Nonlinear Digital Filtering with Python : an Introduction

"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...

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Bibliographic Details
Main Author: Pearson, Ronald K., (Author)
Other Authors: Gabbouj, Moncef.
Format: eBook
Language: English
Published: London : Taylor and Francis, 2017.
Edition: First edition.
Subjects:
Digital Signal Processing.
Digital & Wireless Communication.
Biomedical Engineering.
elektronické knihy
electronic books
ISBN: 9781498714136
1498714137
Physical Description: 1 online resource : text file, PDF

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245 1 0 |a Nonlinear Digital Filtering with Python :  |b an Introduction /  |c Ronald K. Pearson. 
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264 1 |a London :  |b Taylor and Francis,  |c 2017. 
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520 2 |a "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 PythonUses results from algebra and the theory of functional equations to construct and characterize behaviorally defined nonlinear filter classesAnalyzes the impact of a range of useful interconnection strategies on filter behavior, providing Python implementations of the presented filters and interconnection strategiesProposes practical, bottom-up strategies for designing more complex and capable filters from simpler components in a way that preserves the key properties of these componentsIllustrates the behavioral consequences of allowing recursive (i.e., feedback) interconnections in nonlinear digital filters while highlighting a challenging but promising research frontierNonlinear 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."--Provided by publisher. 
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650 0 4 |a Digital Signal Processing. 
650 0 4 |a Digital & Wireless Communication. 
650 0 4 |a Biomedical Engineering. 
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700 1 |a Gabbouj, Moncef. 
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