[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127532-en":3,"doc-seo-127532-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127532,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning for Design Optimization of Antenna Applications - Doctor of Philosophy Thesis","Machine learning is used to tackle complex electromagnetic challenges including radio-frequency signal analysis, antennas, and artificial electromagnetic materials design, where conventional methods may fail. The dissertation evaluates ML techniques for antenna-related applications through three parts: antenna classification via radio-frequency fingerprint extraction using Gaussian mixture models; interactive learning frameworks to quantify mutual coupling in large-scale metasurface arrays and accelerate design optimization; and deep learning methods to model mutual coupling for near-field metasurface focusing. Validation includes classification using DNN analysis, optimization for beam and RCS reduction, and numerical simulations demonstrating accuracy and robustness.","Machine Learning for Design Optimization of Antenna Applications  \nby  \nYihan Ma  \nA thesis submitted in partial fulfilment of the requirements for the  \ndegree of Doctor of Philosophy  \nSchool of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom  \nOctober 2022  \nDeclaration  \nI, Yihan Ma, confirm that the research included within this thesis is my own work or that where it has been carried out in collaboration with, or supported by others, that this is duly acknowledged below and my contribution indicated. Previously published material is also acknowledged below.  \nI attest that I have exercised reasonable care to ensure that the work is original, and does not to the best of my knowledge break any UK law, infringe any third party’s copyright or other Intellectual Property Right, or contain any confidential material.  \nI accept that the College has the right to use plagiarism detection software to check the electronic version of the thesis.  \nI confirm that this thesis has not been previously submitted for the award of a degree by this or any other university.  \nThe copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author.  \nSignature: Yihan Ma  \nDate: 13th October 2022  \nTO MY FAMILY  \nAbstract  \nIn recent years, the rapid development of machine learning (ML) technique enables us to address complex electromagnetic (EM) problems such as Radio frequency (RF) signal analysis, antennas, and artificial EM materials design. Although ML can solve many EM problems where conventional methods have failed, developing the most appropriate framework to solve specific EM problems is still an open-ended question. This dissertation investigates the application of ML techniques to solve the problems related to antenna applications. The research work has been conducted in the following three parts:  \nFirstly, a novel Radio frequency fingerprint (RFF) extraction approach based on the Gaussian mixture model (GMM) is employed to achieve antenna classification from its scattering signals. In contrast to conventional statistical feature extraction methods, the proposed model achieves superior performance in terms of classification accuracy. Except for the application in antenna classification, we also use EM signals scattered from human body movement to detect their stand-by emotion state. The approach is verified by applying the deep neural network (DNN) to analyze a large amount of measurement data.  \nSecondly, I propose and demonstrate a novel framework based on an interactive learning to quantify the influence of mutual coupling between meta-atoms in the large scale non-identical metasurface array. By incorporating the deep neural network with optimization algorithm, the proposed architecture can achieve rapid design optimizationson electrically large metasurfaces with disordered meta-atoms. The approach has been validated by several examples for antenna beam optimization and radar cross section (RCS) reduction.  \nFinally, a novel deep learning (DL) based framework is employed to account for the  \nmutual coupling effects in the design of metasurfaces for near-field focusing. The proposed approach is demonstrated via numerical simulations that DL techniques can be used to tackle complex EM problems with a combination of good accuracy from numerical solutions and robustness of analytical approaches.  \nAcknowledgments  \nFirst and foremost, I would like to express my sincere gratitude to my supervisor Prof. Yang Hao for his unlimited support and unconditional guidance during my PhD journey. He has encourage me to think creatively and go through some tough time during my PhD studies at Queen Mary University of London. I would also like to thank my second supervisor Dr. Akram Alomainy and independent assessor Dr. Flynn Castles for their valuable suggestions on my study. Without the generous financial ","cbCairpGBdpMREgz","https://ap.wps.com/l/cbCairpGBdpMREgz","pdf",62487280,2,1,196,"English","en",105,"# Abstract\n# Acknowledgments\n# List of Publications","[{\"question\":\"What antenna-related problem does the thesis address using machine learning first?\",\"answer\":\"It develops a radio-frequency fingerprint (RFF) extraction approach using a Gaussian mixture model to classify antennas from scattering signals, with a deep neural network validating performance on measurement data.\"},{\"question\":\"How does the thesis model and optimize mutual coupling in large-scale metasurface arrays?\",\"answer\":\"It proposes an interactive learning framework that quantifies mutual coupling between meta-atoms, combining a deep neural network with an optimization algorithm to enable rapid design optimization for electrically large, disordered metasurfaces.\"},{\"question\":\"How are mutual coupling effects handled for metasurface near-field focusing in the final part?\",\"answer\":\"A deep learning based framework is introduced to account for mutual coupling in the design of metasurfaces for near-field focusing, demonstrated through numerical simulations that balance accuracy and robustness compared with numerical and analytical solutions.\"}]","Machine Learning for Design Optimization of Antenna Applications - 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