NEW DIRECTIONS IN RADIOISOTOPE SPECTRUM IDENTIFICATION

Conference · Thu Jun 17 04:00:00 EDT 2010

OSTI ID:982363

Salaymeh, S; Jeffcoat, R

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.

• Research Organization: SRS
• Sponsoring Organization: DOE
• DOE Contract Number: AC09-08SR22470
• OSTI ID: 982363
• Report Number(s): SRNL-L2200-2010-00083
• Country of Publication: United States
• Language: English

Related Subjects

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEAR CHEMISTRY
ALGORITHMS
CLASSIFICATION
GAMMA SPECTROSCOPY
IDENTIFICATION SYSTEMS
PERFORMANCE
RADIOISOTOPES