000 04255nam a22005775i 4500
001 978-3-642-38398-4
003 DE-He213
005 20200420220227.0
007 cr nn 008mamaa
008 130913s2014 gw | s |||| 0|eng d
020 _a9783642383984
_9978-3-642-38398-4
024 7 _a10.1007/978-3-642-38398-4
_2doi
050 4 _aTK5102.9
050 4 _aTA1637-1638
050 4 _aTK7882.S65
072 7 _aTTBM
_2bicssc
072 7 _aUYS
_2bicssc
072 7 _aTEC008000
_2bisacsh
072 7 _aCOM073000
_2bisacsh
082 0 4 _a621.382
_223
245 1 0 _aCompressed Sensing & Sparse Filtering
_h[electronic resource] /
_cedited by Avishy Y. Carmi, Lyudmila Mihaylova, Simon J. Godsill.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2014.
300 _aXII, 502 p. 135 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSignals and Communication Technology,
_x1860-4862
505 0 _aIntroduction to Compressed Sensing and Sparse Filtering -- The Geometry of Compressed Sensing -- Sparse Signal Recovery with Exponential-Family Noise -- Nuclear Norm Optimization and its Application to Observation Model Specification -- Nonnegative Tensor Decomposition -- Sub-Nyquist Sampling and Compressed Sensing in Cognitive Radio Networks -- Sparse Nonlinear MIMO Filtering and Identification -- Optimization Viewpoint on Kalman Smoothing with Applications to Robust and Sparse Estimation -- Compressive System Identification -- Distributed Approximation and Tracking using Selective Gossip -- Recursive Reconstruction of Sparse Signal Sequences -- Estimation of Time-Varying Sparse Signals in Sensor Networks -- Sparsity and Compressed Sensing in Mono-static and Multi-static Radar Imaging -- Structured Sparse Bayesian Modelling for Audio Restoration -- Sparse Representations for Speech Recognition.
520 _aThis book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and limited data. One such trend that recently gained popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals in nature are nearly sparse (or compressible, as they are normally referred to) in some domain, and consequently they can be reconstructed to within high accuracy from far fewer observations than traditionally held to be necessary.  Apart from compressed sensing this book contains other related approaches. Each methodology has its own formalities for dealing with such problems. As an example, in the Bayesian approach, sparseness promoting priors such as Laplace and Cauchy are normally used for penalising improbable model variables, thus promoting low complexity solutions. Compressed sensing techniques and homotopy-type solutions, such as the LASSO, utilise l1-norm penalties for obtaining sparse solutions using fewer observations than conventionally needed. The book emphasizes on the role of sparsity as a machinery for promoting low complexity representations and likewise its connections to variable selection and dimensionality reduction in various engineering problems.  This book is intended for researchers, academics and practitioners with interest in various aspects and applications of sparse signal processing.  .
650 0 _aEngineering.
650 0 _aNumerical analysis.
650 0 _aAlgorithms.
650 0 _aComplexity, Computational.
650 1 4 _aEngineering.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aNumeric Computing.
650 2 4 _aMathematics of Algorithmic Complexity.
650 2 4 _aComplexity.
700 1 _aCarmi, Avishy Y.
_eeditor.
700 1 _aMihaylova, Lyudmila.
_eeditor.
700 1 _aGodsill, Simon J.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642383977
830 0 _aSignals and Communication Technology,
_x1860-4862
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-38398-4
912 _aZDB-2-ENG
942 _cEBK
999 _c52266
_d52266