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The usage of data compression for the background estimation of electron energy loss spectra
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Materials Theory.
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Materials Theory.ORCID iD: 0000-0002-0074-1349
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Applied Materials Sciences.ORCID iD: 0000-0002-8360-1877
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Applied Materials Sciences.
2017 (English)In: Ultramicroscopy, ISSN 0304-3991, E-ISSN 1879-2723, Vol. 181, 117-122 p.Article in journal (Refereed) Published
Abstract [en]

Quantitative analysis of noisy electron spectrum images requires a robust estimation of the underlying background signal. We demonstrate how modern data compression methods can be used as a tool for achieving an analysis result less affected by statistical errors or to speed up the background estimation. In particular, we demonstrate how a multilinear singular value decomposition (MLSVD) can be used to enhance elemental maps obtained from a complex sample measured with energy electron loss spectroscopy. Furthermore, the usage of vertex component analysis (VCA) for a basis vector centered estimation of the background is demonstrated. Arising computational benefits in terms of model accuracy and computational costs are studied.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2017. Vol. 181, 117-122 p.
National Category
Physical Sciences Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-336443DOI: 10.1016/j.ultramic.2017.05.017ISI: 000411170800014PubMedID: 28549246OAI: oai:DiVA.org:uu-336443DiVA: diva2:1165952
Funder
Swedish Research CouncilThe Swedish Foundation for International Cooperation in Research and Higher Education (STINT)Göran Gustafsson Foundation for Research in Natural Sciences and Medicine
Available from: 2017-12-14 Created: 2017-12-14 Last updated: 2017-12-15Bibliographically approved

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Spiegelberg, JakobRusz, JanLEIFER, KLAUS

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