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Machine Learning for Challenging EELS and EDS Spectral Decomposition

Published online by Cambridge University Press:  05 August 2019

Thomas Blum ,
Jeffery Graves ,
Michael Zachman ,
Ramakrishnan Kannan ,
Xiaoqing Pan  and
Miaofang Chi
Thomas Blum*
Affiliation:
Physics & Astronomy, University of California, Irvine, Irvine, CA, USA Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Jeffery Graves
Affiliation:
Computational Data Analytics, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Michael Zachman
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Ramakrishnan Kannan
Affiliation:
Computational Data Analytics, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Xiaoqing Pan
Affiliation:
Physics & Astronomy, University of California, Irvine, Irvine, CA, USA
Miaofang Chi*
Affiliation:
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
*
*Corresponding Author: tblum@uci.edu, chim@ornl.gov
*Corresponding Author: tblum@uci.edu, chim@ornl.gov

Abstract

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Type
Data Acquisition Schemes, Machine Learning Algorithms, and Open Source Software Development for Electron Microscopy
Information
Microscopy and Microanalysis , Volume 25 , Supplement S2 , August 2019 , pp. 180 - 181
Copyright
Copyright © Microscopy Society of America 2019 

References

[1]Graves, J et al. , Submitted (2018)Google Scholar
[2]Ren, H, Chang, C, IEEE Transactions on Aerospace and Electronic Systems 39 (2004), p. 1232Google Scholar
[3]Kannan, R et al. , Advanced Structural and Chemical Imaging 4 (2018), p. 6Google Scholar
[4]This work is supported by ORNL's Laboratory Directed Research and Development (LDRD) funds. The microscopy work was performed at ORNL's Center for Nanophase Material Sciences, which is a U. S. Department of Energy Office of Science User Facility.Google Scholar