Por favor, use este identificador para citar o enlazar este ítem: http://repositorio.utmachala.edu.ec/handle/48000/6511
metadata.dc.type: article
Título : Implementation of a Fast Detector Using a Supervised Machine Learning And Gabor Wavelet Feature Descriptors.
Autor : Tusa Jumbo, Eduardo
Palabras clave : PROCEEDINGS OF THE IEEE;GABOR WAVELET;PANEL DE SISTEMAS DE ENERGIA;ENERGIA ELECTRICA
Fecha de publicación : 2014
Editorial : United States
metadata.dc.rights: openAccess
metadata.dc.rights.uri: http://creativecommons.org/licenses/by-nc-sa/3.0/ec/
Citación : Tusa Jumbo, E. (2014) Implementation of a Fast Detector Using a Supervised Machine Learning And Gabor Wavelet Feature Descriptors. Proceedings of the IEEE.
metadata.dc.identifier.other: AC 014
Resumen : The basic purpose of IEEE 127 is to serve as a guide to show preferred (steady-state) frequency and voltage ratings for primary power systems. A careful study, was accomplished by the Flight Vehicle Systems Integration Subcommittee. The attached tabulation gives recommended standard frequencies and voltages for all types of aerospace electric apparatus for which standards appear practical.
URI : http://repositorio.utmachala.edu.ec/handle/48000/6511
ISSN : 14839479
Otros identificadores : Implementation of a Fast Detector Using a Supervised Machine Learning And Gabor Wavelet Feature Descriptors.
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