[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117196-en":3,"doc-seo-117196-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},117196,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Improving Radar Sensing Capabilities and Data Quality Through Machine Learning","Improving radar sensing performance and data reliability through machine learning is the core objective of this dissertation. It introduces a structured overview of radar sensing and key machine learning concepts, then develops theoretical foundations covering supervised, unsupervised, and classification/regression models and common evaluation metrics. A full methodology is presented for estimating atmospheric humidity using wind profiler radar, including physical modeling, data quality control and preprocessing, dataset description, and strategies to prevent overfitting. The work culminates in results and analysis demonstrating the value of machine learning-driven processing for enhancing data quality.","UNIVERSITY OF OKLAHOMA GRADUATE COLLEGE  \nImproving Radar Sensing Capabilities and  \nData Quality Through Machine Learning  \nA DISSERTATION SUBMITTED TO THE GRADUATE FACULTY in partial fulfillment of the requirements for the  \nDegree of  \nDOCTOR OF PHILOSOPHY  \nBy  \nAnas Amaireh Norman, Oklahoma  \n2024  \nImproving Radar Sensing Capabilities and  \nData Quality Through Machine Learning  \nA DISSERTATION APPROVED FOR THE SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING  \nBY THE COMMITTEE CONSISTING OF  \nDr. Yan Zhang, Chair  \nDr. Cameron Homeyer  \nDr. David Schvartzman  \nDr. Samuel Cheng  \nDr. David Ebert  \n©Copyright by Anas Amaireh 2024 All Rights Reserved.  \nTo my mother, whose boundless love and sacrifices have been the cornerstone of my journey -your unwavering  \nbelief in me has been my greatest strength.  \nTo my father, whose wisdom andguidance have shaped the person I am today -your support has been my  \nguiding star, leading me through every challenge.  \nTo my sisters, Wlla and Farah, and my brother Mohammad, for being my strong support andfor standing by me  \nthrough thick and thin - your love means everything to me.  \nAnd to the quiet inspiration whose influence has fueled passion and illuminated my path with warmth and light - this impact has been truly invaluable andwill never be forgotten.  \nAcknowledgments  \nFirst and foremost, I would like to express my deepest gratitude to my advisor, Dr. Rockee Zhang. His unwavering support, guidance, and belief in my potential have been instrumental in reaching this milestone. Dr. Zhang not only provided me with the opportunity to pursue this research but also continually inspired me with his dedication and profound knowledge. He treated us like family, always showing patience, understanding, and flexibility. I am fortunate to have worked under his guidance, and I truly appreciate his trust in me to succeed. Thank you, Dr. Zhang, for everything.  \nI would also like to sincerely thank the members of my dissertation committee sincerely, Dr. David Schvartzman, Dr. Cameron Homeyer, Dr. Samuel Cheng, and Dr. David Ebert. Their valuable feedback, insightful comments, and encouragement throughout this journey have greatly enriched my work. Their expertise and dedication to excellence have been truly motivating.  \nI am deeply grateful to Dr. Pak-wai Chan, Director of the Hong Kong Observatory, for providing this research’s essential data and resources. His invaluable advice and support have significantly contributed to the success of this work.  \nI want to thank my colleagues for their collaborative spirit, which made this journey both fulfilling and enriching. I also thank the Department of Electrical and Computer Engineering (ECE) and the Advanced Radar Research Center (ARRC) for providing an  \ninnovative and supportive environment. The resources and opportunities offered by the department and ARRC were instrumental in completing this work.  \nContents  \nAcknowledgments v  \nList Of Tables x  \nList Of Figures xii  \nAbstract xviii  \n1 Introduction 1  \n1.1 Radar Sensing ................................... 1  \n1.2 Machine Learning and Deep Learning: A Brief Overview ........... 2  \n1.3 ML/DL and Radar Technology Intersection .................. 3  \n1.4 Outlines and Research Objectives ........................ 4  \n1.5 Research Methodology .............................. 7  \n2 Theoretical Foundations of Machine Learning Methods 8  \n2.1 Overview of Machine Learning .......................... 8  \n2.1.1 History and Evolution .......................... 8  \n2.1.2 Significance in Scientific Research .................... 9  \n2.2 Types of Machine Learning ............................ 10  \n2.2.1 Supervised Learning ........................... 10  \n2.2.2 Unsupervised Learning .......................... 11  \n2.2.3 Reinforcement Learning ......................... 11  \n2.3 Common Machine Learning Models Used .................... 12  \n2.3.1 Regression Models ............................ 13  \n2.3.1.1 Linear Reg","cbCaiftEPK7aqJza","https://ap.wps.com/l/cbCaiftEPK7aqJza","pdf",17489652,1,222,"English","en",105,"# Acknowledgments\n# Contents\n# 1 Introduction\n## 1.1 Radar Sensing\n## 1.2 Machine Learning and Deep Learning: A Brief Overview\n## 1.3 ML/DL and Radar Technology Intersection\n## 1.4 Outlines and Research Objectives\n## 1.5 Research Methodology\n# 2 Theoretical Foundations of Machine Learning Methods\n## 2.1 Overview of Machine Learning\n## 2.2 Types of Machine Learning\n## 2.3 Common Machine Learning Models Used\n## 2.4 Metaheuristic Optimization Algorithms for Data Quality Control\n## 2.5 Evaluation Metrics for ML Models\n# 3 Atmospheric Humidity Estimation from Wind Profiler Radar\n## 3.1 Introduction\n## 3.2 Instrumentation and Physical Modeling Methods\n## 3.3 Approach and Methodology\n## 3.4 Results and Discussion","[{\"question\":\"What is the dissertation’s main research goal?\",\"answer\":\"Improve radar sensing capabilities and enhance data quality using machine learning, with a focus on atmospheric humidity estimation from wind profiler radar.\"},{\"question\":\"Which machine learning techniques and models are discussed?\",\"answer\":\"The dissertation reviews core ML categories (supervised, unsupervised, reinforcement) and representative regression/classification models, including metrics used to evaluate model performance.\"},{\"question\":\"How does the work address data reliability issues?\",\"answer\":\"It incorporates data quality control and preprocessing steps, uses evaluation metrics to quantify performance, and applies methods to prevent overfitting during model development.\"}]","Improving Radar Sensing Capabilities and Data Quality Through Machine Learning | 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