Nonlinear Digital Filtering with Python
An Introduction
Moncef Gabbouj author Ronald K Pearson author
Format:Hardback
Publisher:Taylor & Francis Inc
Published:25th Sep '15
Currently unavailable, and unfortunately no date known when it will be back
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.
"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
ISBN: 9781498714112
Dimensions: unknown
Weight: 566g
308 pages