Name: Compressed Sensing & Sparse Filtering (Signals and Communication Technology) (en Inglés)
Price: 140.43 USD
Availability: InStock
Author: Carmi Avishy Y.,Mihaylova Lyudmila,Godsill Simon J.
ISBN: 9783642383977

portada Compressed Sensing & Sparse Filtering (Signals and Communication Technology) (en Inglés)

Compressed Sensing & Sparse Filtering (Signals and Communication Technology) (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Carmi Avishy Y.,Mihaylova Lyudmila,Godsill Simon J.

Idioma

Inglés

N° páginas

514

Encuadernación

Tapa Dura

ISBN13

9783642383977

N° edición

2014th

Compressed Sensing & Sparse Filtering (Signals and Communication Technology) (en Inglés)

Carmi Avishy Y.,Mihaylova Lyudmila,Godsill Simon J. (Autor) · Springer · Tapa Dura

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Reseña del libro "Compressed Sensing & Sparse Filtering (Signals and Communication Technology) (en Inglés)"

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.