Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/125267
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dc.contributor.advisorEstradé Albiol, Sònia-
dc.contributor.advisorBlanco Portals, Javier-
dc.contributor.authorSospedra Ramírez, Joan-
dc.date.accessioned2018-10-10T13:47:16Z-
dc.date.available2018-10-10T13:47:16Z-
dc.date.issued2018-06-
dc.identifier.urihttps://hdl.handle.net/2445/125267-
dc.descriptionTreballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2018, Tutors: Sonia Estradé Albiol, Javier Blanco Portalsca
dc.description.abstractIn order to characterize an unknown sample, big data and machine learning methods are proposed. An electron energy loss (EEL) spectrum image obtained in the transmission electron microscope is analyzed. Applying principal component analysis (PCA) to image EEL spectra, the noise present in the raw data can be discarded, and comparing with existing datasets and alternatively through clustering analysis, the presence of vanadium and oxygen in the sample over a substrate with lanthanum and oxygen can be recognizedca
dc.format.extent5 p.-
dc.format.mimetypeapplication/pdf-
dc.language.isoengca
dc.rightscc-by-nc-nd (c) Sospedra, 2018-
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.sourceTreballs Finals de Grau (TFG) - Física-
dc.subject.classificationEspectroscòpia de pèrdua d'energia d'electronscat
dc.subject.classificationDades massivescat
dc.subject.classificationTreballs de fi de graucat
dc.subject.otherElectron energy loss spectroscopyeng
dc.subject.otherBig dataeng
dc.subject.otherBachelor's theseseng
dc.titleBig data analysis applied to EEL spectroscopyeng
dc.typeinfo:eu-repo/semantics/bachelorThesisca
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca
Appears in Collections:Treballs Finals de Grau (TFG) - Física

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