Nonlinear Digital Filtering with Python

Name: Nonlinear Digital Filtering with Python
Brand: Taylor & Francis Inc
Price: 228.75 AUD
Availability: BackOrder

An Introduction

By: Ronald K. Pearson, Moncef Gabbouj

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Hardcover | 25 September 2015 | Edition Number 1

At a Glance

Hardcover
308 Pages

Dimensions(cm)
23.4 x 15.6

Hardcover

RRP $263.00

$228.75

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Nonlinear Digital Filtering with Python: An Introduction

discusses important structural filter classes including the median filter and a number of its extensions (e.g., weighted and recursive median filters), and Volterra filters based on polynomial nonlinearities. Adopting both structural and behavioral approaches in characterizing and designing nonlinear digital filters, this book:

Begins with an expedient introduction to programming in the free, open-source computing environment of Python
Uses results from algebra and the theory of functional equations to construct and characterize behaviorally defined nonlinear filter classes
Analyzes the impact of a range of useful interconnection strategies on filter behavior, providing Python implementations of the presented filters and interconnection strategies
Proposes practical, bottom-up strategies for designing more complex and capable filters from simpler components in a way that preserves the key properties of these components
Illustrates the behavioral consequences of allowing recursive (i.e., feedback) interconnections in nonlinear digital filters while highlighting a challenging but promising research frontier

Nonlinear Digital Filtering with Python: An Introduction supplies essential knowledge useful for developing and implementing data cleaning filters for dynamic data analysis and time-series modeling.

Industry Reviews

"The authors bring the reader from the consolidated world of linear filters into the variegate universe of nonlinear filters, and show how the main subclasses of digital nonlinear filters can be described on the basis of their structural and/or behavioral characteristics. This approach is complemented by the use of a free, open-source computing environment-Python-for the implementation of the nonlinear digital filters presented in each chapter."
-Giovanni L. Sicuranza, University of Trieste, Italy

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