LEADER 06374nam 22009135 450 001 9910299783403321 005 20200630180847.0 010 $a3-319-16042-7 024 7 $a10.1007/978-3-319-16042-9 035 $a(CKB)3710000000444367 035 $a(EBL)3567628 035 $a(SSID)ssj0001534435 035 $a(PQKBManifestationID)11879543 035 $a(PQKBTitleCode)TC0001534435 035 $a(PQKBWorkID)11494966 035 $a(PQKB)10274977 035 $a(DE-He213)978-3-319-16042-9 035 $a(MiAaPQ)EBC3567628 035 $a(PPN)187689873 035 $a(EXLCZ)993710000000444367 100 $a20150704d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aCompressed Sensing and its Applications $eMATHEON Workshop 2013 /$fedited by Holger Boche, Robert Calderbank, Gitta Kutyniok, Jan Vybíral 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Birkhäuser,$d2015. 215 $a1 online resource (475 p.) 225 1 $aApplied and Numerical Harmonic Analysis,$x2296-5009 300 $aDescription based upon print version of record. 311 $a3-319-16041-9 320 $aIncludes bibliographical references at the end of each chapters. 327 $aSurvey on Compressed Sensing -- Temporal compressive sensing for video -- Compressed Sensing, Sparse Inversion, and Model Mismatch -- Recovering Structured Signals in Noise: Least-Squares Meets Compressed Sensing -- The Quest for Optimal Sampling: Computationally Efficient, Structure-exploiting Measurements for Compressed Sensing -- Compressive Sensing in Acoustic Imaging -- Quantization and Compressive Sensing -- Compressive Gaussian Mixture Estimation -- Two Algorithms for Compressed Sensing of Sparse Tensors -- Sparse Model Uncertainties in Compressed Sensing with Application to Convolutions and Sporadic Communication -- Cosparsity in Compressed Sensing -- Structured Sparsity: Discrete and Convex Approaches -- Explicit Matrices with the Restricted Isometry Property: Breaking the Square-Root Bottleneck -- Tensor Completion in Hierarchical Tensor Representations -- Compressive Classification: Where Wireless Communications Meets Machine Learning. 330 $aSince publication of the initial papers in 2006, compressed sensing has captured the imagination of the international signal processing community, and the mathematical foundations are nowadays quite well understood. Parallel to the progress in mathematics, the potential applications of compressed sensing have been explored by many international groups of, in particular, engineers and applied mathematicians, achieving very promising advances in various areas such as communication theory, imaging sciences, optics, radar technology, sensor networks, or tomography. Since many applications have reached a mature state, the research center MATHEON in Berlin focusing on "Mathematics for Key Technologies", invited leading researchers on applications of compressed sensing from mathematics, computer science, and engineering to the "MATHEON Workshop 2013: Compressed Sensing and its Applications? in December 2013. It was the first workshop specifically focusing on the applications of compressed sensing. This book features contributions by the plenary and invited speakers of this workshop. To make this book accessible for those unfamiliar with compressed sensing, the book will not only contain chapters on various applications of compressed sensing written by plenary and invited speakers, but will also provide a general introduction into compressed sensing. The book is aimed at both graduate students and researchers in the areas of applied mathematics, computer science, and engineering as well as other applied scientists interested in the potential and applications of the novel methodology of compressed sensing.  For those readers who are not already familiar with compressed sensing, an introduction to the basics of this theory will be included. 410 0$aApplied and Numerical Harmonic Analysis,$x2296-5009 606 $aInformation theory 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aCoding theory 606 $aNumerical analysis 606 $aMatrix theory 606 $aAlgebra 606 $aComputer science$xMathematics 606 $aInformation and Communication, Circuits$3https://scigraph.springernature.com/ontologies/product-market-codes/M13038 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 606 $aCoding and Information Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/I15041 606 $aNumerical Analysis$3https://scigraph.springernature.com/ontologies/product-market-codes/M14050 606 $aLinear and Multilinear Algebras, Matrix Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/M11094 606 $aComputational Science and Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/M14026 615 0$aInformation theory. 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 0$aCoding theory. 615 0$aNumerical analysis. 615 0$aMatrix theory. 615 0$aAlgebra. 615 0$aComputer science$xMathematics. 615 14$aInformation and Communication, Circuits. 615 24$aSignal, Image and Speech Processing. 615 24$aCoding and Information Theory. 615 24$aNumerical Analysis. 615 24$aLinear and Multilinear Algebras, Matrix Theory. 615 24$aComputational Science and Engineering. 676 $a621.38220151 702 $aBoche$b Holger$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCalderbank$b Robert$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKutyniok$b Gitta$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aVybíral$b Jan$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299783403321 996 $aCompressed sensing and its applications$91522582 997 $aUNINA