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Information Loss in Deterministic Signal Processing Systems

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Information Loss in Deterministic Signal Processing Systems/ by Bernhard C. Geiger, Gernot Kubin.
作者:	Geiger, Bernhard C.
其他作者:	Kubin, Gernot.
面頁冊數:	XIII, 145 p. 16 illus., 9 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Computational complexity. -
電子資源:	https://doi.org/10.1007/978-3-319-59533-7
ISBN:	9783319595337

Information Loss in Deterministic Signal Processing Systems
Geiger, Bernhard C.

Information Loss in Deterministic Signal Processing Systems[electronic resource] /by Bernhard C. Geiger, Gernot Kubin. - 1st ed. 2018. - XIII, 145 p. 16 illus., 9 illus. in color.online resource. - Understanding Complex Systems,1860-0832. - Understanding Complex Systems,.

Introduction -- 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.

This 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.

ISBN: 9783319595337

Standard No.: 10.1007/978-3-319-59533-7doiSubjects--Topical Terms:

527777
Computational complexity.

LC Class. No.: QA267.7

Dewey Class. No.: 620

Information Loss in Deterministic Signal Processing Systems
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