Overview
- Presents a definition of information loss for signal processing systems which is the basis of an information-theoretic systems theory
- 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.
- Highlights differences and similarities between design principles based on information-theoretic quantities and those based on energetic measures, such as the mean-squared error
- Includes supplementary material: sn.pub/extras
Part of the book series: Understanding Complex Systems (UCS)
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About this book
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.
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Table of contents (10 chapters)
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Random Variables
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Stationary Stochastic Processes
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Authors and Affiliations
Bibliographic Information
Book Title: Information Loss in Deterministic Signal Processing Systems
Authors: Bernhard C. Geiger, Gernot Kubin
Series Title: Understanding Complex Systems
DOI: https://doi.org/10.1007/978-3-319-59533-7
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG 2018
Hardcover ISBN: 978-3-319-59532-0Published: 10 July 2017
Softcover ISBN: 978-3-319-86645-1Published: 04 August 2018
eBook ISBN: 978-3-319-59533-7Published: 02 July 2017
Series ISSN: 1860-0832
Series E-ISSN: 1860-0840
Edition Number: 1
Number of Pages: XIII, 145
Number of Illustrations: 7 b/w illustrations, 9 illustrations in colour
Topics: Complexity, Signal, Image and Speech Processing, Complex Systems