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Statistical Machine Learning and Compressed Sensing Approaches for Analytical Electron Tomography - Application to Phase Change Materials

Published online by Cambridge University Press:  05 August 2019

Martin Jacob ,
Loubna El Gueddari ,
Gabriele Navarro ,
Marie-Claire Cyrille ,
Pascale Bayle-Guillemaud ,
Philippe Ciuciu  and
Zineb Saghi
Martin Jacob*
Affiliation:
Univ. Grenoble Alpes, CEA-LETI, Grenoble, France.
Loubna El Gueddari
Affiliation:
CEA-NeuroSpin, Bât 145 Gif-sur Yvette, France.
Gabriele Navarro
Affiliation:
Univ. Grenoble Alpes, CEA-LETI, Grenoble, France.
Marie-Claire Cyrille
Affiliation:
Univ. Grenoble Alpes, CEA-LETI, Grenoble, France.
Pascale Bayle-Guillemaud
Affiliation:
Univ. Grenoble Alpes, CEA-INAC, MEM, Grenoble, France.
Philippe Ciuciu
Affiliation:
CEA-NeuroSpin, Bât 145 Gif-sur Yvette, France.
Zineb Saghi
Affiliation:
Univ. Grenoble Alpes, CEA-LETI, Grenoble, France.
*
*Corresponding author: [email protected]

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. 156 - 157
Copyright
Copyright © Microscopy Society of America 2019 

References

[1]Collins, SM and Midgley, PA, Ultramicroscopy 180 (2017), p. 133.Google Scholar
[2]Dobigeon, N and Brun, N, Ultramicroscopy 120 (2012), p. 25.Google Scholar
[3]Leary, R, et al. , Ultramicroscopy 131 (2013), p. 70.Google Scholar
[4]Hyperspy v1.4.1, http://hyperspy.org.Google Scholar
[5]Gürsoy, D, et al. , J. Synchrotron Radiat. 21 (2014), p 1188.Google Scholar