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dc.contributor.author
Piersanti, M.  
dc.contributor.author
Materassi, M.  
dc.contributor.author
Cicone, A.  
dc.contributor.author
Spogli, L.  
dc.contributor.author
Zhou, H.  
dc.contributor.author
Ezquer, Rodolfo Gerardo  
dc.date.available
2018-12-10T14:23:00Z  
dc.date.issued
2017-12  
dc.identifier.citation
Piersanti, M.; Materassi, M.; Cicone, A.; Spogli, L.; Zhou, H.; et al.; Adaptive local iterative filtering: A promising technique for the analysis of nonstationary signals; American Geophysical Union; Journal of Geophysical Research; 123; 1; 12-2017; 1031-1046  
dc.identifier.issn
0148-0227  
dc.identifier.uri
http://hdl.handle.net/11336/66165  
dc.description.abstract
Many real-life signals and, in particular, in the space physics domain, exhibit variations acrossdifferent temporal scales. Hence, their statistical momenta may depend on the time scale at which the signal is studied. To identify and quantify such variations, a time-frequency analysis has to be performed on these signals. The dependence of the statistical properties of a signal fluctuation on the space and time scales is the distinctive character of systems with nonlinear couplings among different modes. Hence, assessing how the statistics of signal fluctuations vary with scale will be of help in understanding the corresponding multiscale statistics of such dynamics. This paper presents a new multiscale data analysis technique, the adaptive local iterative filtering (ALIF), which allows to describe the multiscale nature of the geophysical signal studied better than via Fourier transform, and improves scale resolution with respect to discrete wavelet transform. The example of geophysical signal, to which ALIF has been applied, is ionospheric radio power scintillation on L band. ALIF appears to be a promising technique to study the small-scale structures of radio scintillation due to ionospheric turbulence.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
American Geophysical Union  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
New Data Analisys Tool  
dc.subject
Nonstationary And Nonlinearity  
dc.subject
Statistical Analysis  
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Radio Scintillation Signal  
dc.subject.classification
Meteorología y Ciencias Atmosféricas  
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Ciencias de la Tierra y relacionadas con el Medio Ambiente  
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CIENCIAS NATURALES Y EXACTAS  
dc.title
Adaptive local iterative filtering: A promising technique for the analysis of nonstationary signals  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2018-12-05T14:46:35Z  
dc.journal.volume
123  
dc.journal.number
1  
dc.journal.pagination
1031-1046  
dc.journal.pais
Estados Unidos  
dc.journal.ciudad
Nueva York  
dc.description.fil
Fil: Piersanti, M.. University Of L Aquila, L Aquila, Italy; Italia  
dc.description.fil
Fil: Materassi, M.. National Research Council, Rome, Italy; Italia  
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Fil: Cicone, A.. Universitá Degli Studi Dell Aquila, L Aquila, Italy; Italia  
dc.description.fil
Fil: Spogli, L.. Istituto Nazionale Di Geofisica E Vulcanologia; Italia  
dc.description.fil
Fil: Zhou, H.. Georgia Institute Of Technology, Atlanta, Ga, Usa; Estados Unidos  
dc.description.fil
Fil: Ezquer, Rodolfo Gerardo. Universidad Tecnológica Nacional; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Física. Laboratorio de Ionosfera; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina  
dc.journal.title
Journal of Geophysical Research  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1002/2017JA024153  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2017JA024153