Editors:
- Presents fundamental concepts, methods and algorithms able to cope with undersampled data
- Introduces compressive sampling, called also compressed sensing.
- Written by well-known experts in the field
- Includes supplementary material: sn.pub/extras
Part of the book series: Signals and Communication Technology (SCT)
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Table of contents (15 chapters)
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Front Matter
About this book
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 thanconventionally 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.
Reviews
From the reviews:
“This book reports on the application of compressed sensing. … This book presents cutting-edge research on one of the newest signal processing disciplines. It should be of great value to research scientists in related fields, and it could help research and development engineers evaluate the impact these new methods could have in their work.” (Vladimir Botchev, Computing Reviews, February, 2014)Editors and Affiliations
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Department of Mechanical and Aerospace Engineering, Nanyang Technical University, Singapore, Singapore
Avishy Y. Carmi
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School of Computing and Communications, Lancaster University, Lancaster, United Kingdom
Lyudmila Mihaylova
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Department of Engineering, University of Cambridge, Cambridge, United Kingdom
Simon J. Godsill
Bibliographic Information
Book Title: Compressed Sensing & Sparse Filtering
Editors: Avishy Y. Carmi, Lyudmila Mihaylova, Simon J. Godsill
Series Title: Signals and Communication Technology
DOI: https://doi.org/10.1007/978-3-642-38398-4
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2014
Hardcover ISBN: 978-3-642-38397-7Published: 25 September 2013
Softcover ISBN: 978-3-662-50894-7Published: 27 August 2016
eBook ISBN: 978-3-642-38398-4Published: 13 September 2013
Series ISSN: 1860-4862
Series E-ISSN: 1860-4870
Edition Number: 1
Number of Pages: XII, 502
Number of Illustrations: 135 b/w illustrations
Topics: Signal, Image and Speech Processing, Numeric Computing, Mathematics of Algorithmic Complexity, Complexity