盲信号处理英文版 史习智 著作 阿里云 kindle 下载 pdf chm 百度云 azw3 umd

《盲信号处理:理论与实践(英文)》内容简介：BlindSignalProcessingTheoryandPracticenotonlyintroducesrelatedfundamentalmathematics,butalsoreflectsthenumerousadvancesinthefield,suchasprobabilitydensityestimation-basedprocessingalgorithms,underdeterminedmodels,complexvaluemethods,uncertaintyoforderintheseparationofconvolutivemixturesinfrequencydomains,andfeatureextractionusingIndependentComponentAnalysis(ICA).Attheendofthebook,resultsfromastudyconductedatShanghaiJiaoTongUniversityintheareasofspeechsignalprocessing,underwatersignals,imagefeatureextraction,datacompression,andthelikearediscussed.

Thisbookwillbeofparticularinteresttoadvancedundergraduatestudents,graduatestudents,universityinstructorsandresearchscientistsinrelateddisciplines.XizhiShiisaProfessoratShanghaiJiaoTongUniversity.

Chapter1Introduction

1.1Introduction

1.2BlindSourceSeparation

1.3IndependentComponentAnalysis（ICA）

1.4TheHistoricalDevelopmentandResearchProspectofBlindSignalProcessing

References

Chapter2MathematicalDeionofBlindSignalProcessing

2.1RandomProcessandProbabilityDistribution

2.2EstimationTheory

2.3InformationTheory

2.4Higher-OrderStatistics

2.5PreprocessingofSignal

2.6ComplexNonlinearFunction

2.7EvaluationIndex

References

Chapter3IndependentComponentAnalysis

3.1ProblemStatementandAssumptions

3.2ContrastFunctions

3.3InformationMaximizationMethodofICA

3.4MaximumLikelihoodMethodandCommonLearningRule

3.5FastICAAlgorithm

3.6NaturalGradientMethod

3.7HiddenMarkovIndependentComponentAnalysis

References

Chapter4NonlinearPCA&FeatureExtraction

4.1PrincipalComponentAnalysis&InfinitesimalAnalysis

4.2NonlinearPCAandBlindSourceSeparation

4.3KernelPCA

4.4NeuralNetworksMethodofNonlinearPCAandNonlinearComplexPCA

References

Chapter5NonlinearICA

5.1NonlinearModelandSourceSeparation

5.2LearningAlgorithm

5.3ExtendedGaussianizationMethodofPostNonlinearBlindSeparation

5.4NeuralNetworkMethodforNonlinearICA

5.5GeneticAlgorithmofNonlinearICASolution

5.6ApplicationExamplesofNonlinearICA

References

Chapter6ConvolutiveMixturesandBlindDeconvolution

6.1DeionofIssues

6.2ConvolutiveMixturesinTime-Domain

6.3ConvolutiveMixturesAlgorithmsinFrequency-Domain

6.4Frequency-DomainBlindSeparationofSpeechConvolutiveMixtures

6.5BussgangMethod

6.6Multi-channelBlindDeconvolution

References

Chapter7BlindProcessingAlgorithmBasedonProbabilityDensityEstimation

7.1AdvancingtheProblem

7.2NonparametricEstimationofProbabilityDensityFunction

7.3EstimationofEvaluationFunction

7.4BlindSeparationAlgorithmBasedonProbabilityDensityEstimation

7.5ProbabilityDensityEstimationofGaussianMixturesModel

7.6BlindDeconvolutionAlgorithmBasedonProbabilityDensityFunctionEstimation

7.7On-lineAlgorithmofNonparametricDensityEstimation

References

Chapter8JointApproximateDiagonalizationMethod

8.1Introduction

8.2JADAlgorithmofFrequency-DomainFeature

8.3JADAlgorithmofTime-FrequencyFeature

8.4JointApproximateBlockDiagonalizationAlgorithmofConvolutiveMixtures

8.5JADMethodBasedonCayleyTransformation

8.6JointDiagonalizationandJointNon-DiagonalizationMethod

8.7NonparametricDensityEstimatingSeparatingMethodBasedonTime-FrequencyAnalysis

References

Chapter9ExtensionofBlindSignalProcessing

9.1BlindSignalExtraction

9.2FromProjectionPursuitTechnologytoNonparametricDensityEstimation-BasedICA

9.3Second-OrderStatisticsBasedConvolutiveMixturesSeparationAlgorithm

9.4BlindSeparationforFewerSensorsthanSources——UnderdeterminedModel

9.5FastlCASeparationAlgorithmofComplexNumbersinConvolutiveMixtures

9.6On-lineComplexICAAlgorithmBasedonUncorrelatedCharacteristicsofComplexVectors

9.7ICA-BasedWigner-VilleDistribution

9.8ICAFeatureExtraction

9.9ConstrainedICA

9.10ParticleFilteringBasedNonlinearandNoisyICA

References

Chapter10DataAnalysisandApplicationStudy

10.1TargetEnhancementinActiveSonarDetection

10.2ECGArtifactsRejectioninEEGwithICA

10.3ExperimentonUnderdeterminedBlindSeparationofASpeechSignal

10.4ICAinHumanFaceRecognition

10.5ICAinDataCompression

10.6IndependentComponentAnalysisforFunctionalMRIDataAnalysis

10.7SpeechSeparationforAutomaticSpeechRecognitionSystem

10.8IndependentComponentAnalysisofMicroarrayGeneExpressionDataintheStudyofAlzheimer'sDisease（AD）

References

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《盲信号处理:理论与实践(英文)》是由上海交通大学出版社出版的。

书籍介绍

《盲信号处理:理论与实践(英文)》内容简介：Blind Signal Processing Theory and Practice not only introduces related fundamental mathematics, but also reflects the numerous advances in the field, such as probability density estimation-based processing algorithms,underdetermined models, complex value methods, uncertainty of order in the separation of convolutive mixtures in frequency domains, and feature extraction using Independent Component Analysis (ICA). At the end of the book, results from a study conducted at Shanghai Jiao Tong University in the areas of speech signal processing, underwater signals, image feature extraction, data compression, and the like are discussed.

This book will be of particular interest to advanced undergraduate students,graduate students, university instructors and research scientists in related disciplines. Xizhi Shi is a Professor at Shanghai Jiao Tong University.