000			02177 a2200325 4500
001			1498714137
005			20250317111625.0
008			250312042018xx 39 eng
020			_a9781498714136
037			_bTaylor & Francis _cGBP 52.99 _fBB
040			_a01
041			_aeng
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072	7		_aMQW _2thema
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072	7		_aMED009000 _2bisac
072	7		_aTEC007000 _2bisac
072	7		_aTEC061000 _2bisac
072	7		_a515.252 _2bisac
100	1		_aRonald K. Pearson
245	1	0	_aNonlinear Digital Filtering with Python _bAn Introduction
250			_a1
260			_bCRC Press _c20180903
300			_a300 p
520			_bNonlinear 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.
700	1		_aMoncef Gabbouj _4A01
999			_c6139 _d6139