Minimum entropy deconvolution and simplicity: a noniterative algorithm.

Minimum entropy deconvolution (MED) is a technique with the purpose of separating the components of a signal, as the convolution model of a smooth wavelet with a series of impulses. The advantage of this method, as compared with traditional methods, is that it obviates strong hypotheses over the com...

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Detalles Bibliográficos
Autor principal:	Cabrelli, Carlos Alberto
Publicado:	1985
Materias:	COMPUTER PROGRAMMING - Algorithms STATISTICAL METHODS FACTOR ANALYSIS SIGNAL FILTERING AND PREDICTION
Acceso en línea:	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00168033_v50_n3_p394_Cabrelli http://hdl.handle.net/20.500.12110/paper_00168033_v50_n3_p394_Cabrelli
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	paper:paper_00168033_v50_n3_p394_Cabrelli
record_format	dspace
spelling	paper:paper_00168033_v50_n3_p394_Cabrelli2023-06-08T14:38:59Z Minimum entropy deconvolution and simplicity: a noniterative algorithm. Cabrelli, Carlos Alberto COMPUTER PROGRAMMING - Algorithms STATISTICAL METHODS FACTOR ANALYSIS SIGNAL FILTERING AND PREDICTION Minimum entropy deconvolution (MED) is a technique with the purpose of separating the components of a signal, as the convolution model of a smooth wavelet with a series of impulses. The advantage of this method, as compared with traditional methods, is that it obviates strong hypotheses over the components, which require only the simplicity of the output. The degree of simplicity is measured with the Varimax norm for factor analysis. An iterative algorithm for computation of the filter is derived from this norm, having as an outstanding characteristic its stability in presence of noise. Geometrical analysis of the Varimax norm suggests the definition of a new criterion for simplicity: the D norm. A section of numerical examples is included, where the results of an extensive simulation study with synthetic data are analyzed. The numerical computations show in all cases a remarkable improvement resulting from use of the D norm. The properties of stability in the presence of noise are preserved as shown in the examples.-from Author Fil:Cabrelli, C.A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 1985 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00168033_v50_n3_p394_Cabrelli http://hdl.handle.net/20.500.12110/paper_00168033_v50_n3_p394_Cabrelli
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	COMPUTER PROGRAMMING - Algorithms STATISTICAL METHODS FACTOR ANALYSIS SIGNAL FILTERING AND PREDICTION
spellingShingle	COMPUTER PROGRAMMING - Algorithms STATISTICAL METHODS FACTOR ANALYSIS SIGNAL FILTERING AND PREDICTION Cabrelli, Carlos Alberto Minimum entropy deconvolution and simplicity: a noniterative algorithm.
topic_facet	COMPUTER PROGRAMMING - Algorithms STATISTICAL METHODS FACTOR ANALYSIS SIGNAL FILTERING AND PREDICTION
description	Minimum entropy deconvolution (MED) is a technique with the purpose of separating the components of a signal, as the convolution model of a smooth wavelet with a series of impulses. The advantage of this method, as compared with traditional methods, is that it obviates strong hypotheses over the components, which require only the simplicity of the output. The degree of simplicity is measured with the Varimax norm for factor analysis. An iterative algorithm for computation of the filter is derived from this norm, having as an outstanding characteristic its stability in presence of noise. Geometrical analysis of the Varimax norm suggests the definition of a new criterion for simplicity: the D norm. A section of numerical examples is included, where the results of an extensive simulation study with synthetic data are analyzed. The numerical computations show in all cases a remarkable improvement resulting from use of the D norm. The properties of stability in the presence of noise are preserved as shown in the examples.-from Author
author	Cabrelli, Carlos Alberto
author_facet	Cabrelli, Carlos Alberto
author_sort	Cabrelli, Carlos Alberto
title	Minimum entropy deconvolution and simplicity: a noniterative algorithm.
title_short	Minimum entropy deconvolution and simplicity: a noniterative algorithm.
title_full	Minimum entropy deconvolution and simplicity: a noniterative algorithm.
title_fullStr	Minimum entropy deconvolution and simplicity: a noniterative algorithm.
title_full_unstemmed	Minimum entropy deconvolution and simplicity: a noniterative algorithm.
title_sort	minimum entropy deconvolution and simplicity: a noniterative algorithm.
publishDate	1985
url	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00168033_v50_n3_p394_Cabrelli http://hdl.handle.net/20.500.12110/paper_00168033_v50_n3_p394_Cabrelli
work_keys_str_mv	AT cabrellicarlosalberto minimumentropydeconvolutionandsimplicityanoniterativealgorithm
_version_	1768542533646811136

Minimum entropy deconvolution and simplicity: a noniterative algorithm.

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