{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/90850"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/90850","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Mobile radiation sensor networks for source detection in a fluctuating background using geo-tagged count rate data","abstract":"Mobile radiation sensor networks integrated with geographic information system provide an attractive option for the real-time anomalous radiation source detection. In order to obtain an accurate alarm of the presence of an anomalous radiation source, continuous measurements of the temporal and positional background radiation distribution are needed. The fluctuations of background radiation can be caused by several reasons, such as the variation in soil composition, building materials, and weather patterns. In this thesis, a radiation sensor network is deployed, and a maximum likelihood estimation-based algorithm is developed to evaluate measurements from the sensor network and estimate the experimental area's radiation distribution and fluctuation. Using the reconstructed background radiation distribution and fluctuation, the probability that each individual measurement includes an anomalous source is calculated. This thesis presents the work of using statistical inference to adjudicate gamma-ray count-rate data from a sensor network based on measurements of sources in an urban environment. Results show that the maximum likelihood estimation-based algorithm enhances the sensor network's anomaly detection accuracy over traditional approaches where the background radiation is measured only periodically and considered static in geoposition across large geographic regions.","abstract_html":"Mobile radiation sensor networks integrated with geographic information system provide an attractive option for the real-time anomalous radiation source detection. In order to obtain an accurate alarm of the presence of an anomalous radiation source, continuous measurements of the temporal and positional background radiation distribution are needed. The fluctuations of background radiation can be caused by several reasons, such as the variation in soil composition, building materials, and weather patterns. In this thesis, a radiation sensor network is deployed, and a maximum likelihood estimation-based algorithm is developed to evaluate measurements from the sensor network and estimate the experimental area's radiation distribution and fluctuation. Using the reconstructed background radiation distribution and fluctuation, the probability that each individual measurement includes an anomalous source is calculated. This thesis presents the work of using statistical inference to adjudicate gamma-ray count-rate data from a sensor network based on measurements of sources in an urban environment. Results show that the maximum likelihood estimation-based algorithm enhances the sensor network's anomaly detection accuracy over traditional approaches where the background radiation is measured only periodically and considered static in geoposition across large geographic regions.","abstract_has_math":false,"creators":["Liu, Zheng"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Nuclear, Plasma, Radiolgc Engr","degree_department":null,"school":null,"contributors":["Sullivan, Clair Julia"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-07-07T20:35:29Z","date_published":"2016-07-07T20:35:29Z","updated_at":"2026-07-22T22:26:34Z","subjects":["mobile sensor network","radiation detection","maximum likelihood estimation"],"languages":["en"],"rights":["Copyright 2016 Zheng Liu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/90850","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sullivan, Clair Julia"]},{"key":"dc:creator","label":"Author","values":["Liu, Zheng"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-07-07T20:35:29Z","2018-07-08T09:15:23Z","2016-04-29","2016-05"]},{"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":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["mobile sensor network","radiation detection","maximum likelihood estimation"]}]},{"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 Zheng Liu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/90850"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Mobile radiation sensor networks integrated with geographic information system provide an attractive option for the real-time anomalous radiation source detection. In order to obtain an accurate alarm of the presence of an anomalous radiation source, continuous measurements of the temporal and positional background radiation distribution are needed. The fluctuations of background radiation can be caused by several reasons, such as the variation in soil composition, building materials, and weather patterns. In this thesis, a radiation sensor network is deployed, and a maximum likelihood estimation-based algorithm is developed to evaluate measurements from the sensor network and estimate the experimental area's radiation distribution and fluctuation. Using the reconstructed background radiation distribution and fluctuation, the probability that each individual measurement includes an anomalous source is calculated. This thesis presents the work of using statistical inference to adjudicate gamma-ray count-rate data from a sensor network based on measurements of sources in an urban environment. Results show that the maximum likelihood estimation-based algorithm enhances the sensor network's anomaly detection accuracy over traditional approaches where the background radiation is measured only periodically and considered static in geoposition across large geographic regions.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, Zheng Liu, accepted the attached license on 2016-04-28 at 12:41.","The student, Zheng Liu, submitted this Thesis for approval on 2016-04-28 at 13:43.","This Thesis was approved for publication on 2016-04-29 at 11:26.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9569 on 2016-07-07 at 13:51:09","Made available in DSpace on 2016-07-07T20:35:29Z (GMT). No. of bitstreams: 2 LIU-THESIS-2016.pdf: 35723856 bytes, checksum: 9c0d4eae82107f23db607e607167bec6 (MD5) LICENSE.txt: 4206 bytes, checksum: a8e5395b16c84bd58133f1cb79802dba (MD5) Previous issue date: 2016-04-29","Embargo set by: Seth Robbins for item 93203 Lift date: 2018-07-07T20:35:34Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 93203 on 2018-07-08T09:15:23Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Mobile radiation sensor networks for source detection in a fluctuating background using geo-tagged count rate data"]}]}],"canonical_facts":{"dc:contributor":["Sullivan, Clair Julia"],"dc:creator":["Liu, Zheng"],"dc:date":["2016-07-07T20:35:29Z","2018-07-08T09:15:23Z","2016-04-29","2016-05"],"dc:description":["Mobile radiation sensor networks integrated with geographic information system provide an attractive option for the real-time anomalous radiation source detection. In order to obtain an accurate alarm of the presence of an anomalous radiation source, continuous measurements of the temporal and positional background radiation distribution are needed. The fluctuations of background radiation can be caused by several reasons, such as the variation in soil composition, building materials, and weather patterns. In this thesis, a radiation sensor network is deployed, and a maximum likelihood estimation-based algorithm is developed to evaluate measurements from the sensor network and estimate the experimental area's radiation distribution and fluctuation. Using the reconstructed background radiation distribution and fluctuation, the probability that each individual measurement includes an anomalous source is calculated. This thesis presents the work of using statistical inference to adjudicate gamma-ray count-rate data from a sensor network based on measurements of sources in an urban environment. Results show that the maximum likelihood estimation-based algorithm enhances the sensor network's anomaly detection accuracy over traditional approaches where the background radiation is measured only periodically and considered static in geoposition across large geographic regions.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, Zheng Liu, accepted the attached license on 2016-04-28 at 12:41.","The student, Zheng Liu, submitted this Thesis for approval on 2016-04-28 at 13:43.","This Thesis was approved for publication on 2016-04-29 at 11:26.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9569 on 2016-07-07 at 13:51:09","Made available in DSpace on 2016-07-07T20:35:29Z (GMT). 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