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Book
Compressed sensing & sparse filtering
Authors: --- ---
ISBN: 3642383971 364238398X Year: 2014 Publisher: Heidelberg, Germany : Springer,

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Abstract

This book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and limited data. One such trend that recently gained popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals in nature are nearly sparse (or compressible, as they are normally referred to) in some domain, and consequently they can be reconstructed to within high accuracy from far fewer observations than traditionally held to be necessary.  Apart from compressed sensing this book contains other related approaches. Each methodology has its own formalities for dealing with such problems. As an example, in the Bayesian approach, sparseness promoting priors such as Laplace and Cauchy are normally used for penalising improbable model variables, thus promoting low complexity solutions. Compressed sensing techniques and homotopy-type solutions, such as the LASSO, utilise l1-norm penalties for obtaining sparse solutions using fewer observations than conventionally needed. The book emphasizes on the role of sparsity as a machinery for promoting low complexity representations and likewise its connections to variable selection and dimensionality reduction in various engineering problems.  This book is intended for researchers, academics and practitioners with interest in various aspects and applications of sparse signal processing.  .


Digital
Compressed Sensing & Sparse Filtering
Authors: --- ---
ISBN: 9783642383984 Year: 2014 Publisher: Berlin, Heidelberg Springer

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Abstract

This book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and limited data. One such trend that recently gained popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals in nature are nearly sparse (or compressible, as they are normally referred to) in some domain, and consequently they can be reconstructed to within high accuracy from far fewer observations than traditionally held to be necessary.  Apart from compressed sensing this book contains other related approaches. Each methodology has its own formalities for dealing with such problems. As an example, in the Bayesian approach, sparseness promoting priors such as Laplace and Cauchy are normally used for penalising improbable model variables, thus promoting low complexity solutions. Compressed sensing techniques and homotopy-type solutions, such as the LASSO, utilise l1-norm penalties for obtaining sparse solutions using fewer observations than conventionally needed. The book emphasizes on the role of sparsity as a machinery for promoting low complexity representations and likewise its connections to variable selection and dimensionality reduction in various engineering problems.  This book is intended for researchers, academics and practitioners with interest in various aspects and applications of sparse signal processing.  .


Digital
Monte Carlo Methods and Applications : Proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29 - September 2, 2011, Borovets, Bulgaria
Authors: --- --- --- --- --- et al.
ISBN: 9783110293586 9783110293470 Year: 2012 Publisher: Berlin ;; Boston De Gruyter

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Mathematics


Book
Monte Carlo Methods and Applications
Authors: --- --- --- --- --- et al.
ISBN: 3110293587 9783110293586 3110293471 9783110293470 9783110293470 1299719864 Year: 2012 Publisher: Berlin Boston

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Abstract

This is the proceedings of the "8th IMACS Seminar on Monte Carlo Methods" held from August 29 to September 2, 2011 in Borovets, Bulgaria, and organized by the Institute of Information and Communication Technologies of the Bulgarian Academy of Sciences in cooperation with the International Association for Mathematics and Computers in Simulation (IMACS). Included are 24 papers which cover all topics presented in the sessions of the seminar: stochastic computation and complexity of high dimensional problems, sensitivity analysis, high-performance computations for Monte Carlo applications, stochastic metaheuristics for optimization problems, sequential Monte Carlo methods for large-scale problems, semiconductor devices and nanostructures. The history of the IMACS Seminar on Monte Carlo Methods goes back to April 1997 when the first MCM Seminar was organized in Brussels: 1st IMACS Seminar, 1997, Brussels, Belgium2nd IMACS Seminar, 1999, Varna, Bulgaria 3rd IMACS Seminar, 2001, Salzburg, Austria 4th IMACS Seminar, 2003, Berlin, Germany 5th IMACS Seminar, 2005, Tallahassee, USA6th IMACS Seminar, 2007, Reading, UK7th IMACS Seminar, 2009, Brussels, Belgium8th IMACS Seminar, 2011, Borovets, Bulgaria

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