Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/1181
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dc.contributor.authorGeiger, Bernhard C. ;en_US
dc.contributor.authorKubin, Gernot. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:26:11Z-
dc.date.available2020-05-17T08:26:11Z-
dc.date.issued2018en_US
dc.identifier.isbn9783319595337 ;en_US
dc.identifier.isbn9783319595320 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/1181-
dc.descriptionPrinted edition: ; 9783319595320. ;en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThis book introduces readers to essential tools for the measurement and analysis of information loss in signal processing systems. Employing a new information-theoretic systems theory, the book analyzes various systems in the signal processing engineerees toolbox: polynomials, quantizers, rectifiers, linear filters with and without quantization effects, principal components analysis, multirate systems, etc. The user benefit of signal processing is further highlighted with the concept of relevant information loss. Signal or data processing operates on the physical representation of information so that users can easily access and extract that information. However, a fundamental theorem in information theoryeedata processing inequalityeestates that deterministic processing always involves information loss.e These measures form the basis of a new information-theoretic systems theory, which complements the currently prevailing approaches based on second-order statistics, such as the mean-squared error or error energy. This theory not only provides a deeper understanding but also extends the design space for the applied engineer with a wide range of methods rooted in information theory, adding to existing methods based on energy or quadratic representations. ;en_US
dc.description.statementofresponsibilityby Bernhard C. Geiger, Gernot Kubin.en_US
dc.description.tableofcontentsIntroduction -- Part I: Random Variables -- Piecewise Bijective Functions and Continuous Inputs -- General Input Distributions -- Dimensionality-Reducing Functions -- Relevant Information Loss -- II. Part II: Stationary Stochastic Processes -- Discrete-Valued Processes -- Piecewise Bijective Functions and Continuous Inputs -- Dimensionality-Reducing Functions -- Relevant Information Loss Rate -- Conclusion and Outlook. ;en_US
dc.format.extentXIII, 145 p. 16 illus., 9 illus. in color. ; online resource. ;en_US
dc.publisherSpringer International Publishing :en_US
dc.publisherImprint: Springer,en_US
dc.relation.ispartofseriesUnderstanding Complex Systems, ; 1860-0832. ;en_US
dc.relation.ispartofseriesUnderstanding Complex Systems, ; 1860-0832. ;en_US
dc.relation.haspart9783319595320.pdfen_US
dc.subjectEngineeringen_US
dc.subjectSystem theory. ;en_US
dc.subjectComplexity, Computational. ;en_US
dc.subjectEngineeringen_US
dc.subjectComplexity. ;en_US
dc.subjectSignal, Image and Speech Processing. ;en_US
dc.subjectComplex Systems. ;en_US
dc.subject.ddc620 ; 23 ;en_US
dc.subject.lccQA76.9.M35 ;en_US
dc.titleInformation Loss in Deterministic Signal Processing Systemsen_US
dc.typeBooken_US
dc.publisher.placeCham :en_US
Appears in Collections:مدیریت فناوری اطلاعات

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorGeiger, Bernhard C. ;en_US
dc.contributor.authorKubin, Gernot. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:26:11Z-
dc.date.available2020-05-17T08:26:11Z-
dc.date.issued2018en_US
dc.identifier.isbn9783319595337 ;en_US
dc.identifier.isbn9783319595320 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/1181-
dc.descriptionPrinted edition: ; 9783319595320. ;en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThis book introduces readers to essential tools for the measurement and analysis of information loss in signal processing systems. Employing a new information-theoretic systems theory, the book analyzes various systems in the signal processing engineerees toolbox: polynomials, quantizers, rectifiers, linear filters with and without quantization effects, principal components analysis, multirate systems, etc. The user benefit of signal processing is further highlighted with the concept of relevant information loss. Signal or data processing operates on the physical representation of information so that users can easily access and extract that information. However, a fundamental theorem in information theoryeedata processing inequalityeestates that deterministic processing always involves information loss.e These measures form the basis of a new information-theoretic systems theory, which complements the currently prevailing approaches based on second-order statistics, such as the mean-squared error or error energy. This theory not only provides a deeper understanding but also extends the design space for the applied engineer with a wide range of methods rooted in information theory, adding to existing methods based on energy or quadratic representations. ;en_US
dc.description.statementofresponsibilityby Bernhard C. Geiger, Gernot Kubin.en_US
dc.description.tableofcontentsIntroduction -- Part I: Random Variables -- Piecewise Bijective Functions and Continuous Inputs -- General Input Distributions -- Dimensionality-Reducing Functions -- Relevant Information Loss -- II. Part II: Stationary Stochastic Processes -- Discrete-Valued Processes -- Piecewise Bijective Functions and Continuous Inputs -- Dimensionality-Reducing Functions -- Relevant Information Loss Rate -- Conclusion and Outlook. ;en_US
dc.format.extentXIII, 145 p. 16 illus., 9 illus. in color. ; online resource. ;en_US
dc.publisherSpringer International Publishing :en_US
dc.publisherImprint: Springer,en_US
dc.relation.ispartofseriesUnderstanding Complex Systems, ; 1860-0832. ;en_US
dc.relation.ispartofseriesUnderstanding Complex Systems, ; 1860-0832. ;en_US
dc.relation.haspart9783319595320.pdfen_US
dc.subjectEngineeringen_US
dc.subjectSystem theory. ;en_US
dc.subjectComplexity, Computational. ;en_US
dc.subjectEngineeringen_US
dc.subjectComplexity. ;en_US
dc.subjectSignal, Image and Speech Processing. ;en_US
dc.subjectComplex Systems. ;en_US
dc.subject.ddc620 ; 23 ;en_US
dc.subject.lccQA76.9.M35 ;en_US
dc.titleInformation Loss in Deterministic Signal Processing Systemsen_US
dc.typeBooken_US
dc.publisher.placeCham :en_US
Appears in Collections:مدیریت فناوری اطلاعات

Files in This Item:
File Description SizeFormat 
9783319595320.pdf2.7 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorGeiger, Bernhard C. ;en_US
dc.contributor.authorKubin, Gernot. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:26:11Z-
dc.date.available2020-05-17T08:26:11Z-
dc.date.issued2018en_US
dc.identifier.isbn9783319595337 ;en_US
dc.identifier.isbn9783319595320 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/1181-
dc.descriptionPrinted edition: ; 9783319595320. ;en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThis book introduces readers to essential tools for the measurement and analysis of information loss in signal processing systems. Employing a new information-theoretic systems theory, the book analyzes various systems in the signal processing engineerees toolbox: polynomials, quantizers, rectifiers, linear filters with and without quantization effects, principal components analysis, multirate systems, etc. The user benefit of signal processing is further highlighted with the concept of relevant information loss. Signal or data processing operates on the physical representation of information so that users can easily access and extract that information. However, a fundamental theorem in information theoryeedata processing inequalityeestates that deterministic processing always involves information loss.e These measures form the basis of a new information-theoretic systems theory, which complements the currently prevailing approaches based on second-order statistics, such as the mean-squared error or error energy. This theory not only provides a deeper understanding but also extends the design space for the applied engineer with a wide range of methods rooted in information theory, adding to existing methods based on energy or quadratic representations. ;en_US
dc.description.statementofresponsibilityby Bernhard C. Geiger, Gernot Kubin.en_US
dc.description.tableofcontentsIntroduction -- Part I: Random Variables -- Piecewise Bijective Functions and Continuous Inputs -- General Input Distributions -- Dimensionality-Reducing Functions -- Relevant Information Loss -- II. Part II: Stationary Stochastic Processes -- Discrete-Valued Processes -- Piecewise Bijective Functions and Continuous Inputs -- Dimensionality-Reducing Functions -- Relevant Information Loss Rate -- Conclusion and Outlook. ;en_US
dc.format.extentXIII, 145 p. 16 illus., 9 illus. in color. ; online resource. ;en_US
dc.publisherSpringer International Publishing :en_US
dc.publisherImprint: Springer,en_US
dc.relation.ispartofseriesUnderstanding Complex Systems, ; 1860-0832. ;en_US
dc.relation.ispartofseriesUnderstanding Complex Systems, ; 1860-0832. ;en_US
dc.relation.haspart9783319595320.pdfen_US
dc.subjectEngineeringen_US
dc.subjectSystem theory. ;en_US
dc.subjectComplexity, Computational. ;en_US
dc.subjectEngineeringen_US
dc.subjectComplexity. ;en_US
dc.subjectSignal, Image and Speech Processing. ;en_US
dc.subjectComplex Systems. ;en_US
dc.subject.ddc620 ; 23 ;en_US
dc.subject.lccQA76.9.M35 ;en_US
dc.titleInformation Loss in Deterministic Signal Processing Systemsen_US
dc.typeBooken_US
dc.publisher.placeCham :en_US
Appears in Collections:مدیریت فناوری اطلاعات

Files in This Item:
File Description SizeFormat 
9783319595320.pdf2.7 MBAdobe PDFThumbnail
Preview File