[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125144-en":3,"doc-seo-125144-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125144,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Improving Calibration and Multi-frequency Inversion in Microwave Imaging with Machine Learning","This thesis investigates two machine-learning methods to enhance microwave imaging. One contribution enables calibration when an imaging system is uncooperative and lacks known targets, leveraging Cycle Generative Adversarial Networks to learn mappings between synthetic field data and raw S-parameters. The second contribution studies how to use multi-frequency measurements for inverse problem solving, proposing cascaded sequential UNets and a novel LSTM-like architecture. Both are evaluated on a 2D TM experimental setup and outperform single-frequency and naive multi-frequency strategies.","Improving Calibration and Multi-frequency Inversion in Microwave Imaging with Machine  \nLearning  \nby  \nBen Martin  \nA thesis submitted to the Faculty of Graduate Studies of The University of Manitoba  \nin partial fulfilment of the requirements of the degree of  \nMaster of Science  \nUniversity of Manitoba  \nWinnipeg, Manitoba, Canada  \nSeptember 2024  \nCopyright © Ben Martin, 2024  \nAbstract  \nThis thesis explores two unique ways of using machine learning to improve or facilitate microwave imaging. The first contribution provides a means of calibration under the assumption of an uncooperative imaging system, a term indicating that it is impossible or impractical to have any control over the region of interest, which means no known targets are available for calibration. The approach uses a method of style transfer provided by Cycle Generative Adversarial Networks. Cycle Generative Adversarial Networks are capable of learning arbitrary transformations between two sets of data in which there exists no paired samples across the two domains. In the case of calibration the two domains are synthetic field data and raw S-parameters from a physical imaging setup. The method is shown to be nearly as good as calibration with a known target for a 2D TM experimental imaging setup.  \nThe second contribution of this thesis focuses on how multi-frequency data should best be used in a machine learning model to solve the inverse problem. This work introduces two novel architectures capable of using multi-frequency data and testing the results on experimental data. The first method uses a system of sequential UNets referred to as the cascaded multi-frequency approach. This method turned out to be very similar to a recurrent neural network which inspired the creation of a novel LSTM-like architecture - the second method. These two methods were compared  \nto single frequency inversions and a ‘naive’multi-frequency inversion which collapse the frequency data into the channels of the single frequency inversion. The models were tested on the same 2D TM experimental imaging setup in which labeled data was acquired through an automated target positioning system. Both multi-frequency approaches show significant improvement over the single frequency counter part and the naive approach.  \nContributions  \nThe work in this thesis has led to the following contributions, two of which are included in this thesis.  \nCopyright Notice  \nIn reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of U of M’s products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to [http://www.ieee.org/publications](http://www.ieee.org/publications) standards/publications/rights/rights   link.html to learn how to obtain a License from RightsLink.  \nJournal Papers  \n1. © 2023 IEEE. Reprinted, with permission, from Martin, B. , Edwards, K. , Jeffrey, I., & Gilmore, C., Experimental microwave imaging system calibration via Cycle-GAN, IEEE TAP, September 2023-Featured Article  \nPersonal Contributions: Data collection, code development, validation, manuscript.  \n2. © 2024 IEEE. Reprinted, with permission, from Martin, B. , Jeffrey, I., &  \nGilmore, C. , A Long Short-Term Memory Approach to Incorporating MultiFrequency Data into Deep-Learning-Based Microwave Imaging, IEEE TAP, September 2024  \nPersonal Contributions: Algorithm creation, code development, validation, manuscript.  \n3. Narendra, K. , Martin, B. , Gilmore, C., & Jeffrey, I. (2023) . AutoencoderAugmented Machine-Learning-Based Uncertainty Quantification for Electromagnetic Imaging. IEEE Transactions on Antennas and Propagation.  \n4. Cathers, S. , Martin, B. , Steiler, N., Jeffrey, I., & Gilmore, C. (2024) . Improved Machine Learning-based Microwave Inversion with E","cbCaik6AJLnytYhc","https://ap.wps.com/l/cbCaik6AJLnytYhc","pdf",8066961,1,109,"English","en",105,"# Abstract\n# Contributions\n# Journal Papers\n## Personal Contributions\n# Conference Papers\n## Personal Contributions\n# Acknowledgments","[{\"question\":\"How does the thesis address microwave imaging calibration without known targets?\",\"answer\":\"It proposes a calibration method for an uncooperative imaging system by learning a transformation between synthetic field data and raw S-parameters using Cycle Generative Adversarial Networks (Cycle-GAN).\"},{\"question\":\"What are the two multi-frequency inverse-problem architectures introduced in the thesis?\",\"answer\":\"The thesis introduces a cascaded multi-frequency approach using sequential UNets and a second method based on a novel LSTM-like architecture inspired by similarity to a recurrent neural network.\"},{\"question\":\"How do the multi-frequency methods compare with single-frequency and naive multi-frequency inversion?\",\"answer\":\"Evaluations on the same 2D TM experimental imaging setup show that both multi-frequency approaches significantly improve over single-frequency inversion and over a naive method that collapses frequency data into single-frequency channels.\"}]","Improving Calibration and Multi-frequency Inversion in Microwave Imaging with Machine Learning | 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does the thesis address microwave imaging calibration without known targets?","Question",{"text":75,"@type":76},"It proposes a calibration method for an uncooperative imaging system by learning a transformation between synthetic field data and raw S-parameters using Cycle Generative Adversarial Networks (Cycle-GAN).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What are the two multi-frequency inverse-problem architectures introduced in the thesis?",{"text":80,"@type":76},"The thesis introduces a cascaded multi-frequency approach using sequential UNets and a second method based on a novel LSTM-like architecture inspired by similarity to a recurrent neural network.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the multi-frequency methods compare with single-frequency and naive multi-frequency inversion?",{"text":84,"@type":76},"Evaluations on the same 2D TM experimental imaging setup show that both multi-frequency approaches significantly improve over single-frequency 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