00457nam##22001337##450#99105515478033219782728309467########d########u##y0engy50####ba########00###Il governo di ogni giorno : l'amministrazione quotidiana in uno stato di antico regime (Lucca, XVII-XVIII secolo) / Matteo Giuli.Giuli, Matteo.6174509910551547803321Il governo di ogni giorno2432978UNINA03325nam 2200589 450 991014202520332120220715222302.01-118-57977-11-118-57974-71-118-57973-9(CKB)2670000000432331(EBL)1443829(OCoLC)861558924(SSID)ssj0001173036(PQKBManifestationID)11608947(PQKBTitleCode)TC0001173036(PQKBWorkID)11193392(PQKB)10017191(MiAaPQ)EBC1443829(Au-PeEL)EBL1443829(CaPaEBR)ebr10780752(PPN)178460338(EXLCZ)99267000000043233120131029d2013 uy| 0engur|n|---|||||txtccrBlind identification and separation of complex-valued signals /Eric Moreau, Tülay AdalıLondon :ISTE,2013.1 online resource (108 p.)Focus : digital signal and image processing series,2051-2481Description based upon print version of record.1-84821-459-6 Includes bibliographical references and index.Cover; 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. Introduction3.2.1. 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; IndexBlind 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 algebraDigital signal and image processing series.Signal processingStatistical methodsSignal processingStatistical methods.108Moreau Eric958804Adali Tülay845678MiAaPQMiAaPQMiAaPQBOOK9910142025203321Blind identification and separation of complex-valued signals2897840UNINA