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Compressed sensing : = theory and ap...

Eldar, Yonina C.

Compressed sensing : = theory and applications /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Compressed sensing :/ edited by Yonina C. Eldar, Gitta Kutyniok.
Reminder of title:	theory and applications /
other author:	Kutyniok, Gitta.
Published:	Cambridge ;Cambridge University Press, : 2012.,
Description:	xii, 544 p. :ill. ; : 26 cm.;
Subject:	Wavelets (Mathematics) -
Online resource:	http://assets.cambridge.org/97811070/05587/cover/9781107005587.jpg
Online resource:	http://www.loc.gov/catdir/enhancements/fy1117/2011040519-b.html
Online resource:	http://www.loc.gov/catdir/enhancements/fy1117/2011040519-d.html
Online resource:	http://www.loc.gov/catdir/enhancements/fy1117/2011040519-t.html
ISBN:	9781107005587 (cloth) :

Compressed sensing : = theory and applications /
Compressed sensing :theory and applications /edited by Yonina C. Eldar, Gitta Kutyniok. - Cambridge ;Cambridge University Press,2012. - xii, 544 p. :ill. ;26 cm.

Includes bibliographical references and index.

Machine generated contents note: 1. Introduction to compressed sensing Mark A. Davenport, Marco F. Duarte, Yonina C. Eldar and Gitta Kutyniok; 2. Second generation sparse modeling: structured and collaborative signal analysis Alexey Castrodad, Ignacio Ramirez, Guillermo Sapiro, Pablo Sprechmann and Guoshen Yu; 3. Xampling: compressed sensing of analog signals Moshe Mishali and Yonina C. Eldar; 4. Sampling at the rate of innovation: theory and applications Jose Antonia Uriguen, Yonina C. Eldar, Pier Luigi Dragotta and Zvika Ben-Haim; 5. Introduction to the non-asymptotic analysis of random matrices Roman Vershynin; 6. Adaptive sensing for sparse recovery Jarvis Haupt and Robert Nowak; 7. Fundamental thresholds in compressed sensing: a high-dimensional geometry approach Weiyu Xu and Babak Hassibi; 8. Greedy algorithms for compressed sensing Thomas Blumensath, Michael E. Davies and Gabriel Rilling; 9. Graphical models concepts in compressed sensing Andrea Montanari; 10. Finding needles in compressed haystacks Robert Calderbank, Sina Jafarpour and Jeremy Kent; 11. Data separation by sparse representations Gitta Kutyniok; 12. Face recognition by sparse representation Arvind Ganesh, Andrew Wagner, Zihan Zhou, Allen Y. Yang, Yi Ma and John Wright.

"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"--

ISBN: 9781107005587 (cloth) :NT2898

LCCN: 2011040519Subjects--Topical Terms:

528163
Wavelets (Mathematics)

LC Class. No.: QA601 / .C638 2012

Dewey Class. No.: 621.382/2

Compressed sensing : = theory and applications /
LDR:03505cam a2200277 a 4500 001 715229
003 DLC
005 20121003103503.0
008 121122s2012 enka b 001 0 eng
010 $a 2011040519
020 $a 9781107005587 (cloth) : $c NT2898
020 $a 1107005582 (cloth)
035 $a 2011040519
040 $a DLC $c DLC $d DLC $d NFU
042 $a pcc
050 0 0 $a QA601 $b .C638 2012
082 0 0 $a 621.382/2 $2 23
245 0 0 $a Compressed sensing : $b theory and applications / $c edited by Yonina C. Eldar, Gitta Kutyniok.
260 $a Cambridge ; $a New York : $c 2012. $b Cambridge University Press,
300 $a xii, 544 p. : $b ill. ; $c 26 cm.
504 $a Includes bibliographical references and index.
505 8 $a Machine generated contents note: 1. Introduction to compressed sensing Mark A. Davenport, Marco F. Duarte, Yonina C. Eldar and Gitta Kutyniok; 2. Second generation sparse modeling: structured and collaborative signal analysis Alexey Castrodad, Ignacio Ramirez, Guillermo Sapiro, Pablo Sprechmann and Guoshen Yu; 3. Xampling: compressed sensing of analog signals Moshe Mishali and Yonina C. Eldar; 4. Sampling at the rate of innovation: theory and applications Jose Antonia Uriguen, Yonina C. Eldar, Pier Luigi Dragotta and Zvika Ben-Haim; 5. Introduction to the non-asymptotic analysis of random matrices Roman Vershynin; 6. Adaptive sensing for sparse recovery Jarvis Haupt and Robert Nowak; 7. Fundamental thresholds in compressed sensing: a high-dimensional geometry approach Weiyu Xu and Babak Hassibi; 8. Greedy algorithms for compressed sensing Thomas Blumensath, Michael E. Davies and Gabriel Rilling; 9. Graphical models concepts in compressed sensing Andrea Montanari; 10. Finding needles in compressed haystacks Robert Calderbank, Sina Jafarpour and Jeremy Kent; 11. Data separation by sparse representations Gitta Kutyniok; 12. Face recognition by sparse representation Arvind Ganesh, Andrew Wagner, Zihan Zhou, Allen Y. Yang, Yi Ma and John Wright.
520 $a "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"-- $c Provided by publisher.
650 0 $a Wavelets (Mathematics) $3 528163
650 0 $a Signal processing. $3 561459
700 1 $a Kutyniok, Gitta. $3 848850
700 1 $a Eldar, Yonina C. $3 719792
856 4 2 $3 Cover image $u http://assets.cambridge.org/97811070/05587/cover/9781107005587.jpg
856 4 2 $3 Contributor biographical information $u http://www.loc.gov/catdir/enhancements/fy1117/2011040519-b.html
856 4 2 $3 Publisher description $u http://www.loc.gov/catdir/enhancements/fy1117/2011040519-d.html
856 4 1 $3 Table of contents only $u http://www.loc.gov/catdir/enhancements/fy1117/2011040519-t.html