盲信號處理英文版 史習(xí)智 ..
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品牌:圖書詳情 商品基本信息,請以下列介紹為準(zhǔn) 商品名稱: 盲信號處理英文版 作者: 史習(xí)智 市場價: 150元 文軒網(wǎng)價: 111元【74折】 ISBN號: 9787313058201 出版社: 上海交通大學(xué)出版社 商品類型: 圖書
其他參考信息(以實物為準(zhǔn)) 裝幀: 開本: 語種: 出版時間:2010-09-03 版次: 頁數(shù): 印刷時間:2011-01-11 印次: 字?jǐn)?shù):
主編推薦 《盲信號處理:理論與實踐(英文)》是由上海交通大學(xué)出版社出版的。
內(nèi)容簡介 《盲信號處理:理論與實踐(英文)》內(nèi)容簡介:BlindSignalProcessingTheoryandPracticenotonlyintroducesrelatedfundamentalmathematics,butalsoreflectsthenumerousadvancesinthefield,suchasprobabilitydensityestimation-basedprocessingalgorithms,underdeterminedmodels,complexvaluemethods,uncertaintyoforderintheseparationofconvolutivemixturesinfrequencydomains,andfeatureextractionusingIndependentComponentAnalysis(ICA).Attheendofthebook,resultsfromastudyconductedatShanghaiJiaoTongUniversityintheareasofspeechsignalprocessing,underwatersignals,imagefeatureextraction,datacompression,andthelikearediscussed.
Thisbookwillbeofparticularinteresttoadvancedundergraduatestudents,gradua
......
目錄 Chapter1Introduction
1.1Introduction
1.2BlindSourceSeparation
1.3IndependentComponentAnalysis(ICA)
1.4TheHistoricalDevelopmentandResearchProspectofBlindSignalProcessing
References
Chapter2MathematicalDe*ionofBlindSignalProcessing
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
6.1De*ionofIssues
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
Index
目錄
品牌:圖書
商品基本信息,請以下列介紹為準(zhǔn) | |
商品名稱: | 盲信號處理英文版 |
作者: | 史習(xí)智 |
市場價: | 150元 |
文軒網(wǎng)價: | 111元【74折】 |
ISBN號: | 9787313058201 |
出版社: | 上海交通大學(xué)出版社 |
商品類型: | 圖書 |
其他參考信息(以實物為準(zhǔn)) | ||
裝幀: | 開本: | 語種: |
出版時間:2010-09-03 | 版次: | 頁數(shù): |
印刷時間:2011-01-11 | 印次: | 字?jǐn)?shù): |
主編推薦 | |
《盲信號處理:理論與實踐(英文)》是由上海交通大學(xué)出版社出版的。 |
內(nèi)容簡介 | |
《盲信號處理:理論與實踐(英文)》內(nèi)容簡介:BlindSignalProcessingTheoryandPracticenotonlyintroducesrelatedfundamentalmathematics,butalsoreflectsthenumerousadvancesinthefield,suchasprobabilitydensityestimation-basedprocessingalgorithms,underdeterminedmodels,complexvaluemethods,uncertaintyoforderintheseparationofconvolutivemixturesinfrequencydomains,andfeatureextractionusingIndependentComponentAnalysis(ICA).Attheendofthebook,resultsfromastudyconductedatShanghaiJiaoTongUniversityintheareasofspeechsignalprocessing,underwatersignals,imagefeatureextraction,datacompression,andthelikearediscussed. Thisbookwillbeofparticularinteresttoadvancedundergraduatestudents,gradua ...... |
目錄 | |
Chapter1Introduction 1.1Introduction 1.2BlindSourceSeparation 1.3IndependentComponentAnalysis(ICA) 1.4TheHistoricalDevelopmentandResearchProspectofBlindSignalProcessing References Chapter2MathematicalDe*ionofBlindSignalProcessing 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 6.1De*ionofIssues 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 Index |
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