000			03811 am a2200385 i 4500
001			42780
008			111003s2012 enka b 001 0 eng
010			_a 2011040519
020			_a9781107005587 _qhardcover
035			_a(OCoLC)
040			_aDLC _cDLC _dDLC _dBAUN _erda _beng
049			_aBAUN_MERKEZ
050	0	0	_aQA601 _b.C638 2012
082	0	0	_223
245	0	0	_aCompressed sensing : _btheory and applications / _cedited by Yonina C. Eldar, Gitta Kutyniok.
264		1	_aCambridge ; _aNew York : _bCambridge University Press, _c[2012]
264		4	_c©2012
300			_axii, 544 pages : _billustrations ; _c26 cm.
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
504			_aIncludes bibliographical references and index.
505	8		_tMachine generated contents note _t1. Introduction to compressed sensing Mark A. Davenport, Marco F. Duarte, Yonina C. Eldar and Gitta Kutyniok; 2. Second generation sparse modeling _tstructured and collaborative signal analysis Alexey Castrodad, Ignacio Ramirez, Guillermo Sapiro, Pablo Sprechmann and Guoshen Yu; 3. Xampling _tcompressed sensing of analog signals Moshe Mishali and Yonina C. Eldar; 4. Sampling at the rate of innovation _ttheory 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 _ta 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"--
650		0	_aSignal processing.
650		0	_aWavelets (Mathematics)
650		0	_aCompressed sensing (Telecommunication)
700	1		_aEldar, Yonina C.
700	1		_aKutyniok, Gitta.
710	2		_972911 _aCambridge University Press.
856	4	2	_3Contributor biographical information _uhttp://www.loc.gov/catdir/enhancements/fy1117/2011040519-b.html
856	4	2	_3Publisher description _uhttp://www.loc.gov/catdir/enhancements/fy1117/2011040519-d.html
856	4	1	_3Table of contents only _uhttp://www.loc.gov/catdir/enhancements/fy1117/2011040519-t.html
942			_2lcc _cKT
999			_c41308 _d41308