| Neural networks for blind-source separation of Stromboli explosion quakes. | |
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MedLine Citation:
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PMID: 18237999 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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Independent component analysis (ICA) is used to analyze the seismic signals produced by explosions of the Stromboli volcano. It has been experimentally proved that it is possible to extract the most significant components from seismometer recorders. In particular, the signal, eventually thought as generated by the source, is corresponding to the higher power spectrum, isolated by our analysis. Furthermore, the amplitude of the source signals has been found by using a simple trick and so overcoming, for this specific case, the classical problem of ICA regarding the amplitude loss of the separated signals. |
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Authors:
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F Acernese; A Ciaramella; S De Martino; R De Rosa; M Falanga; R Tagliaferri |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council Volume: 14 ISSN: 1045-9227 ISO Abbreviation: IEEE Trans Neural Netw Publication Date: 2003 |
Date Detail:
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Created Date: 2008-02-01 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101211035 Medline TA: IEEE Trans Neural Netw Country: United States |
Other Details:
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Languages: eng Pagination: 167-75 Citation Subset: - |
Affiliation:
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Dipt. di Sci. Fisiche, Universita di Napoli "Federico II", Italy. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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