Compressed Sensing: Theory and Applications

Compressed Sensing: Theory and Applications

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Cambridge University Press, 5/17/2012
EAN 9781107005587, ISBN10: 1107005582

Hardcover, 558 pages, 24.7 x 17.4 x 3 cm
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 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.

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.