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010   $a3-319-59533-4
024 7 $a10.1007/978-3-319-59533-7
035   $a(CKB)4340000000062735
035   $a(DE-He213)978-3-319-59533-7
035   $a(MiAaPQ)EBC6281895
035   $a(MiAaPQ)EBC5592729
035   $a(Au-PeEL)EBL5592729
035   $a(OCoLC)1066177633
035   $a(PPN)203669290
035   $a(EXLCZ)994340000000062735
100   $a20170703d2018      u|         0
101 0 $aeng
135   $aurnn#008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aInformation Loss in Deterministic Signal Processing Systems /$fby Bernhard C. Geiger, Gernot Kubin
205   $a1st ed. 2018.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (XIII, 145 p. 16 illus., 9 illus. in color.)
225 1 $aUnderstanding Complex Systems,$x1860-0832
311   $a3-319-59532-6 
327   $aIntroduction -- 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.
330   $aThis book introduces readers to essential tools for the measurement and analysis of information loss in signal processing systems. Employing a new information-theoretic systems theory, the book analyzes various systems in the signal processing engineer?s toolbox: polynomials, quantizers, rectifiers, linear filters with and without quantization effects, principal components analysis, multirate systems, etc. The user benefit of signal processing is further highlighted with the concept of relevant information loss. Signal or data processing operates on the physical representation of information so that users can easily access and extract that information. However, a fundamental theorem in information theory?data processing inequality?states that deterministic processing always involves information loss.  These measures form the basis of a new information-theoretic systems theory, which complements the currently prevailing approaches based on second-order statistics, such as the mean-squared error or error energy. This theory not only provides a deeper understanding but also extends the design space for the applied engineer with a wide range of methods rooted in information theory, adding to existing methods based on energy or quadratic representations.
410  0$aUnderstanding Complex Systems,$x1860-0832
606   $aComputational complexity
606   $aSignal processing
606   $aImage processing
606   $aSpeech processing systems
606   $aStatistical physics
606   $aDynamical systems
606   $aComplexity$3https://scigraph.springernature.com/ontologies/product-market-codes/T11022
606   $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051
606   $aComplex Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/P33000
615  0$aComputational complexity.
615  0$aSignal processing.
615  0$aImage processing.
615  0$aSpeech processing systems.
615  0$aStatistical physics.
615  0$aDynamical systems.
615 14$aComplexity.
615 24$aSignal, Image and Speech Processing.
615 24$aComplex Systems.
676   $a621.3822
700   $aGeiger$b Bernhard C$4aut$4http://id.loc.gov/vocabulary/relators/aut$01063952
702   $aKubin$b Gernot$4aut$4http://id.loc.gov/vocabulary/relators/aut
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910299904603321
996   $aInformation Loss in Deterministic Signal Processing Systems$92535457
997   $aUNINA