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An Application of CNNs to Time Sequenced One Dimensional Data in Radiation Detection

Autor: Moore, Eric T., Ford, William P., Hague, Emma J., Turk, Johanna

Publikováno v: Proc. SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, 109861C (14 May 2019); https://doi.org/10.1117/12.2519037

A Convolutional Neural Network architecture was used to classify various isotopes of time-sequenced gamma-ray spectra, a typical output of a radiation detection system of a type commonly fielded for security or environmental measurement purposes. A t

Externí odkaz: http://arxiv.org/abs/1908.10887

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Threat determination for radiation detection from the Remote Sensing Laboratory

Autor: Ford, William P., Hague, Emma, McCullough, Tom, Moore, Eric, Turk, Johanna

Publikováno v: Proceedings of the SPIE, Volume 10644, id. 106440G 7 pp. (2018)

The ability to search for radiation sources is of interest to the Homeland Security community. The hope is to find any radiation sources which may pose a reasonable chance for harm in a terrorist act. The best chance of success for search operations

Externí odkaz: http://arxiv.org/abs/1908.11207

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A Comparison of Adaptive and Template Matching Techniques for Radio-Isotope Identification

Autor: Hague, Emma J., Kamuda, Mark, Ford, William P., Moore, Eric T., Turk, Johanna

Publikováno v: Proc. SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, 1098608 (14 May 2019)

We compare and contrast the effectiveness of a set of adaptive and non-adaptive algorithms for isotope identification based on gamma-ray spectra. One dimensional energy spectra are simulated for a variety of dwell-times and source to detector distanc

Externí odkaz: http://arxiv.org/abs/1908.09816

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