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NEW DIRECTIONS IN RADIOISOTOPE SPECTRUM IDENTIFICATION [electronic resource].

Published
Washington, D.C. : United States. Dept. of Energy, 2010.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
Additional Creators
United States. Department of Energy. Savannah River Site, United States. Department of Energy, and United States. Department of Energy. Office of Scientific and Technical Information
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Summary
Recent studies have found the performance of commercial handheld detectors with automatic RIID software to be less than acceptable. Previously, we have explored approaches rooted in speech processing such as cepstral features and information-theoretic measures. Scientific advances are often made when researchers identify mathematical or physical commonalities between different fields and are able to apply mature techniques or algorithms developed in one field to another field which shares some of the same challenges. The authors of this paper have identified similarities between the unsolved problems faced in gamma-spectroscopy for automated radioisotope identification and the challenges of the much larger body of research in speech processing. Our research has led to a probabilistic framework for describing and solving radioisotope identification problems. Many heuristic approaches to classification in current use, including for radioisotope classification, make implicit probabilistic assumptions which are not clear to the users and, if stated explicitly, might not be considered desirable. Our framework leads to a classification approach with demonstrable improvements using standard feature sets on proof-of-concept simulated and field-collected data.
Report Numbers
E 1.99:srnl-l2200-2010-00083
srnl-l2200-2010-00083
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Note
Published through SciTech Connect.
06/17/2010.
"srnl-l2200-2010-00083"
51st INMM Annual Meeting.
Salaymeh, S.; Jeffcoat, R.
Funding Information
DE-AC09-08SR22470
View MARC record | catkey: 14759279