[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118490-en":3,"doc-seo-118490-105":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},118490,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning for Space Signal Processing - Thesis","Doctoral thesis on machine learning methods for space signal processing, developed through several research contributions spanning graph clustering, deep learning-based phase retrieval, and compressive sensing for space optical communications. The work formulates non-convex clustering problems, proposes algorithms that improve computational efficiency, and studies clustering under partial, missing, and comparison information. It further presents unfolded and quantum-oriented methodologies, including quantum compressive sensing, quantum noise exploration, and practical experimental workflows.","Machine Learning for Space Signal Processing  \nBY  \nNAVEED NAIMIPOUR  \nB.Sc., University of Illinois at Chicago, 2015  \nM.Sc., University of Illinois at Chicago, 2024  \nTHESIS  \nSubmitted as partial fulfillment of the requirements  \nfor the degree of Doctor of Philosophy in Electrical and Computer Engineering  \nin the Graduate College of the  \nUniversity of Illinois at Chicago, 2025  \nChicago, Illinois  \nDefense Committee:  \nMojtaba Soltanalian, Chair and Advisor  \nBesma Smida  \nRashid Ansari  \nHarry Shaw, NASA Goddard Space Flight Center  \nHaleh Safavi, NASA Goddard Space Flight Center  \nCopyright by Naveed Naimipour  \n2025  \nDedicated to my father, the one person who wanted this more for me than I did.  \nACKNOWLEDGMENTS  \nThank you to my advisor, Professor Mojtaba Soltanalian for all his inputs, understanding, and support throughout my PhD journey. His encouragement, guidance, and assistance have been invaluable in my graduate journey.  \nAdditional thanks to my mentors at NASA, Dr. Harry Shaw and Dr. Haleh Safavi. I could write a book with the amount of things I’ve learned under their mentorship and I will be eternally grateful for the way they have jump started my career.  \nFurther thanks to my committee members not mentioned above, Professors Rashid Ansari and Besma Smida, for taking the time to listen to my research.  \nTo my lab members and colleagues at UIC and NASA, thank you for the work we did together and discussions we had. The list is too long to mention everyone, but I truly appreciate the support and help from all of them along the way. Not only did they help advance projects and research, but they also made life a bit more interesting.  \nA thanks to the funding agencies that supported my work: U.S. National Science Foundation (NSF), who supported parts of this work under National Science Foundation Grant CCF-1704401, and NASA Goddard Space Flight Center Advanced Communications Capabilities for Exploration and Science Systems (ACCESS) Project Office.  \nFinally, thank you to my family, who have supported me through the ups and downs of my PhD journey. NN  \nCONTRIBUTION OF AUTHORS  \nIn the work “Efficient Non-Convex Graph Clustering for Big Data,” Naveed Naimipour and Mojtaba Soltanalian developed the concept, methodology, and algorithms. Naveed Naimipour carried out the simulations for the results. Naveed Naimipour wrote the manuscript with help from Mojtaba Soltanalian.  \nIn the work “Graph Clustering Using One-Bit Comparison Data,” Naveed Naimipour developed the concept, methodology, algorithms, and carried out the simulations for the results. Naveed Naimipour wrote the manuscript with help from Mojtaba Soltanalian.  \nIn the work “Machine Learning Algorithms for Error Correction in Space Optical Communications Systems,” Naveed Naimipour developed the concept, methodology, framework, and carried out the simulations for the results. Naveed Naimipour wrote the manuscript with help from Harry Shaw, Haleh Safavi, and Mojtaba Soltanalian.  \nIn the work “Unfolded Algorithms for Deep Phase Retrieval,” Naveed Naimipour developed the concept and Shahin Khobahi developed the general approach. Specific frameworks were developed by Naveed Naimipour and Shahin Khobahi. Naveed Naimipour and Shahin Khobahi carried out the simulations for the results. Additional simulations were carried out by Naveed Naimipour. Naveed Naimipour and Shahin Khobahi wrote the manuscript with help from Mojtaba Soltanalian, Harry Shaw, and Haleh Safavi.  \nIn the work “Quantum Compressive Sensing: Mathematical Machinery, Quantum Algorithms, and Quantum Circuitry,” Naveed Naimipour and Kyle Sherbert developed the  \nCONTRIBUTION OF AUTHORS (Continued)  \nconcept and general methodology. Naveed Naimipour developed some of the classical concepts. Kyle Sherbert developed the protocols and carried out the simulations with inputs from Naveed Naimipour. Kyle Sherbert and Naveed Naimipour wrote the manuscript with help from Harry Shaw, Haleh Safavi, and Mojtaba","cbCaigH1omnnKmpk","https://ap.wps.com/l/cbCaigH1omnnKmpk","pdf",6152987,1,225,"English","en",105,"# Chapter 1 Introduction\n## 1.1 Graph Clustering aided Signal Processing\n## 1.2 Deep Learning for Phase Retrieval\n## 1.3 Quantum Methodologies for Compressive Sensing\n# Chapter 2 Practical Non-Convex Graph Clustering with Space Applications\n## 2.2 Problem Formulation\n## 2.3 Proposed Algorithms\n## 2.4 Clustering with Partial Information\n## 2.5 Numerical Results\n## 2.6 Practical Applications for Error Correction in Space Optical Communications Systems","[{\"question\":\"What are the main research themes of the thesis?\",\"answer\":\"The thesis focuses on machine learning for space signal processing through graph clustering, deep learning for phase retrieval, and compressive sensing methods including quantum-oriented approaches.\"},{\"question\":\"How does the thesis handle clustering when information is incomplete?\",\"answer\":\"It develops clustering methods that work with partial information, including scenarios with missing data and with comparison information, supported by numerical results and discussion.\"},{\"question\":\"What is the thesis contribution for error correction in space optical communications?\",\"answer\":\"It proposes a practical system and studies results for error correction in space optical communications, linking non-convex clustering methods to communications system performance.\"}]","Machine Learning for Space Signal Processing - 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