{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108091"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108091","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Low-complexity convolutional neural networks for automatic target recognition","abstract":"Over the decades, several algorithms have been proposed for designing automatic target recognition systems based on synthetic aperture radar imagery. Recently, with the rise of Deep Learning, there has been growing interest in developing neural network based automatic target recognition systems for synthetic aperture radar applications. However, these networks are typically complex in terms of storage and computation which inhibits their deployment in the field, where such resources are heavily constrained. In order to reduce the cost of implementing these networks, in this thesis we develop a set of compact network architectures and train them in fixed-point. Our proposed method achieves an overall 984× reduction in terms of storage requirements and 71× reduction in terms of computational complexity compared to state-of-the-art convolutional neural networks for automatic target recognition, while maintaining a classification accuracy of >99% on the MSTAR dataset.","abstract_html":"Over the decades, several algorithms have been proposed for designing automatic target recognition systems based on synthetic aperture radar imagery. Recently, with the rise of Deep Learning, there has been growing interest in developing neural network based automatic target recognition systems for synthetic aperture radar applications. However, these networks are typically complex in terms of storage and computation which inhibits their deployment in the field, where such resources are heavily constrained. In order to reduce the cost of implementing these networks, in this thesis we develop a set of compact network architectures and train them in fixed-point. Our proposed method achieves an overall 984× reduction in terms of storage requirements and 71× reduction in terms of computational complexity compared to state-of-the-art convolutional neural networks for automatic target recognition, while maintaining a classification accuracy of >99% on the MSTAR dataset.","abstract_has_math":false,"creators":["Dbouk, Hassan"],"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":["Shanbhag, Naresh R"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-26T23:51:24Z","date_published":"2020-08-26T23:51:24Z","updated_at":"2026-07-22T22:24:47Z","subjects":["deep learning","neural networks","automatic target recognition","synthetic aperture radar","quantization"],"languages":["en"],"rights":["Copyright 2020 Hassan Dbouk"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108091","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Shanbhag, Naresh R"]},{"key":"dc:creator","label":"Author","values":["Dbouk, Hassan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-26T23:51:24Z","2022-08-26T23:58:55Z","2020-03-11","2020-05"]},{"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":["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":["deep learning","neural networks","automatic target recognition","synthetic aperture radar","quantization"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Hassan Dbouk"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108091"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Over the decades, several algorithms have been proposed for designing automatic target recognition systems based on synthetic aperture radar imagery. Recently, with the rise of Deep Learning, there has been growing interest in developing neural network based automatic target recognition systems for synthetic aperture radar applications. However, these networks are typically complex in terms of storage and computation which inhibits their deployment in the field, where such resources are heavily constrained. In order to reduce the cost of implementing these networks, in this thesis we develop a set of compact network architectures and train them in fixed-point. Our proposed method achieves an overall 984× reduction in terms of storage requirements and 71× reduction in terms of computational complexity compared to state-of-the-art convolutional neural networks for automatic target recognition, while maintaining a classification accuracy of >99% on the MSTAR dataset.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-05-01","The student, Hassan Dbouk, accepted the attached license on 2020-03-10 at 11:07.","The student, Hassan Dbouk, submitted this Thesis for approval on 2020-03-10 at 11:18.","This Thesis was approved for publication on 2020-03-11 at 11:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14893 on 2020-08-25 at 17:26:59","Made available in DSpace on 2020-08-26T23:51:24Z (GMT). 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Recently, with the rise of Deep Learning, there has been growing interest in developing neural network based automatic target recognition systems for synthetic aperture radar applications. However, these networks are typically complex in terms of storage and computation which inhibits their deployment in the field, where such resources are heavily constrained. In order to reduce the cost of implementing these networks, in this thesis we develop a set of compact network architectures and train them in fixed-point. Our proposed method achieves an overall 984× reduction in terms of storage requirements and 71× reduction in terms of computational complexity compared to state-of-the-art convolutional neural networks for automatic target recognition, while maintaining a classification accuracy of >99% on the MSTAR dataset.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-05-01","The student, Hassan Dbouk, accepted the attached license on 2020-03-10 at 11:07.","The student, Hassan Dbouk, submitted this Thesis for approval on 2020-03-10 at 11:18.","This Thesis was approved for publication on 2020-03-11 at 11:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14893 on 2020-08-25 at 17:26:59","Made available in DSpace on 2020-08-26T23:51:24Z (GMT). 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