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English
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Nonlinear Digital Filtering with Python: An Introduction discusses important structural filter class...
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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.
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About the book
Full name
Nonlinear Digital Filtering with Python
Author
Moncef Gabbouj
Language
English
English
Binding
Book - Hardback
Date of issue
2015
Number of pages
286
EAN
9781498714112
ISBN
9781498714112
Libristo code
09407283
Publishers
Taylor & Francis Inc
Weight
630
Dimensions
163 x 243 x 23
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