
The following group members will be presenting their work at the upcoming 2016 IEEE International Symposium on Information Theory Tobias Koch and Gonzalo Vazquez-Vilar, «A General Rate-Distortion Converse Bound for Entropy-Constrained Scalar Quantization” Markus Stinner (TUM), Luca Barletta (Politecnico di Milano) and Pablo M. Olmos, «Finite-Length Scaling Based on Belief Propagation for Spatially Coupled LDPC…