{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/95304"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/95304","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Automated isotope identification algorithms for low-resolution gamma spectrometers","abstract":"Handheld radio-isotope identifiers (RIIDs) are widely deployed for nuclear security applications, but these detectors generally have poor isotope identification performance. Most of these deployed detectors use low-resolution NaI scintillators due to their low cost and good efficiency. Alternative detection hardware could be used to improve performance, but better detectors are generally cost-prohibitive for wide deployment in this mission space. However, a trained spectroscopist can use these low-resolution detectors to make much more accurate identifications than the RIIDs produce. For this reason, it has been suggested that these RIIDs could be significantly improved by changing the onboard identification algorithms. To this end, a peak-based Bayesian classifier has been developed to perform automated isotope identification. This algorithm was constructed to mimic the manual identification that a spectroscopist would perform. This approach can handle challenges such as detector calibration drift and unknown shielding scenarios, is capable of identifying mixed radiation sources, and is computationally inexpensive enough to be feasible for deployment on handheld RIID systems. A method for easily generating isotope libraries that are coupled to the detector and to the feature extraction algorithm is presented as well. This method is demonstrated on a broad variety of gamma-ray spectra, ranging from small calibration sources ($<$ 1 $\\mu$Ci) to Category I quantities of special nuclear material. This algorithm is also benchmarked against the ANSI N42.34-2006 Standard for Handheld Identifiers as a part of the Department of Homeland Security Algorithm Improvement Program.","abstract_html":"Handheld radio-isotope identifiers (RIIDs) are widely deployed for nuclear security applications, but these detectors generally have poor isotope identification performance. Most of these deployed detectors use low-resolution NaI scintillators due to their low cost and good efficiency. Alternative detection hardware could be used to improve performance, but better detectors are generally cost-prohibitive for wide deployment in this mission space. However, a trained spectroscopist can use these low-resolution detectors to make much more accurate identifications than the RIIDs produce. For this reason, it has been suggested that these RIIDs could be significantly improved by changing the onboard identification algorithms. To this end, a peak-based Bayesian classifier has been developed to perform automated isotope identification. This algorithm was constructed to mimic the manual identification that a spectroscopist would perform. This approach can handle challenges such as detector calibration drift and unknown shielding scenarios, is capable of identifying mixed radiation sources, and is computationally inexpensive enough to be feasible for deployment on handheld RIID systems. A method for easily generating isotope libraries that are coupled to the detector and to the feature extraction algorithm is presented as well. This method is demonstrated on a broad variety of gamma-ray spectra, ranging from small calibration sources ($&lt;$ 1 <span class=\"etd-inline-math\">&mu;</span>Ci) to Category I quantities of special nuclear material. This algorithm is also benchmarked against the ANSI N42.34-2006 Standard for Handheld Identifiers as a part of the Department of Homeland Security Algorithm Improvement Program.","abstract_has_math":true,"creators":["Stinnett, Jacob Benjamin"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Nuclear, Plasma, Radiolgc Engr","degree_department":null,"school":null,"contributors":["Sullivan, Clair J.","Stubbins, James F.","Ruzic, David N.","Varshney, Lav R."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-03-01T15:46:18Z","date_published":"2017-03-01T15:46:18Z","updated_at":"2026-07-22T22:26:37Z","subjects":["Gamma-ray spectroscopy","Bayesian classifiers","Isotope identification","Wavelets"],"languages":["en"],"rights":["Copyright 2016 Jacob Benjamin Stinnett"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/95304","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sullivan, Clair J.","Stubbins, James F.","Ruzic, David N.","Varshney, Lav R."]},{"key":"dc:creator","label":"Author","values":["Stinnett, Jacob Benjamin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-03-01T15:46:18Z","2016-11-01","2016-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Nuclear, Plasma, Radiolgc Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Gamma-ray spectroscopy","Bayesian classifiers","Isotope identification","Wavelets"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Jacob Benjamin Stinnett"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/95304"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Handheld radio-isotope identifiers (RIIDs) are widely deployed for nuclear security applications, but these detectors generally have poor isotope identification performance. Most of these deployed detectors use low-resolution NaI scintillators due to their low cost and good efficiency. Alternative detection hardware could be used to improve performance, but better detectors are generally cost-prohibitive for wide deployment in this mission space. However, a trained spectroscopist can use these low-resolution detectors to make much more accurate identifications than the RIIDs produce. For this reason, it has been suggested that these RIIDs could be significantly improved by changing the onboard identification algorithms. To this end, a peak-based Bayesian classifier has been developed to perform automated isotope identification. This algorithm was constructed to mimic the manual identification that a spectroscopist would perform. This approach can handle challenges such as detector calibration drift and unknown shielding scenarios, is capable of identifying mixed radiation sources, and is computationally inexpensive enough to be feasible for deployment on handheld RIID systems. A method for easily generating isotope libraries that are coupled to the detector and to the feature extraction algorithm is presented as well. This method is demonstrated on a broad variety of gamma-ray spectra, ranging from small calibration sources ($<$ 1 $\\mu$Ci) to Category I quantities of special nuclear material. This algorithm is also benchmarked against the ANSI N42.34-2006 Standard for Handheld Identifiers as a part of the Department of Homeland Security Algorithm Improvement Program.","Submission original under an indefinite embargo labeled 'Open Access'. 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Alternative detection hardware could be used to improve performance, but better detectors are generally cost-prohibitive for wide deployment in this mission space. However, a trained spectroscopist can use these low-resolution detectors to make much more accurate identifications than the RIIDs produce. For this reason, it has been suggested that these RIIDs could be significantly improved by changing the onboard identification algorithms. To this end, a peak-based Bayesian classifier has been developed to perform automated isotope identification. This algorithm was constructed to mimic the manual identification that a spectroscopist would perform. This approach can handle challenges such as detector calibration drift and unknown shielding scenarios, is capable of identifying mixed radiation sources, and is computationally inexpensive enough to be feasible for deployment on handheld RIID systems. A method for easily generating isotope libraries that are coupled to the detector and to the feature extraction algorithm is presented as well. This method is demonstrated on a broad variety of gamma-ray spectra, ranging from small calibration sources ($<$ 1 $\\mu$Ci) to Category I quantities of special nuclear material. This algorithm is also benchmarked against the ANSI N42.34-2006 Standard for Handheld Identifiers as a part of the Department of Homeland Security Algorithm Improvement Program.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-02-28 without embargo terms","The student, Jacob Stinnett, accepted the attached license on 2016-10-31 at 16:15.","The student, Jacob Stinnett, submitted this Dissertation for approval on 2016-10-31 at 16:21.","This Dissertation was approved for publication on 2016-11-01 at 15:03.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10212 on 2017-02-28 at 14:46:44","Made available in DSpace on 2017-03-01T15:46:18Z (GMT). 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