000 | 06229nam a2201009 i 4500 | ||
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001 | 5521814 | ||
003 | IEEE | ||
005 | 20200421114118.0 | ||
006 | m o d | ||
007 | cr |n||||||||| | ||
008 | 151221s2010 nyua ob 001 eng d | ||
020 |
_a9780470575758 _qelectronic |
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020 | _a0470575751 | ||
020 |
_z9780470195178 _qprint |
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024 | 7 |
_a10.1002/9780470575758 _2doi |
|
035 | _a(CaBNVSL)mat05521814 | ||
035 | _a(IDAMS)0b000064812d17b5 | ||
040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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050 | 4 |
_aTK5102.9 _b.A288 2010eb |
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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] |
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300 |
_a1 PDF (xv, 407 pages) : _billustrations. |
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336 |
_atext _2rdacontent |
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337 |
_aelectronic _2isbdmedia |
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338 |
_aonline resource _2rdacarrier |
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490 | 1 |
_aAdaptive and learning systems for signal processing, communications and control series ; _v55 |
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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. |
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710 | 2 |
_aIEEE Xplore (Online service), _edistributor. |
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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 _uhttp://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5521814 |
942 | _cEBK | ||
999 |
_c59651 _d59651 |