Description
Book SynopsisNonlinear 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
Trade Review
"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
Table of Contents
Introduction. Python. Linear and Volterra Filters. Median Filters and Some Extensions. Forms of Nonlinear Behavior. Composite Structures: Bottom-Up Design. Recursive Structures and Stability.
