Signals and Communication Technology

Compressed Sensing & Sparse Filtering

Editors: Carmi, Avishy Y., Mihaylova, Lyudmila S., Godsill, Simon J. (Eds.)

  • 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
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  • ISBN 978-3-642-38398-4
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Hardcover $249.99
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  • ISBN 978-3-642-38397-7
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Softcover $179.99
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  • Customers within the U.S. and Canada please contact Customer Service at 1-800-777-4643, Latin America please contact us at +1-212-460-1500 (Weekdays 8:30am – 5:30pm ET) to place your order.
  • Due: October 14, 2016
  • ISBN 978-3-662-50894-7
  • Free shipping for individuals worldwide
Rent the eBook  
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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 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.  

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)

Table of contents (15 chapters)

  • Introduction to Compressed Sensing and Sparse Filtering

    Carmi, Avishy Y. (et al.)

    Pages 1-23

  • The Geometry of Compressed Sensing

    Blumensath, Thomas

    Pages 25-75

  • Sparse Signal Recovery with Exponential-Family Noise

    Rish, Irina (et al.)

    Pages 77-93

  • Nuclear Norm Optimization and Its Application to Observation Model Specification

    Hao, Ning (et al.)

    Pages 95-122

  • Nonnegative Tensor Decomposition

    Hao, N. (et al.)

    Pages 123-148

Buy this book

eBook $139.00
price for USA (gross)
  • ISBN 978-3-642-38398-4
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $249.99
price for USA
  • ISBN 978-3-642-38397-7
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $179.99
price for USA
  • Customers within the U.S. and Canada please contact Customer Service at 1-800-777-4643, Latin America please contact us at +1-212-460-1500 (Weekdays 8:30am – 5:30pm ET) to place your order.
  • Due: October 14, 2016
  • ISBN 978-3-662-50894-7
  • Free shipping for individuals worldwide
Rent the eBook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
Compressed Sensing & Sparse Filtering
Editors
  • Avishy Y. Carmi
  • Lyudmila S. Mihaylova
  • Simon J. Godsill
Series Title
Signals and Communication Technology
Copyright
2014
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-642-38398-4
DOI
10.1007/978-3-642-38398-4
Hardcover ISBN
978-3-642-38397-7
Softcover ISBN
978-3-662-50894-7
Series ISSN
1860-4862
Edition Number
1
Number of Pages
XII, 502
Number of Illustrations and Tables
135 b/w illustrations
Topics