Mostrar el registro sencillo del ítem

dc.contributor.author
Schlotthauer, Gaston  
dc.contributor.author
Torres, Maria Eugenia  
dc.contributor.author
Rufiner, Hugo Leonardo  
dc.contributor.author
Flandrin, Patrick  
dc.date.available
2020-05-08T19:46:25Z  
dc.date.issued
2009-09  
dc.identifier.citation
Schlotthauer, Gaston; Torres, Maria Eugenia; Rufiner, Hugo Leonardo; Flandrin, Patrick; EMD of Gaussian White Noise: Effects of Signal Length and Sifting Number on the Statistical Properties of Intrinsic Mode Functions; World Scientific Publishing; Advances in Adaptive Data Analysis; 1; 4; 9-2009; 517-527  
dc.identifier.issn
1793-7175  
dc.identifier.uri
http://hdl.handle.net/11336/104667  
dc.description.abstract
This work presents a discussion on the probability density function of Intrinsic Mode Functions (IMFs) provided by the Empirical Mode Decomposition of Gaussian white noise, based on experimental simulations. The influence on the probability density functions of the data length and of the maximum allowed number of iterations is analyzed by means of kernel smoothing density estimations. The obtained results are confirmed by statistical normality tests indicating that the IMFs have non-Gaussian distributions. Our study also indicates that large data length and high number of iterations produce multimodal distributions in all modes.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
World Scientific Publishing  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
EMPIRICAL MODE DESCOMPOSITION  
dc.subject
INTRINSIC MODE FUNTION  
dc.subject
GAUSSIAN WHITE NOISE  
dc.subject
SIFTING  
dc.subject.classification
Matemática Aplicada  
dc.subject.classification
Matemáticas  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
EMD of Gaussian White Noise: Effects of Signal Length and Sifting Number on the Statistical Properties of Intrinsic Mode Functions  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2020-04-28T13:09:01Z  
dc.identifier.eissn
1793-5369  
dc.journal.volume
1  
dc.journal.number
4  
dc.journal.pagination
517-527  
dc.journal.pais
Singapur  
dc.journal.ciudad
Singapore  
dc.description.fil
Fil: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina  
dc.description.fil
Fil: Torres, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; Argentina  
dc.description.fil
Fil: Rufiner, Hugo Leonardo. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina  
dc.description.fil
Fil: Flandrin, Patrick. École Normale Supérieure de Lyon; Francia  
dc.journal.title
Advances in Adaptive Data Analysis  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.worldscientific.com/doi/abs/10.1142/S1793536909000217  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1142/S1793536909000217  

Archivos asociados
Thumbnail
 
Tamaño: 600.2Kb
Formato: PDF
.