{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/127337"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/127337","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Enhancing wireless signal perception through combined processing and learning methods","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-12-01","abstract_has_math":false,"creators":["Madani, Sohrab"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Al-Hassanieh, Haitham","Patel, Sanjay J","Gupta, Saurabh","Mitra, Sayan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-12-05","date_published":"2024-12-05","updated_at":"2026-07-22T22:25:03Z","subjects":["Wireless Sensing","Deep Learning","Signal Processing"],"languages":["eng","en"],"rights":["Copyright 2024 Sohrab Madani"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/127337","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Al-Hassanieh, Haitham","Patel, Sanjay J","Gupta, Saurabh","Mitra, Sayan"]},{"key":"dc:creator","label":"Author","values":["Madani, Sohrab"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-12-05","2024-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer 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":["Wireless Sensing","Deep Learning","Signal Processing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng","en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Sohrab Madani"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/127337"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-12-01","The student, Sohrab Madani, accepted the attached license on 2024-12-02 at 20:32.","The student, Sohrab Madani, submitted this Dissertation for approval on 2024-12-02 at 20:43.","This Dissertation was approved for publication on 2024-12-05 at 16:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21272 on 2025-03-28 at 14:49:51","In recent years, wireless signals have transcended their traditional role in communication systems, emerging as a powerful medium for sensing and perception. These electromagnetic waves have demonstrated remarkable versatility, enabling applications ranging from coarse-grained autonomous vehicle perception to finegrained physiological monitoring of human vital signs. While the underlying sensing mechanisms vary across applications, they fundamentally leverage the same physical properties of radio frequency propagation, creating opportunities for unified theoretical frameworks and methodological approaches. This dissertation introduces novel hybrid methodologies that bridge classical signal processing techniques with modern deep learning approaches. We build upon established wireless sensing foundations while introducing innovative processing pipelines that combine domain-specific signal transformations with adaptive neural architectures. Our framework extends beyond application-specific solutions, presenting a generalizable approach to wireless signal processing that maintains theoretical rigor while embracing the flexibility of data-driven methods. Through extensive experimental validation, we demonstrate the efficacy of our proposed methods across multiple perception tasks. Specifically, we address critical challenges in autonomous vehicle sensing and indoor object localization and tracking. Our approach leverages domain knowledge of wireless propagation characteristics to inform the design of specialized learning architectures, resulting in significant performance improvements over traditional methods. The methodologies developed in this work not only advance the state-of-the-art in wireless sensing but also establish new paradigms for integrating physical understanding with learning-based approaches in signal processing systems."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Enhancing wireless signal perception through combined processing and learning methods"]}]}],"canonical_facts":{"dc:contributor":["Al-Hassanieh, Haitham","Patel, Sanjay J","Gupta, Saurabh","Mitra, Sayan"],"dc:creator":["Madani, Sohrab"],"dc:date":["2024-12-05","2024-12"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-12-01","The student, Sohrab Madani, accepted the attached license on 2024-12-02 at 20:32.","The student, Sohrab Madani, submitted this Dissertation for approval on 2024-12-02 at 20:43.","This Dissertation was approved for publication on 2024-12-05 at 16:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21272 on 2025-03-28 at 14:49:51","In recent years, wireless signals have transcended their traditional role in communication systems, emerging as a powerful medium for sensing and perception. These electromagnetic waves have demonstrated remarkable versatility, enabling applications ranging from coarse-grained autonomous vehicle perception to finegrained physiological monitoring of human vital signs. While the underlying sensing mechanisms vary across applications, they fundamentally leverage the same physical properties of radio frequency propagation, creating opportunities for unified theoretical frameworks and methodological approaches. This dissertation introduces novel hybrid methodologies that bridge classical signal processing techniques with modern deep learning approaches. We build upon established wireless sensing foundations while introducing innovative processing pipelines that combine domain-specific signal transformations with adaptive neural architectures. Our framework extends beyond application-specific solutions, presenting a generalizable approach to wireless signal processing that maintains theoretical rigor while embracing the flexibility of data-driven methods. Through extensive experimental validation, we demonstrate the efficacy of our proposed methods across multiple perception tasks. Specifically, we address critical challenges in autonomous vehicle sensing and indoor object localization and tracking. Our approach leverages domain knowledge of wireless propagation characteristics to inform the design of specialized learning architectures, resulting in significant performance improvements over traditional methods. The methodologies developed in this work not only advance the state-of-the-art in wireless sensing but also establish new paradigms for integrating physical understanding with learning-based approaches in signal processing systems."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/127337"],"dc:language":["eng","en"],"dc:rights":["Copyright 2024 Sohrab Madani"],"dc:subject":["Wireless Sensing","Deep Learning","Signal Processing"],"dc:title":["Enhancing wireless signal perception through combined processing and learning methods"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:03Z"}