LEADER 04573nam 22007092 450 001 9910790352603321 005 20151005020623.0 010 $a1-107-22736-4 010 $a1-280-77350-2 010 $a0-511-79430-4 010 $a9786613684271 010 $a1-139-33754-8 010 $a1-139-33999-0 010 $a1-139-34157-X 010 $a1-139-33841-2 010 $a1-139-33667-3 035 $a(CKB)2670000000205799 035 $a(EBL)866824 035 $a(OCoLC)796803943 035 $a(SSID)ssj0000677090 035 $a(PQKBManifestationID)11420739 035 $a(PQKBTitleCode)TC0000677090 035 $a(PQKBWorkID)10693488 035 $a(PQKB)11697227 035 $a(UkCbUP)CR9780511794308 035 $a(Au-PeEL)EBL866824 035 $a(CaPaEBR)ebr10578314 035 $a(CaONFJC)MIL368427 035 $z(PPN)26133798X 035 $a(MiAaPQ)EBC866824 035 $a(PPN)180002198 035 $a(EXLCZ)992670000000205799 100 $a20100702d2012|||| uy| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aCompressed sensing $etheory and applications /$fedited by Yonina C. Eldar, Gitta Kutyniok$b[electronic resource] 210 1$aCambridge :$cCambridge University Press,$d2012. 215 $a1 online resource (xii, 544 pages) $cdigital, PDF file(s) 300 $aTitle from publisher's bibliographic system (viewed on 05 Oct 2015). 311 $a1-107-38672-1 311 $a1-107-00558-2 320 $aIncludes bibliographical references and index. 327 $g1.$tIntroduction to compressed sensing /$rMark A. Davenport, Marco F. Duarte, Yonina C. Eldar, and Gitta Kutyniok --$g2.$tSecond-generation sparse modeling : structured and collaborative signal analysis /$rAlexey Castrodad, Ignacio Ramirez, Guillermo Sapiro, Pablo Sprechmann, and Guoshen Yu --$g3.$tXampling : compressed sensing of analog signals /$rMoshe Mishali and Yonina C. Eldar --$g4.$tSampling at the rate of innovation : theory and applications /$rJose Antonia Urigu?en, Yonina C. Eldar, Pier Luigi Dragotta, and Zvika Ben-Haim --$g5.$tIntroduction to the non-asymptotic analysis of random matrices /$rRoman Vershynin --$g6.$tAdaptive sensing for sparse recovery /$rJarvis Haupt and Robert Nowak --$g7.$tFundamental thresholds in compressed sensing : a high-dimensional geometry approach /$rWeiyu Xu and Babak Hassibi --$g8.$tGreedy algorithms for compressed sensing /$rThomas Blumensath, Michael E. Davies, and Gabriel Rilling --$g9.$tGraphical models concepts in compressed sensing /$rAndrea Montanari --$g10.$tFinding needles in compressed haystacks /$rRobert Calderbank and Sina Jafarpour --$g11.$tData separation by sparse representations /$rGitta Kutyniok --$g12.$tFace recognition by sparse representation /$rArvind Ganesh, Andrew Wagner, Zihan Zhou, Allen Y. Yang, Yi Ma, and John Wright. 330 $aCompressed 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. 606 $aSignal processing 606 $aWavelets (Mathematics) 606 $aCompressed sensing (Telecommunication) 615 0$aSignal processing. 615 0$aWavelets (Mathematics) 615 0$aCompressed sensing (Telecommunication) 676 $a621.382/2 702 $aEldar$b Yonina C. 702 $aKutyniok$b Gitta 801 0$bUkCbUP 801 1$bUkCbUP 906 $aBOOK 912 $a9910790352603321 996 $aCompressed sensing$91524328 997 $aUNINA