Compressed Sensing & Sparse Filtering (Record no. 93238)

000 -LEADER
fixed length control field 04196nam a22005535i 4500
001 - CONTROL NUMBER
control field 978-3-642-38398-4
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20140220082517.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 130913s2014 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783642383984
-- 978-3-642-38398-4
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-3-642-38398-4
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TK5102.9
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TA1637-1638
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TK7882.S65
072 #7 - SUBJECT CATEGORY CODE
Subject category code TTBM
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYS
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code TEC008000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM073000
Source bisacsh
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 621.382
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Carmi, Avishy Y.
Relator term editor.
245 10 - TITLE STATEMENT
Title Compressed Sensing & Sparse Filtering
Medium [electronic resource] /
Statement of responsibility, etc edited by Avishy Y. Carmi, Lyudmila Mihaylova, Simon J. Godsill.
264 #1 -
-- Berlin, Heidelberg :
-- Springer Berlin Heidelberg :
-- Imprint: Springer,
-- 2014.
300 ## - PHYSICAL DESCRIPTION
Extent XII, 502 p. 135 illus.
Other physical details online resource.
336 ## -
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-- txt
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-- computer
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-- rdamedia
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-- online resource
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347 ## -
-- text file
-- PDF
-- rda
490 1# - SERIES STATEMENT
Series statement Signals and Communication Technology,
International Standard Serial Number 1860-4862
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Introduction 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 ## - SUMMARY, ETC.
Summary, etc This 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 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Engineering.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Electronic data processing.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Physics.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Engineering.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Signal, Image and Speech Processing.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Numeric Computing.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mathematics of Algorithmic Complexity.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Complexity.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Mihaylova, Lyudmila.
Relator term editor.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Godsill, Simon J.
Relator term editor.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783642383977
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Signals and Communication Technology,
-- 1860-4862
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-3-642-38398-4
912 ## -
-- ZDB-2-ENG

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