{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115945"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115945","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Data-driven techniques in signal restoration and detection problems","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-08-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-08-01","abstract_has_math":false,"creators":["Lim, Teck-Yian"],"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":["Do, Minh N","Schwing, Alexander G","Forsyth, David A","Gupta, Saurabh","Wang, Yuxiong"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-08","date_published":"2022-08","updated_at":"2026-07-22T22:24:55Z","subjects":["FMCW radar","image restoration","sensor fusion"],"languages":["en","eng"],"rights":["Copyright 2022 Teck-Yian Lim"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/115945","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Do, Minh N","Schwing, Alexander G","Forsyth, David A","Gupta, Saurabh","Wang, Yuxiong"]},{"key":"dc:creator","label":"Author","values":["Lim, Teck-Yian"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-08","2022-07-15"]},{"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":["FMCW radar","image restoration","sensor fusion"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Teck-Yian Lim"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/115945"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-08-01","The student, Teck-Yian Lim, accepted the attached license on 2022-07-15 at 12:05.","The student, Teck-Yian Lim, submitted this Dissertation for approval on 2022-07-15 at 12:13.","This Dissertation was approved for publication on 2022-07-15 at 13:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18333 on 2022-11-16 at 10:56:25","With the abundance of data and affordable computational power, data-driven approaches have exploded in the last few years, solving various problems in the field of computer vision with performance exceeding human performance. In this work, we study how learned priors can be applied to the task of image restoration, without having to explicitly train for such a task. We also study how traditional signal processing chains for radars can be augmented with modern data-driven techniques. Our experiments showed that there is sufficient information in a radar heatmap to reliably identify the class of the reflecting object. Using an automatically labeled dataset, we were able to achieve a classification accuracy of over 85% in the indoor scenario and over 98% in the outdoor scenario. Our evaluation, performed on real world data, suggests that the complementary nature of radar and camera signals can be leveraged to reduce the lateral error by 15% when applied to object detection Finally, we also propose a novel method for combining multiple sensor observations in a learned feature space, demonstrating robustness to sensor failure."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Data-driven techniques in signal restoration and detection problems"]}]}],"canonical_facts":{"dc:contributor":["Do, Minh N","Schwing, Alexander G","Forsyth, David A","Gupta, Saurabh","Wang, Yuxiong"],"dc:creator":["Lim, Teck-Yian"],"dc:date":["2022-08","2022-07-15"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-08-01","The student, Teck-Yian Lim, accepted the attached license on 2022-07-15 at 12:05.","The student, Teck-Yian Lim, submitted this Dissertation for approval on 2022-07-15 at 12:13.","This Dissertation was approved for publication on 2022-07-15 at 13:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18333 on 2022-11-16 at 10:56:25","With the abundance of data and affordable computational power, data-driven approaches have exploded in the last few years, solving various problems in the field of computer vision with performance exceeding human performance. In this work, we study how learned priors can be applied to the task of image restoration, without having to explicitly train for such a task. We also study how traditional signal processing chains for radars can be augmented with modern data-driven techniques. Our experiments showed that there is sufficient information in a radar heatmap to reliably identify the class of the reflecting object. Using an automatically labeled dataset, we were able to achieve a classification accuracy of over 85% in the indoor scenario and over 98% in the outdoor scenario. Our evaluation, performed on real world data, suggests that the complementary nature of radar and camera signals can be leveraged to reduce the lateral error by 15% when applied to object detection Finally, we also propose a novel method for combining multiple sensor observations in a learned feature space, demonstrating robustness to sensor failure."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115945"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Teck-Yian Lim"],"dc:subject":["FMCW radar","image restoration","sensor fusion"],"dc:title":["Data-driven techniques in signal restoration and detection problems"],"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":"2026-07-22T22:24:55Z"}