Deep Learning, Dynamic Sampling and Smart Energy-dispersive Spectroscopy

Yan Zhang; G. M. Dilshan Godaliyadda; Nicola Ferrier; Emine B. Gulsoy; Charles A. Bouman; Charudatta Phatak

doi:10.1364/FIO.2017.FM3C.6

Frontiers in Optics 2017
OSA Technical Digest (online) (Optica Publishing Group, 2017),
paper FM3C.6
•https://doi.org/10.1364/FIO.2017.FM3C.6

Deep Learning, Dynamic Sampling and Smart Energy-dispersive Spectroscopy

Yan Zhang, G. M. Dilshan Godaliyadda, Nicola Ferrier, Emine B. Gulsoy, Charles A. Bouman, and Charudatta Phatak

Frontiers in Optics 2017

Washington, D.C. United States
18–21 September 2017
ISBN: 978-1-943580-33-0

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Computational/Transformation Optics and Optics in Computing (FM3C)

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Y. Zhang, G. M. Dilshan Godaliyadda, N. Ferrier, E. B. Gulsoy, C. A. Bouman, and C. Phatak, "Deep Learning, Dynamic Sampling and Smart Energy-dispersive Spectroscopy," in Frontiers in Optics 2017, OSA Technical Digest (online) (Optica Publishing Group, 2017), paper FM3C.6.

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Abstract

A convolutional neural network (CNN) classifier is trained using simulated energy-dispersive spectroscopy data. The CNN incorporated within a dynamic sampling method is to reduce radiation exposure and data acquisition time for elemental mapping.

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