Descriptor based on spectraln peaks correlograms

Autores
Valdez Reyna, Brenda Lilia; Zivanovic, Miroslav
Año de publicación
2015
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
We have presented in this report a new peak descriptor SD based on the lag plot and discussed its relationship to the NBD. The SD, defined as the slope of a linear regression model constructed through the data samples belonging to a time sequence, shows to be a good candidate for describing sinusoidal and noise peak classes. A proper choice of data set is crucial for discerning correctly between the peak classes. The SD makes use only of the spectral peak shape and consequently this information can be explained by just two parameters, namely the root mean square BWrms and absolute bandwidth L. This is similar to the way the shape factor explains the filter spectral shape in the circuit theory. We have shown that BWrms and L, initially defined in the spectrum domain, hold relationship to the time duration of the data set and its sampling rate respectively
VI Workshop Procesamiento de Señales y Sistemas de Tiempo Real (WPSTR)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Spectral methods
correlograma
lag plot
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/50546
Acceder
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network_name_str SEDICI (UNLP)
spelling Descriptor based on spectraln peaks correlogramsValdez Reyna, Brenda LiliaZivanovic, MiroslavCiencias InformáticasSpectral methodscorrelogramalag plotWe have presented in this report a new peak descriptor SD based on the lag plot and discussed its relationship to the NBD. The SD, defined as the slope of a linear regression model constructed through the data samples belonging to a time sequence, shows to be a good candidate for describing sinusoidal and noise peak classes. A proper choice of data set is crucial for discerning correctly between the peak classes. The SD makes use only of the spectral peak shape and consequently this information can be explained by just two parameters, namely the root mean square BWrms and absolute bandwidth L. This is similar to the way the shape factor explains the filter spectral shape in the circuit theory. We have shown that BWrms and L, initially defined in the spectrum domain, hold relationship to the time duration of the data set and its sampling rate respectivelyVI Workshop Procesamiento de Señales y Sistemas de Tiempo Real (WPSTR)Red de Universidades con Carreras en Informática (RedUNCI)2015-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/50546enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-3806-05-6info:eu-repo/semantics/reference/hdl/10915/50028info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T16:45:29Zoai:sedici.unlp.edu.ar:10915/50546Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:45:29.868SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Descriptor based on spectraln peaks correlograms
title Descriptor based on spectraln peaks correlograms
spellingShingle Descriptor based on spectraln peaks correlograms
Valdez Reyna, Brenda Lilia
Ciencias Informáticas
Spectral methods
correlograma
lag plot
title_short Descriptor based on spectraln peaks correlograms
title_full Descriptor based on spectraln peaks correlograms
title_fullStr Descriptor based on spectraln peaks correlograms
title_full_unstemmed Descriptor based on spectraln peaks correlograms
title_sort Descriptor based on spectraln peaks correlograms
dc.creator.none.fl_str_mv Valdez Reyna, Brenda Lilia
Zivanovic, Miroslav
author Valdez Reyna, Brenda Lilia
author_facet Valdez Reyna, Brenda Lilia
Zivanovic, Miroslav
author_role author
author2 Zivanovic, Miroslav
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Spectral methods
correlograma
lag plot
topic Ciencias Informáticas
Spectral methods
correlograma
lag plot
dc.description.none.fl_txt_mv We have presented in this report a new peak descriptor SD based on the lag plot and discussed its relationship to the NBD. The SD, defined as the slope of a linear regression model constructed through the data samples belonging to a time sequence, shows to be a good candidate for describing sinusoidal and noise peak classes. A proper choice of data set is crucial for discerning correctly between the peak classes. The SD makes use only of the spectral peak shape and consequently this information can be explained by just two parameters, namely the root mean square BWrms and absolute bandwidth L. This is similar to the way the shape factor explains the filter spectral shape in the circuit theory. We have shown that BWrms and L, initially defined in the spectrum domain, hold relationship to the time duration of the data set and its sampling rate respectively
VI Workshop Procesamiento de Señales y Sistemas de Tiempo Real (WPSTR)
Red de Universidades con Carreras en Informática (RedUNCI)
description We have presented in this report a new peak descriptor SD based on the lag plot and discussed its relationship to the NBD. The SD, defined as the slope of a linear regression model constructed through the data samples belonging to a time sequence, shows to be a good candidate for describing sinusoidal and noise peak classes. A proper choice of data set is crucial for discerning correctly between the peak classes. The SD makes use only of the spectral peak shape and consequently this information can be explained by just two parameters, namely the root mean square BWrms and absolute bandwidth L. This is similar to the way the shape factor explains the filter spectral shape in the circuit theory. We have shown that BWrms and L, initially defined in the spectrum domain, hold relationship to the time duration of the data set and its sampling rate respectively
publishDate 2015
dc.date.none.fl_str_mv 2015-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/50546
url http://sedici.unlp.edu.ar/handle/10915/50546
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-987-3806-05-6
info:eu-repo/semantics/reference/hdl/10915/50028
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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