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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, p. 117-122Article 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, p. 117-122
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, id: 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: 2018-04-11Bibliographically approved
In thesis
1. Signal Processing Tools for Electron Microscopy
Open this publication in new window or tab >>Signal Processing Tools for Electron Microscopy
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The detection of weak signals in noisy data is a problem which occurs across various disciplines. Here, the signal of interest is the spectral signature of the electron magnetic chiral dichroism (EMCD) effect. In principle, EMCD allows for the measurement of local magnetic structures in the electron microscope, its spatial resolution, versatility and low hardware requirements giving it an eminent position among competing measurement techniques. However, experimental shortcomings as well as intrinsically low signal to noise ratio render its measurement challenging to the present day.   

This thesis explores how posterior data processing may aid the analysis of various data from the electron microscope. Following a brief introduction to different signals arising in the microscope and a yet briefer survey of the state of the art of EMCD measurements, noise removal strategies are presented. Afterwards, gears are shifted to discuss the separation of mixed signals into their physically meaningful source components based on their assumed mathematical characteristics, so called blind source separation (BSS).    

A data processing workflow for detecting weak signals in noisy spectra is derived from these considerations, ultimately culminating in several demonstrations of the extraction of EMCD signals. While the focus of the thesis does lie on data processing strategies for EMCD detection, the approaches presented here are similarly applicable in other situations. Related topics such as the general analysis of hyperspectral images using BSS methods or the fast analysis of large data sets are also discussed.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2018. p. 60
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1672
National Category
Physical Sciences Computer Sciences Other Mathematics
Identifiers
urn:nbn:se:uu:diva-348264 (URN)978-91-513-0345-1 (ISBN)
Public defence
2018-06-12, Å2001, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 09:00 (English)
Opponent
Supervisors
Available from: 2018-05-18 Created: 2018-04-11 Last updated: 2018-05-18

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Spiegelberg, JakobRusz, JanLEIFER, KLAUS

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