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Compressed Sensing : Theory and Applications by Gitta Kutyniok (2012, Hardcover)
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About this product
Product Identifiers
PublisherCambridge University Press
ISBN-101107005582
ISBN-139781107005587
eBay Product ID (ePID)110851816
Product Key Features
Number of Pages558 Pages
Publication NameCompressed Sensing : Theory and Applications
LanguageEnglish
SubjectSignals & Signal Processing, Mathematical Analysis
Publication Year2012
TypeTextbook
AuthorGitta Kutyniok
Subject AreaMathematics, Technology & Engineering
FormatHardcover
Dimensions
Item Height1.2 in
Item Weight43 Oz
Item Length9.8 in
Item Width6.9 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN2011-040519
Dewey Edition23
Reviews'… a charming encouragement to fascinating scientific adventure for talented students. Also … a solid reference platform for researchers in many fields.' Artur Przelaskowski, IEEE Communications Magazine, "It looks like a charming encouragement to fascinating scientific adventure for talented students. Also, the book provides a solid reference platform for researchers in many fields..." - Artur Przelaskowski, IEEE Communications Magazine, April 2013
IllustratedYes
Dewey Decimal621.382/2
Table Of Content1. 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.
SynopsisCompressed sensing has rapidly become a key concept in various areas of applied mathematics, computer science and electrical engineering. This book highlights theoretical advances and applications in this area. Ideal for both researchers and graduate students seeking an understanding of the potential of compressed sensing., 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., 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 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.
LC Classification NumberQA601 .C638 2012
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