Compressed Sensing (eBook, PDF)

Theory and Applications
Redaktion: Eldar, Yonina C.

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 17. Mai 2012
• Englisch
• ISBN-13: 9781139334938
• Artikelnr.: 38277060

Inhaltsangabe

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.