| 000 | 03305nam a2200265 a 4500 | ||
|---|---|---|---|
| 001 | vtls000060746 | ||
| 003 | KUKTEM | ||
| 005 | 20251125093125.0 | ||
| 008 | 120531t2012 enka f 001 0 eng d | ||
| 020 | _a9781107005587 | ||
| 039 | 9 |
_a201211051549 _bida _c201209111257 _dasmadi _y201205311202 _zsafura |
|
| 040 | _aUMP | ||
| 090 | _aQA601 .C66 2012 | ||
| 245 | 0 | 0 |
_aCompressed sensing : _btheory and applications / _cedited by Yonina C. Eldar, Gitta Kutyniok |
| 260 |
_aCambridge, UK : _bCambridge University Press, _c2012 |
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| 300 |
_axii, 544 p. : _bill. ; _c26 cm. |
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| 504 | _aIncludes bibliographical references and index | ||
| 505 | 0 | _aMachine 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"-- _cProvided by publisher |
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| 650 | 0 | _aSignal processing | |
| 650 | 0 | _aWavelets (Mathematics) | |
| 700 | 1 | _aEldar, Yonina C. | |
| 700 | 1 | _aKutyniok, Gitta | |
| 999 |
_aVIRTUA40 _c61612 _d61618 |
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