LEADER 03325nam 2200589 450 001 9910822663203321 005 20220715222302.0 010 $a1-118-57977-1 010 $a1-118-57974-7 010 $a1-118-57973-9 035 $a(CKB)2670000000432331 035 $a(EBL)1443829 035 $a(OCoLC)861558924 035 $a(SSID)ssj0001173036 035 $a(PQKBManifestationID)11608947 035 $a(PQKBTitleCode)TC0001173036 035 $a(PQKBWorkID)11193392 035 $a(PQKB)10017191 035 $a(MiAaPQ)EBC1443829 035 $a(Au-PeEL)EBL1443829 035 $a(CaPaEBR)ebr10780752 035 $a(PPN)178460338 035 $a(EXLCZ)992670000000432331 100 $a20131029d2013 uy| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBlind identification and separation of complex-valued signals /$fEric Moreau, Tu?lay Adal? 210 1$aLondon :$cISTE,$d2013. 215 $a1 online resource (108 p.) 225 1 $aFocus : digital signal and image processing series,$x2051-2481 300 $aDescription based upon print version of record. 311 $a1-84821-459-6 320 $aIncludes bibliographical references and index. 327 $aCover; Title Page; Contents; Preface; Acknowledgments; Chapter 1. Mathematical Preliminaries; 1.1. Introduction; 1.2. Linear mixing model; 1.3. Problem definition; 1.4. Statistics; 1.4.1. Statistics of random variables and random vectors; 1.4.2. Differential entropy of complex random vectors; 1.4.3. Statistics of random processes; 1.4.4. Complex matrix decompositions; 1.5. Optimization: Wirtinger calculus; 1.5.1. Scalar case; 1.5.2. Vector case; 1.5.3. Matrix case; 1.5.4. Summary; Chapter 2. Estimation by Joint Diagonalization; 2.1. Introduction 327 $a3.2.1. Mutual information and mutual information rate minimization3.2.2. Maximum likelihood; 3.2.3. Identifiability of the complex ICA model; 3.3. Algorithms; 3.3.1. ML ICA: unconstrained W; 3.3.2. Complex maximization of non-Gaussianity: ML ICA with unitary W; 3.3.3. Density matching; 3.3.4. A flexible complex ICA algorithm: Entropy bound minimization; 3.4. Summary; Bibliography; Index 330 $aBlind identification consists of estimating a multi-dimensional system only through the use of its output, and source separation, the blind estimation of the inverse of the system. Estimation is generally carried out using different statistics of the output. The authors of this book consider the blind identification and source separation problem in the complex-domain, where the available statistical properties are richer and include non-circularity of the sources - underlying components. They define identifiability conditions and present state-of-the-art algorithms that are based on algebra 410 0$aDigital signal and image processing series. 606 $aSignal processing$xStatistical methods 615 0$aSignal processing$xStatistical methods. 676 $a108 700 $aMoreau$b Eric$0958804 701 $aAdali$b Tu?lay$0845678 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910822663203321 996 $aBlind identification and separation of complex-valued signals$92897840 997 $aUNINA