2026-09-20T15:07:51Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/108091 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

deep learning neural networks en Copyright 2020 Hassan Dbouk Shanbhag, Naresh R Dbouk, Hassan 2020-08-26T23:51:24Z 2020-08-26T23:51:24Z 2022-08-26T23:58:55Z 2020-03-11 2020-05 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). No. of bitstreams: 2 DBOUK-THESIS-2020.pdf: 1264282 bytes, checksum: 081816a0d9fac969b3084e01e987d955 (MD5) LICENSE.txt: 4209 bytes, checksum: 555c4643e121e7aa3736b7cfffe3f5f4 (MD5) Previous issue date: 2020-03-11 Embargo set by: Seth Robbins for item 115700 Lift date: 2022-08-26T23:51:32Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system Embargo set by: Seth Robbins for item 115700 Lift date: 2022-08-26T23:54:40Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system Embargo set by: Seth Robbins for item 115700 Lift date: 2022-08-26T23:55:59Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system Embargo set by: Seth Robbins for item 115700 Lift date: 2022-08-26T23:57:28Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system Embargo set by: Seth Robbins for item 115700 Lift date: 2022-08-26T23:58:55Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system U of I Only application/pdf http://hdl.handle.net/2142/108091 automatic target recognition synthetic aperture radar quantization Low-complexity convolutional neural networks for automatic target recognition text Thesis Electrical & Computer Eng Electrical & Computer Engr University of Illinois at Urbana-Champaign Thesis M.S.