Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/3379
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dc.contributor.advisorSavaci, Ferit Acaren
dc.contributor.authorÇek, Mehmet Emre-
dc.date.accessioned2014-07-22T13:51:25Z
dc.date.available2014-07-22T13:51:25Z
dc.date.issued2004en
dc.identifier.urihttp://hdl.handle.net/11147/3379
dc.descriptionThesis (Master)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2004en
dc.descriptionIncludes bibliographical references (leaves: 86)en
dc.descriptionText in English; Abstract: Turkish and Englishen
dc.descriptionxii, 89 leavesen
dc.description.abstractIn this thesis, analysis of observed chaotic data has been investigated. The purpose of analyzing time series is to make a classification between the signals observed from dynamical systems. The classifiers are the invariants related to the dynamics. The correlation dimension has been used as classifier which has been obtained after phase space reconstruction. Therefore, necessary methods to find the phase space parameters which are time delay and the embedding dimension have been offered. Since observed time series practically are contaminated by noise, the invariants of dynamical system can not be reached without noise reduction. The noise reduction has been performed by the new proposed singular value decomposition based rank estimation method.Another classification has been realized by analyzing time-frequency characteristics of the signals. The time-frequency distribution has been investigated by wavelet transform since it supplies flexible time-frequency window. Classification in wavelet domain has been performed by wavelet entropy which is expressed by the sum of relative wavelet energies specified in certain frequency bands. Another wavelet based classification has been done by using the wavelet ridges where the energy is relatively maximum in time-frequency domain. These new proposed analysis methods have been applied to electrical signals taken from healthy human brains and the results have been compared with other studies.en
dc.language.isoenen_US
dc.publisherIzmir Institute of Technologyen
dc.publisherIzmir Institute of Technologyen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subject.lccQ172.5.C45 .C39 2004en
dc.subject.lcshChaotic behavior in systemsen
dc.titleAnalysis of observed chaotic dataen_US
dc.typeMaster Thesisen_US
dc.authoridTR20599
dc.institutionauthorÇek, Mehmet Emre-
dc.departmentThesis (Master)--İzmir Institute of Technology, Electrical and Electronics Engineeringen_US
dc.relation.publicationcategoryTezen_US
item.openairetypeMaster Thesis-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
Appears in Collections:Master Degree / Yüksek Lisans Tezleri
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