000			06210nam a2200997 i 4500
001			5521814
003			IEEE
005			20191218152120.0
006			m o d
007			cr |n|||||||||
008			151221s2010 nyua ob 001 eng d
020			_a9780470575758 _qelectronic
020			_a0470575751
020			_z9780470195178 _qprint
024	7		_a10.1002/9780470575758 _2doi
035			_a(CaBNVSL)mat05521814
035			_a(IDAMS)0b000064812d17b5
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aTK5102.9 _b.A288 2010eb
082	0	4	_a621.382/2 _222
245	0	0	_aAdaptive signal processing : _bnext generation solutions / _cedited by T�eulay Adali, Simon Haykin.
264		1	_aNew York : _bIEEE, Institute of Electrical and Electronics Engineers, _cc2010.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2010]
300			_a1 PDF (xv, 407 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aAdaptive and learning systems for signal processing, communications and control series ; _v55
504			_aIncludes bibliographical references and index.
505	0		_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.
505	8		_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.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/21/2015.
650		0	_aAdaptive signal processing.
655		0	_aElectronic books.
695			_aAdaptive signal processing
695			_aAdvertising
695			_aAlgorithm design and analysis
695			_aAlgorithms
695			_aArrays
695			_aAtmospheric measurements
695			_aBandwidth
695			_aBooks
695			_aCalculus
695			_aComplexity theory
695			_aConvolutional codes
695			_aCovariance matrix
695			_aDecoding
695			_aDelay
695			_aEigenvalues and eigenfunctions
695			_aEqualizers
695			_aEquations
695			_aEstimation
695			_aIndependent component analysis
695			_aIndexes
695			_aIntersymbol interference
695			_aIterative decoding
695			_aKalman filters
695			_aLinear algebra
695			_aMagnetic resonance imaging
695			_aMathematical model
695			_aMultilayer perceptrons
695			_aNeurons
695			_aNoise
695			_aParticle measurements
695			_aReceivers
695			_aRobustness
695			_aSections
695			_aSignal processing
695			_aSignal processing algorithms
695			_aSpeech processing
695			_aState estimation
695			_aSupport vector machines
695			_aSymmetric matrices
695			_aTelephony
695			_aTraining
700	1		_aHaykin, Simon S., _d1931-
700	1		_aAdali, T�eulay.
710	2		_aJohn Wiley & Sons, _epublisher.
710	2		_aIEEE Xplore (Online service), _edistributor.
776	0	8	_iPrint version: _z9780470195178
830		0	_aAdaptive and learning systems for signal processing, communications, and control ; _v55
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5521814
999			_c42247 _d42247