Information Loss In Deterministic Signal Processing Systems (understanding Complex Systems)
by Bernhard C. Geiger /
2017 / English / PDF
2.6 MB Download
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.
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
These measures form the basis of a new information-theoretic
systems theory, which complements the currently prevailing
approaches based on second-orderstatistics, 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.
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.