Compressed Sensing Theory and Applications

ISBN-10: 1107005582
ISBN-13: 9781107005587
Edition: 2012
List price: $215.00 Buy it from $94.99
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Description: Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics, statistics and computer science. This book provides the first detailed introduction to the subject,  More...

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Book details

List price: $215.00
Copyright year: 2012
Publisher: Cambridge University Press
Publication date: 5/17/2012
Binding: Hardcover
Pages: 558
Size: 6.00" wide x 10.00" long x 1.00" tall
Weight: 2.706
Language: English

Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics, statistics and computer science. This book provides the first detailed introduction to the subject, highlighting recent theoretical advances and a range of applications, as well as outlining numerous remaining research challenges. After a thorough review of the basic theory, many cutting-edge techniques are presented, including advanced signal modeling, sub-Nyquist sampling of analog signals, non-asymptotic analysis of random matrices, adaptive sensing, greedy algorithms and use of graphical models. All chapters are written by leading researchers in the field, and consistent style and notation are utilized throughout. Key background information and clear definitions make this an ideal resource for researchers, graduate students and practitioners wanting to join this exciting research area. It can also serve as a supplementary textbook for courses on computer vision, coding theory, signal processing, image processing and algorithms for efficient data processing.

Introduction to compressed sensing
Second generation sparse modeling: structured and collaborative signal analysis
Xampling: compressed sensing of analog signals
Sampling at the rate of innovation: theory and applications
Introduction to the non-asymptotic analysis of random matrices
Adaptive sensing for sparse recovery
Fundamental thresholds in compressed sensing: a high-dimensional geometry approach
Greedy algorithms for compressed sensing
Graphical models concepts in compressed sensing
Finding needles in compressed haystacks
Data separation by sparse representations
Face recognition by sparse representation

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