{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-1948"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-1948","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"Addressing the challenges facing deep learning based Specific Emitter Identification via preamble based waveforms","abstract":"The purpose of this study is to conduct in-depth experiments that analyze the effects on Deep Learning (DL) based Specific Emitter Identification (SEI) and address three issues facing the field. SEI is targeted as a physical-layer security measure that can identify radios within an Internet of Things (IoT) deployment without the need of digital credentials. In the current space, DL SEI is still in its infancy, and has not had the incubation time for a tailor-suited approach to solve issues facing SEI. This thesis introduces methods of improving DL SEI using transforms to allow the networks to learn features that reduce computational cost and improve security. Overall, this thesis highlights the introduction of (i) the natural logarithm as a computationally inexpensive transform of preamble-based waveforms, (ii) assessment of the impacts signal energy has on DL SEI, and (iii) an approach to improving the multi-day classification performance of IEEE 802.11a OFDM emitters.","abstract_html":"The purpose of this study is to conduct in-depth experiments that analyze the effects on Deep Learning (DL) based Specific Emitter Identification (SEI) and address three issues facing the field. SEI is targeted as a physical-layer security measure that can identify radios within an Internet of Things (IoT) deployment without the need of digital credentials. In the current space, DL SEI is still in its infancy, and has not had the incubation time for a tailor-suited approach to solve issues facing SEI. This thesis introduces methods of improving DL SEI using transforms to allow the networks to learn features that reduce computational cost and improve security. Overall, this thesis highlights the introduction of (i) the natural logarithm as a computationally inexpensive transform of preamble-based waveforms, (ii) assessment of the impacts signal energy has on DL SEI, and (iii) an approach to improving the multi-day classification performance of IEEE 802.11a OFDM emitters.","abstract_has_math":false,"creators":["Tyler, Joshua"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Reising, Donald","Kaplagoglu, Erkan; Kandah, Farah","College of Engineering and Computer Science"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-08-01T07:00:00Z","date_published":"2023-08-01T07:00:00Z","updated_at":"2026-07-24T05:47:06Z","subjects":["Deep learning (Machine learning)","Radio frequency identification systems"],"languages":["English","eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/773","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Reising, Donald","Kaplagoglu, Erkan; Kandah, Farah","College of Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Tyler, Joshua"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12-01T08:00:00Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-08-01T07:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"]},{"key":"dc:relation","label":"Dc Relation","values":["Masters Theses and Doctoral Dissertations"]},{"key":"dc:type","label":"Dc Type","values":["Masters theses","Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Deep learning (Machine learning)","Radio frequency identification systems"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholar.utc.edu/theses/773"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Dept. of Electrical Engineering","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."]},{"key":"dc:description.abstract","label":"Abstract","values":["The purpose of this study is to conduct in-depth experiments that analyze the effects on Deep Learning (DL) based Specific Emitter Identification (SEI) and address three issues facing the field. SEI is targeted as a physical-layer security measure that can identify radios within an Internet of Things (IoT) deployment without the need of digital credentials. In the current space, DL SEI is still in its infancy, and has not had the incubation time for a tailor-suited approach to solve issues facing SEI. This thesis introduces methods of improving DL SEI using transforms to allow the networks to learn features that reduce computational cost and improve security. Overall, this thesis highlights the introduction of (i) the natural logarithm as a computationally inexpensive transform of preamble-based waveforms, (ii) assessment of the impacts signal energy has on DL SEI, and (iii) an approach to improving the multi-day classification performance of IEEE 802.11a OFDM emitters."]},{"key":"dc:title","label":"Title","values":["Addressing the challenges facing deep learning based Specific Emitter Identification via preamble based waveforms"]}]}],"canonical_facts":{"dc:contributor":["Reising, Donald","Kaplagoglu, Erkan; Kandah, Farah","College of Engineering and Computer Science"],"dc:creator":["Tyler, Joshua"],"dc:date":["2022-12-01T08:00:00Z"],"dc:date.available":["2023-08-01T07:00:00Z"],"dc:description":["Dept. of Electrical Engineering","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."],"dc:description.abstract":["The purpose of this study is to conduct in-depth experiments that analyze the effects on Deep Learning (DL) based Specific Emitter Identification (SEI) and address three issues facing the field. SEI is targeted as a physical-layer security measure that can identify radios within an Internet of Things (IoT) deployment without the need of digital credentials. In the current space, DL SEI is still in its infancy, and has not had the incubation time for a tailor-suited approach to solve issues facing SEI. This thesis introduces methods of improving DL SEI using transforms to allow the networks to learn features that reduce computational cost and improve security. Overall, this thesis highlights the introduction of (i) the natural logarithm as a computationally inexpensive transform of preamble-based waveforms, (ii) assessment of the impacts signal energy has on DL SEI, and (iii) an approach to improving the multi-day classification performance of IEEE 802.11a OFDM emitters."],"dc:identifier":["https://scholar.utc.edu/theses/773"],"dc:language":["English","eng"],"dc:publisher":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"],"dc:relation":["Masters Theses and Doctoral Dissertations"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Deep learning (Machine learning)","Radio frequency identification systems"],"dc:title":["Addressing the challenges facing deep learning based Specific Emitter Identification via preamble based waveforms"],"dc:type":["Masters theses","Text"]},"updated_at":"2026-07-24T05:47:06Z"}