Please use this identifier to cite or link to this item:
http://hdl.handle.net/2122/13816
DC Field | Value | Language |
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dc.date.accessioned | 2020-10-21T05:35:25Z | - |
dc.date.available | 2020-10-21T05:35:25Z | - |
dc.date.issued | 2020-09-16 | - |
dc.identifier.uri | http://hdl.handle.net/2122/13816 | - |
dc.description.abstract | Algorithms based on Empirical Mode Decomposition (EMD) and Iterative Filtering (IF) are largely implemented for representing a signal as superposition of simpler well-behaved components called Intrinsic Mode Functions (IMFs). Although they are more suitable than traditional methods for the analysis of nonlinear and nonstationary signals, they could be easily misused if their known limitations, together with the assumptions they rely on, are not carefully considered. In this work, we examine the main pitfalls and provide caveats for the proper use of the EMD- and IF-based algorithms. Specifically, we address the problems related to boundary errors, to the presence of spikes or jumps in the signal and to the decomposition of highly-stochastic signals. The consequences of an improper usage of these techniques are discussed and clarified also by analysing real data and performing numerical simulations. Finally, we provide the reader with the best practices to maximize the quality and meaningfulness of the decomposition produced by these techniques. In particular, a technique for the extension of signal to reduce the boundary effects is proposed; a careful handling of spikes and jumps in the signal is suggested; the concept of multi-scale statistical analysis is presented to treat highly stochastic signals. | en_US |
dc.language.iso | English | en_US |
dc.publisher.name | Nature P.G. | en_US |
dc.relation.ispartof | Scientific Reports | en_US |
dc.relation.ispartofseries | /10 (2020) | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.title | New insights and best practices for the successful use of Empirical Mode Decomposition, Iterative Filtering and derived algorithms | en_US |
dc.type | article | en |
dc.description.status | Published | en_US |
dc.type.QualityControl | Peer-reviewed | en_US |
dc.description.pagenumber | 15161 | en_US |
dc.identifier.doi | 10.1038/s41598-020-72193-2 | en_US |
dc.description.obiettivoSpecifico | 5T. Sismologia, geofisica e geologia per l'ingegneria sismica | en_US |
dc.description.journalType | JCR Journal | en_US |
dc.contributor.author | Stallone, Angela | - |
dc.contributor.author | Cicone, Antonio | - |
dc.contributor.author | Materassi, Massimo | - |
dc.contributor.department | Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Roma1, Roma, Italia | en_US |
dc.contributor.department | Istituto di Astrofisica e Planetologia Spaziali dell’Istituto Nazionale di Astrofisica | en_US |
dc.contributor.department | taly. 3Istituto dei Sistemi Complessi del Consiglio Nazionale delle Ricerche (ISC-CNR) | en_US |
item.openairetype | article | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.grantfulltext | open | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
crisitem.author.dept | Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Roma1, Roma, Italia | - |
crisitem.author.dept | Istituto dei Sistemi Complessi, Consiglio Nazionale delle Ricerche | - |
crisitem.author.orcid | 0000-0002-8141-017X | - |
crisitem.author.orcid | 0000-0002-8107-9624 | - |
crisitem.author.parentorg | Istituto Nazionale di Geofisica e Vulcanologia | - |
crisitem.department.parentorg | Istituto Nazionale di Geofisica e Vulcanologia | - |
Appears in Collections: | Article published / in press |
Files in This Item:
File | Description | Size | Format | |
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SR_2020_Stallone_Cicone_Materassi.pdf | 3.42 MB | Adobe PDF | View/Open |
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