Big data analysis applied to EEL spectroscopy

Sospedra Ramírez, Joan

Big data analysis applied to EEL spectroscopy

dc.contributor.advisor	Estradé Albiol, Sònia
dc.contributor.advisor	Blanco Portals, Javier
dc.contributor.author	Sospedra Ramírez, Joan
dc.date.accessioned	2018-10-10T13:47:16Z
dc.date.available	2018-10-10T13:47:16Z
dc.date.issued	2018-06
dc.description	Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2018, Tutors: Sonia Estradé Albiol, Javier Blanco Portals	ca
dc.description.abstract	In 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 recognized	ca
dc.format.extent	5 p.
dc.format.mimetype	application/pdf
dc.identifier.uri	https://hdl.handle.net/2445/125267
dc.language.iso	eng	ca
dc.rights	cc-by-nc-nd (c) Sospedra, 2018
dc.rights.accessRights	info:eu-repo/semantics/openAccess	ca
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/es/	*
dc.source	Treballs Finals de Grau (TFG) - Física
dc.subject.classification	Espectroscòpia de pèrdua d'energia d'electrons	cat
dc.subject.classification	Dades massives	cat
dc.subject.classification	Treballs de fi de grau	cat
dc.subject.other	Electron energy loss spectroscopy	eng
dc.subject.other	Big data	eng
dc.subject.other	Bachelor's theses	eng
dc.title	Big data analysis applied to EEL spectroscopy	eng
dc.type	info:eu-repo/semantics/bachelorThesis	ca

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Treballs Finals de Grau (TFG) - Física