{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/45422"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/45422","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Energy-efficient detection system in time-varying signal and noise power","abstract":"In many detection applications with battery-powered or energy-harvesting sensors, energy constraints preclude the use of the optimal detector all the time. Optimal energy-performance trade-off is therefore needed in such situations. In many signal processing applications, the signal and noise power may vary greatly over time, which can be exploited to constrain energy consumption while maintaining the best possible performance. A detector scheduling algorithm based on the signal and noise power information is developed in this thesis. The resulting algorithm is simple due to its threshold-test structure and can be easily implemented with almost no overhead. A detection system with two detectors using the proposed scheduling scheme is estimated to greatly reduce the energy consumption for a wildlife monitoring application. Hardware implementation also consolidates the empirical evidence for the effectiveness of the proposed method.","abstract_html":"In many detection applications with battery-powered or energy-harvesting sensors, energy constraints preclude the use of the optimal detector all the time. Optimal energy-performance trade-off is therefore needed in such situations. In many signal processing applications, the signal and noise power may vary greatly over time, which can be exploited to constrain energy consumption while maintaining the best possible performance. A detector scheduling algorithm based on the signal and noise power information is developed in this thesis. The resulting algorithm is simple due to its threshold-test structure and can be easily implemented with almost no overhead. A detection system with two detectors using the proposed scheduling scheme is estimated to greatly reduce the energy consumption for a wildlife monitoring application. Hardware implementation also consolidates the empirical evidence for the effectiveness of the proposed method.","abstract_has_math":false,"creators":["Le, Long"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Jones, Douglas L."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-08-22T16:39:42Z","date_published":"2013-08-22T16:39:42Z","updated_at":"2026-07-22T22:25:34Z","subjects":["Energy-efficient detection","Scheduler","Time-varying","Signal-to-Noise Ratio (SNR)","Estimation","Adaptive threshold"],"languages":["en"],"rights":["Copyright 2013 Long Le"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/45422","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jones, Douglas L."]},{"key":"dc:creator","label":"Author","values":["Le, Long"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2013-08-22T16:39:42Z","2013-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer 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":["Energy-efficient detection","Scheduler","Time-varying","Signal-to-Noise Ratio (SNR)","Estimation","Adaptive threshold"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2013 Long Le"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/45422"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In many detection applications with battery-powered or energy-harvesting sensors, energy constraints preclude the use of the optimal detector all the time. Optimal energy-performance trade-off is therefore needed in such situations. In many signal processing applications, the signal and noise power may vary greatly over time, which can be exploited to constrain energy consumption while maintaining the best possible performance. A detector scheduling algorithm based on the signal and noise power information is developed in this thesis. The resulting algorithm is simple due to its threshold-test structure and can be easily implemented with almost no overhead. A detection system with two detectors using the proposed scheduling scheme is estimated to greatly reduce the energy consumption for a wildlife monitoring application. Hardware implementation also consolidates the empirical evidence for the effectiveness of the proposed method.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-06-10T13:56:06Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Le_Long.pdf: 1575237 bytes, checksum: 6fc9da2f9949d3f1ceb286c3d608364d (MD5)","Made available in DSpace on 2013-08-22T16:39:42Z (GMT). No. of bitstreams: 2 Long_Le.pdf: 1575237 bytes, checksum: 6fc9da2f9949d3f1ceb286c3d608364d (MD5) license.txt: 4056 bytes, checksum: 108b12b5f0b1a917d593c29cf1409ea6 (MD5)"]},{"key":"dc:title","label":"Title","values":["Energy-efficient detection system in time-varying signal and noise power"]}]}],"canonical_facts":{"dc:contributor":["Jones, Douglas L."],"dc:creator":["Le, Long"],"dc:date":["2013-08-22T16:39:42Z","2013-08"],"dc:description":["In many detection applications with battery-powered or energy-harvesting sensors, energy constraints preclude the use of the optimal detector all the time. Optimal energy-performance trade-off is therefore needed in such situations. In many signal processing applications, the signal and noise power may vary greatly over time, which can be exploited to constrain energy consumption while maintaining the best possible performance. A detector scheduling algorithm based on the signal and noise power information is developed in this thesis. The resulting algorithm is simple due to its threshold-test structure and can be easily implemented with almost no overhead. A detection system with two detectors using the proposed scheduling scheme is estimated to greatly reduce the energy consumption for a wildlife monitoring application. Hardware implementation also consolidates the empirical evidence for the effectiveness of the proposed method.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-06-10T13:56:06Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Le_Long.pdf: 1575237 bytes, checksum: 6fc9da2f9949d3f1ceb286c3d608364d (MD5)","Made available in DSpace on 2013-08-22T16:39:42Z (GMT). No. of bitstreams: 2 Long_Le.pdf: 1575237 bytes, checksum: 6fc9da2f9949d3f1ceb286c3d608364d (MD5) license.txt: 4056 bytes, checksum: 108b12b5f0b1a917d593c29cf1409ea6 (MD5)"],"dc:identifier":["http://hdl.handle.net/2142/45422"],"dc:language":["en"],"dc:rights":["Copyright 2013 Long Le"],"dc:subject":["Energy-efficient detection","Scheduler","Time-varying","Signal-to-Noise Ratio (SNR)","Estimation","Adaptive threshold"],"dc:title":["Energy-efficient detection system in time-varying signal and noise power"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:34Z"}