LEADER 05557nam 22006734a 450 001 9910140741803321 005 20200520144314.0 010 $a1-282-65650-3 010 $a9786612656507 010 $a0-470-57575-1 010 $a0-470-57574-3 024 7 $a10.1002/9780470575758 035 $a(CKB)2670000000032876 035 $a(EBL)554990 035 $a(SSID)ssj0000402484 035 $a(PQKBManifestationID)11276171 035 $a(PQKBTitleCode)TC0000402484 035 $a(PQKBWorkID)10426814 035 $a(PQKB)10259682 035 $a(MiAaPQ)EBC554990 035 $a(CaBNVSL)mat05521814 035 $a(IDAMS)0b000064812d17b5 035 $a(IEEE)5521814 035 $a(CaSebORM)9780470195178 035 $a(OCoLC)644162805 035 $a(PPN)176131914 035 $a(OCoLC)871683821 035 $a(OCoLC)ocn871683821 035 $a(EXLCZ)992670000000032876 100 $a20090813d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aAdaptive signal processing $enext generation solutions /$f[edited by] Tulay Adali, Simon S. Haykin 205 $a1st edition 210 $aHoboken, N.J. $cWiley-IEEE$dc2010 215 $a1 online resource (428 p.) 225 1 $aAdaptive and learning systems for signal processing, communications and control series ;$v55 300 $aDescription based upon print version of record. 311 $a0-470-19517-7 320 $aIncludes bibliographical references and index. 327 $aPreface -- Contributors -- Chapter 1 Complex-Valued Adaptive Signal Processing -- 1.1 Introduction -- -- 1.2 Preliminaries -- 1.3 Optimization in the Complex Domain -- 1.4 Widely Linear Adaptive Filtering -- 1.5 Nonlinear Adaptive Filtering with Multilayer Perceptrons -- 1.6 Complex Independent Component Analysis -- 1.7 Summary -- 1.8 Acknowledgment -- 1.9 Problems -- References -- Chapter 2 Robust Estimation Techniques for Complex-Valued Random Vectors -- 2.1 Introduction -- 2.2 Statistical Characterization of Complex Random Vectors -- 2.3 Complex Elliptically Symmetric (CES) Distributions -- 2.4 Tools to Compare Estimators -- 2.5 Scatter and Pseudo-Scatter Matrices -- 2.6 Array Processing Examples -- 2.7 MVDR Beamformers Based on M-Estimators -- 2.8 Robust ICA -- 2.9 Conclusion -- 2.10 Problems -- References -- Chapter 3 Turbo Equalization -- 3.1 Introduction -- 3.2 Context -- 3.3 Communication Chain -- 3.4 Turbo Decoder: Overview -- 3.5 Forward-Backward Algorithm -- 3.6 Simplified Algorithm: Interference Canceler -- 3.7 Capacity Analysis -- 3.8 Blind Turbo Equalization -- 3.9 Convergence -- 3.10 Multichannel and Multiuser Settings -- 3.11 Concluding Remarks -- 3.12 Problems -- References -- Chapter 4 Subspace Tracking for Signal Processing -- 4.1 Introduction -- 4.2 Linear Algebra Review -- 4.3 Observation Model and Problem Statement -- 4.4 Preliminary Example: Oja's Neuron -- 4.5 Subspace Tracking -- 4.6 Eigenvectors Tracking -- 4.7 Convergence and Performance Analysis Issues -- 4.8 Illustrative Examples -- 4.9 Concluding Remarks -- 4.10 Problems -- References -- Chapter 5 Particle Filtering -- 5.1 Introduction -- 5.2 Motivation for Use of Particle Filtering -- 5.3 The Basic Idea -- 5.4 The Choice of Proposal Distribution and Resampling -- 5.5 Some Particle Filtering Methods -- 5.6 Handling Constant Parameters -- 5.7 Rao-Blackwellization -- 5.8 Prediction -- 5.9 Smoothing -- 5.10 Convergence Issues -- 5.11 Computational Issues and Hardware Implementation -- 5.12 Acknowledgments. 327 $a5.13 Exercises -- References -- Chapter 6 Nonlinear Sequential State Estimation for Solving Pattern-Classification Problems -- 6.1 Introduction -- 6.2 Back-Propagation and Support Vector Machine-Learning Algorithms: Review -- 6.3 Supervised Training Framework of MLPs Using Nonlinear Sequential State Estimation -- 6.4 The Extended Kalman Filter -- 6.5 Experimental Comparison of the Extended Kalman Filtering Algorithm with the Back-Propagation and Support Vector Machine Learning Algorithms -- 6.6 Concluding Remarks -- 6.7 Problems -- References -- Chapter 7 Bandwidth Extension of Telephony Speech -- 7.1 Introduction -- 7.2 Organization of the Chapter -- 7.3 Nonmodel-Based Algorithms for Bandwidth Extension -- 7.4 Basics -- 7.5 Model-Based Algorithms for Bandwidth Extension -- 7.6 Evaluation of Bandwidth Extension Algorithms -- 7.7 Conclusion -- 7.8 Problems -- References -- Index. 330 $aLeading experts present the latest research results in adaptive signal processing Recent developments in signal processing have made it clear that significant performance gains can be achieved beyond those achievable using standard adaptive filtering approaches. Adaptive Signal Processing presents the next generation of algorithms that will produce these desired results, with an emphasis on important applications and theoretical advancements. This highly unique resource brings together leading authorities in the field writing on the key topics of significance, each at the cutti 410 0$aAdaptive and learning systems for signal processing, communications, and control ;$v55 606 $aAdaptive signal processing 615 0$aAdaptive signal processing. 676 $a621.382/2 701 $aAdali$b Tulay$0845678 701 $aHaykin$b Simon S.$f1931-$08857 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910140741803321 996 $aAdaptive signal processing$91887893 997 $aUNINA