Advertisement

© 2018

Information Loss in Deterministic Signal Processing Systems

Book

Part of the Understanding Complex Systems book series (UCS)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Bernhard C. Geiger, Gernot Kubin
    Pages 1-14
  3. Random Variables

    1. Front Matter
      Pages 15-15
    2. Bernhard C. Geiger, Gernot Kubin
      Pages 17-34
    3. Bernhard C. Geiger, Gernot Kubin
      Pages 35-42
    4. Bernhard C. Geiger, Gernot Kubin
      Pages 43-72
    5. Bernhard C. Geiger, Gernot Kubin
      Pages 73-89
  4. Stationary Stochastic Processes

    1. Front Matter
      Pages 91-91
    2. Bernhard C. Geiger, Gernot Kubin
      Pages 93-103
    3. Bernhard C. Geiger, Gernot Kubin
      Pages 105-113
    4. Bernhard C. Geiger, Gernot Kubin
      Pages 115-125
    5. Bernhard C. Geiger, Gernot Kubin
      Pages 127-136
    6. Bernhard C. Geiger, Gernot Kubin
      Pages 137-139
  5. Back Matter
    Pages 141-145

About this book

Introduction

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.


Keywords

Linear Filters System Theory Multirate Systems Information Processing Information Theory Polynomials Principal Components Analysis

Authors and affiliations

  1. 1.Institute for Communications EngineeringTechnical University of MunichMunichGermany
  2. 2.Signal Processing and Speech Communication LabGraz University of TechnologyGrazAustria

Bibliographic information

  • Book Title Information Loss in Deterministic Signal Processing Systems
  • Authors Bernhard C. Geiger
    Gernot Kubin
  • Series Title Understanding Complex Systems
  • Series Abbreviated Title Understanding Complex Systems
  • DOI https://doi.org/10.1007/978-3-319-59533-7
  • Copyright Information Springer International Publishing AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Hardcover ISBN 978-3-319-59532-0
  • Softcover ISBN 978-3-319-86645-1
  • eBook ISBN 978-3-319-59533-7
  • 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
  • Buy this book on publisher's site
Industry Sectors
Pharma
Electronics
Aerospace
Oil, Gas & Geosciences
Engineering

Reviews