{"id":53,"date":"2025-05-17T22:32:59","date_gmt":"2025-05-17T22:32:59","guid":{"rendered":"https:\/\/research.ece.ncsu.edu\/ci4r\/?page_id=53"},"modified":"2025-05-17T23:34:48","modified_gmt":"2025-05-17T23:34:48","slug":"ai-ml-in-radar-system-design","status":"publish","type":"page","link":"https:\/\/research.ece.ncsu.edu\/ci4r\/ai-ml-in-radar-system-design\/","title":{"rendered":"AI\/ML in Radar System Design"},"content":{"rendered":"\n<h3 class=\"wp-block-heading\">PhD-level Special Topics Class <\/h3>\n\n\n\n<p><strong>[The University of Alabama, Spring 2022]<\/strong><\/p>\n\n\n\n<p>Recent advances and applications of AI and deep learning as applied to radar system design, especially next generation cognitive radar systems, will be discussed.<br>1:&nbsp; Basic principles of radar systems; radar range equation, link budget, FMCW and MIMO radar. Radar data representations (2D,3D,4D)<br>2: Basic principles of machine learning and deep learning<br>3: Radar applications (SAR imaging, micro-Doppler, biomedical, human-computer interaction, autonomous vehicles, cyber-physical human systems (CPHS))<br>4: Sensing specific challenges &#8211; training under low sample support, synthetic data generation and transfer learning<br>5: Physics-aware machine learning and application to radar-based and multi-modal sensing<br>6: Sequential modeling, online and iterative learning methods<br>7: Cognitive radar and cognitive process modeling<br>8: Markov decision processes, reinforcement learning and implementation of a perception-action cycle in fully-adaptive radar&nbsp;<\/p>\n\n\n\n<p><em>Pre-Requisites:&nbsp; B.S. in ECE or CS or related field<\/em>;  <em>Prior experience with radar and AI\/ML helpful.<\/em><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>MODULE 1 &#8211; INTRODUCTION TO CONCEPT OF COGNITIVE RADAR<\/strong><\/h3>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/ieeexplore.ieee.org\/document\/8961364\">An Overview of Cognitive Radar:<br>\u200b&nbsp; &nbsp; &nbsp; &nbsp;Past, Present, and Future<\/a><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/digital-library.theiet.org\/doi\/book\/10.1049\/sbra529e\">DNN Design for Radar Applications, prologue: Perspectives on Deep Learning of RF data<\/a><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/digital-library.theiet.org\/doi\/book\/10.1049\/sbra529e\">DNN Design for radar applications, Ch. 1: radar systems, signals, &nbsp;and phenomenology<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Lecture 1:&nbsp;&nbsp;<a href=\"https:\/\/research.ece.ncsu.edu\/ci4r\/wp-content\/uploads\/sites\/40\/2025\/05\/AI4R_Lecture1_Introduction.pdf\" data-type=\"attachment\" data-id=\"207\">Introduction<\/a><\/p>\n<\/div>\n<\/div>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>MODULE 2 &#8211; WAVEFORM DIVERSITY AND BIOMIMETIC RADAR&nbsp;<\/strong><\/h3>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/ieeexplore.ieee.org\/document\/7771665\">overview of waveform diversity<\/a><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/ieeexplore.ieee.org\/document\/4775878\">waveform diversity in radar signal processing<\/a><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/ieeexplore.ieee.org\/document\/6766229\">Biomimetic echolocation for radar and sonar<\/a><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/ieeexplore.ieee.org\/document\/8746859\">Applying biomimetic sensors to<br>\u200bachieve autonomous navigation<\/a><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/ieeexplore.ieee.org\/document\/8828030\">Toward millimeter wave&nbsp;<br>joint radar communications<\/a><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\">Radar and Communication Coexistence: An Overview: A Review of Recent Methods<\/a><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\">joint radar and communication design . . .&nbsp; the road ahead<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<ul class=\"wp-block-list\">\n<li>Lecture 2:&nbsp;<a href=\"https:\/\/research.ece.ncsu.edu\/ci4r\/wp-content\/uploads\/sites\/40\/2025\/05\/AI4R_Lecture2_RadarWaveforms.pdf\" data-type=\"attachment\" data-id=\"208\">Radar Waveforms<\/a><\/li>\n\n\n\n<li>Lecture 3:&nbsp;<a href=\"https:\/\/research.ece.ncsu.edu\/ci4r\/wp-content\/uploads\/sites\/40\/2025\/05\/AI4R_Lecture3_SpectrumSharing.pdf\" data-type=\"attachment\" data-id=\"209\">Spectrum Sharing<\/a><\/li>\n<\/ul>\n<\/div>\n<\/div>\n\n\n\n<p><strong>Student Presentations:<br>\u200b<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Inspiration from Dolphins for Waveform Diversity<\/strong>&nbsp;[Presenter: Deepak Elluru] \n<ul class=\"wp-block-list\">\n<li>T.G. Leighton, G.H. Chua, P.R. White, K.F. Tong, H.D. Griffiths, and D.J. Daniels, &#8220;<a href=\"https:\/\/royalsocietypublishing.org\/doi\/10.1098\/rspa.2013.0512\" target=\"_blank\" rel=\"noreferrer noopener\">Radar Clutter Suppression and Target Discrimination Using Twin Inverted Pulses<\/a>,&#8221; in Proc. The Royal Society A 469: 20130512, Sep. 2013.<\/li>\n\n\n\n<li>M. Greco and F. Gini, &#8220;The Biosonar of the Mediterranean Bottlenose Dolphins:&nbsp; Analysis and Modeling of Echolocation Signals,&#8221; in&nbsp;<a href=\"https:\/\/shop.theiet.org\/biologically-inspired-radar-and-sonar-lessons-from-nature\" target=\"_blank\" rel=\"noreferrer noopener\">Biologically-Inspired Radar and Sonar: Lessons from Nature<\/a>, Ed. A. Balleri, H. Griffiths, and C. Baker, IET, 2017.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p>\u200b<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Phase-Perturbed Waveform Design for SAR-ECCM<\/strong>&nbsp;[Presenter: Emre Kurtoglu] \n<ul class=\"wp-block-list\">\n<li>M. Soumekh, &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/1603414\" target=\"_blank\" rel=\"noreferrer noopener\">SAR-ECCM using phase-perturbed LFM chirp&nbsp;<\/a><a href=\"uploads\/1\/1\/0\/2\/110261619\/phase-perturbed_waveform_design_for_sar-eccm.pdf\">phase-perturbed_waveform_design_for_sar-eccm.pdf<\/a><a href=\"https:\/\/ieeexplore.ieee.org\/document\/1603414\" target=\"_blank\" rel=\"noreferrer noopener\">signals and DRFM repeat jammer penalization<\/a>,&#8221; in IEEE Transactions on Aerospace and Electronic Systems, vol. 42, no. 1, pp. 191-205, Jan. 2006.<\/li>\n\n\n\n<li>M. Soumekh, &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/1435879\" target=\"_blank\" rel=\"noreferrer noopener\">SAR-ECCM using phase-perturbed LFM chirp signals and DRFM repeat jammer penalization<\/a>,&#8221; IEEE International Radar Conference, 2005., 2005, pp. 507-512.<\/li>\n\n\n\n<li>L. Wei, Y. Lin and G. Xu, &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/8551978\" target=\"_blank\" rel=\"noreferrer noopener\">A New Method of Phase-Perturbed LFM Chirp Signals for SAR ECCM<\/a>,&#8221; 2018 China International SAR Symposium (CISS), 2018.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u200b<strong>Waveform Diversity with Noise Radar&nbsp;<\/strong>[Presenter: Eddie Hackett]\n<ul class=\"wp-block-list\">\n<li>S. D. Blunt et al., &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/9216208\" target=\"_blank\" rel=\"noreferrer noopener\">Principles and Applications of Random FM Radar Waveform Design<\/a>,&#8221; in IEEE Aerospace and Electronic Systems Magazine, vol. 35, no. 10, pp. 20-28, 1 Oct. 2020.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Dual Function Radar Communication Systems<\/strong>&nbsp;[Presenter: Ladi Adeoluwa] \n<ul class=\"wp-block-list\">\n<li>\u200bA. Hassanien, M. G. Amin, E. Aboutanios and B. Himed, &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/8828023\" target=\"_blank\" rel=\"noreferrer noopener\">Dual-Function Radar Communication Systems: A Solution to the Spectrum Congestion Problem<\/a>,&#8221; in IEEE Signal Processing Magazine, vol. 36, no. 5, pp. 115-126, Sept. 2019.<\/li>\n\n\n\n<li>A. Hassanien, M. G. Amin, Y. D. Zhang and F. Ahmad, &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/7746569\" target=\"_blank\" rel=\"noreferrer noopener\">Signaling strategies for dual-function radar communications: an overview<\/a>,&#8221; in IEEE Aerospace and Electronic Systems Magazine, vol. 31, no. 10, pp. 36-45, October 2016.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Cognitive radar for spectrum sharing<\/strong>&nbsp;[Presenter: Sean Kearney] \n<ul class=\"wp-block-list\">\n<li>\u200bA. F. Martone et al., &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/9532494\" target=\"_blank\" rel=\"noreferrer noopener\">Closing the Loop on Cognitive Radar for Spectrum Sharing<\/a>,&#8221; in IEEE Aerospace and Electronic Systems Magazine, vol. 36, no. 9, pp. 44-55, 1 Sept. 2021.<\/li>\n\n\n\n<li>P. Stinco, M. Greco, F. Gini and B. Himed, &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/7746567\" target=\"_blank\" rel=\"noreferrer noopener\">Cognitive radars in spectrally dense environments,<\/a>&#8221; in IEEE Aerospace and Electronic Systems Magazine, vol. 31, no. 10, pp. 20-27, October 2016.\u200b<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<div style=\"height:46px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>MODULE 3 &#8211;&nbsp; MACHINE LEARNING AND DEEP NEURAL NETWORKS<\/strong><\/h3>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/digital-library.theiet.org\/doi\/book\/10.1049\/sbra529e\">DNN Design for Radar Applications, Ch. 2: Basic principles of machine learning<\/a><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/digital-library.theiet.org\/doi\/book\/10.1049\/sbra529e\">DNN Design for radar Applications, ch. 3: theoretical foundations of deep learning<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<ul class=\"wp-block-list\">\n<li>Lecture 4 &#8211;&nbsp;<a href=\"https:\/\/research.ece.ncsu.edu\/ci4r\/wp-content\/uploads\/sites\/40\/2025\/05\/AI4R_Lecture4_MachineLearning.pdf\" data-type=\"attachment\" data-id=\"210\">Introduction to Machine Learning<\/a><\/li>\n\n\n\n<li>Lecture 5 &#8211;&nbsp;<a href=\"https:\/\/research.ece.ncsu.edu\/ci4r\/wp-content\/uploads\/sites\/40\/2025\/05\/AI4R_Lecture5_DeepLearning.pdf\" data-type=\"attachment\" data-id=\"211\">Introduction to Deep Learning<\/a><\/li>\n<\/ul>\n<\/div>\n<\/div>\n\n\n\n<div style=\"height:43px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>MODULE 4 &#8211;&nbsp; CLASSIFICATION OF RADAR DATASETS WITH DEEP LEARNING<\/strong><\/h3>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/ieeexplore.ieee.org\/document\/8746862\">radar-based human motion recognition<br>\u200bwith deep learning<\/a><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/ieeexplore.ieee.org\/document\/9351574\">Deep learning meets SAR<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Lecture 6: &nbsp;<a href=\"https:\/\/research.ece.ncsu.edu\/ci4r\/wp-content\/uploads\/sites\/40\/2025\/05\/ai4r_lecture6_md_classification_limited_data.pdf\" data-type=\"attachment\" data-id=\"219\">Radar Micro-Doppler Classification with Limited Data<\/a><\/p>\n<\/div>\n<\/div>\n\n\n\n<p><strong>Student Presentations:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u200bSAR Image Classification with Transfer Learning&nbsp;<\/strong>[Presenter: Sean Kearney] \n<ul class=\"wp-block-list\">\n<li><strong>\u200b<\/strong>Z. Huang, Z. Pan and B. Lei, &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/8907833\" target=\"_blank\" rel=\"noreferrer noopener\">What, Where, and How to Transfer in SAR Target Recognition Based on Deep CNNs<\/a>,&#8221; in IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 4, pp. 2324-2336, April 2020.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Transfer Learning with Novel Optimization Function<\/strong>&nbsp;[Presenter: Eddie Hackett] \n<ul class=\"wp-block-list\">\n<li>Y. Xu and H. Lang, &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/9216603\" target=\"_blank\" rel=\"noreferrer noopener\">Ship Classification in SAR Images With Geometric Transfer Metric Learning<\/a>,&#8221; in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 8, pp. 6799-6813, Aug. 2021.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Transfer Learning in Dynamic Environments&nbsp;<\/strong>[Presenter:&nbsp; Emre Kurtoglu] \n<ul class=\"wp-block-list\">\n<li>C. Yang, Y. -m. Cheung, J. Ding and K. C. Tan, &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/9352556\" target=\"_blank\" rel=\"noreferrer noopener\">Concept Drift-Tolerant Transfer Learning in Dynamic Environments<\/a>,&#8221; in IEEE Transactions on Neural Networks and Learning Systems, 2021.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Data Augmentation and Training SAR Imagery&nbsp;with Limited Data<\/strong>&nbsp;[Presenter: Ladi Adeoluwa] \n<ul class=\"wp-block-list\">\n<li>B. Lewis, M. Levy, J. Nehrbass, T. Scarnati, E. Sudkamp, E. Zelnio, &#8220;<a href=\"https:\/\/apps.dtic.mil\/sti\/citations\/AD1079518\" target=\"_blank\" rel=\"noreferrer noopener\">Machine Learning Techniques for SAR Data Augmentation (Preprint)<\/a>,&#8221; in&nbsp;Deep Neural Network Design for Radar Applications, Ed. S.Z. Gurbuz, IET, 2020.<\/li>\n\n\n\n<li>T. Scarnati and B. Lewis, &#8220;A deep learning approach to the Synthetic and Measured Paired and Labeled Experiment (SAMPLE) challenge problem,&#8221; Proc. SPIE 10987, Algorithms for Synthetic Aperture Radar Imagery XXVI, 109870G, May 14, 2019.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Techniques for Exploiting Synthetic Data in Classification of Real Data&nbsp;<\/strong>[Presenter:&nbsp; Deepak Elluru] \n<ul class=\"wp-block-list\">\n<li>N. Inkawhich, M. .J. Inkawhich, E.K. Davis, U.K. Majumder, E. Tripp, C. Capraro, and Y. Chen, &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/stamp\/stamp.jsp?arnumber=9356129\" target=\"_blank\" rel=\"noreferrer noopener\">Bridging a gap in SAR-ATR:&nbsp; Training on Fully Synthetic and Testing on Measured Data<\/a>,&#8221; IEEE Journal of Selected Topics in Applied Earth Observations and Remove Sensing, Vol. 14, 2021.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<div style=\"height:44px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>MODULE 5 &#8211;&nbsp; PHYSICS-AWARE DEEP LEARNING&nbsp;<\/strong><\/h3>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/dl.acm.org\/doi\/10.1145\/3514228\">Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems<\/a><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.researchgate.net\/publication\/351814752_Physics-informed_machine_learning\">PHYSICS-INFORMED MACHINE LEARNING<\/a><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/digital-library.theiet.org\/doi\/book\/10.1049\/sbra529e\">DNN Design for radar applications, CH 5: PHYSICS-AWARE TRAINING OF RADAR MICRO-DOPPLER SIGNATURES<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Lecture 7 &#8211; Physics-Aware Generative Adversarial Networks for Training DNNs with Synthetic Samples<\/p>\n<\/div>\n<\/div>\n\n\n\n<p><strong>Student Presentations:<br>\u200b<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Graph Neural Networks for Particle Momentum Estimation in the CMS Trigger System&nbsp;<\/strong>[Presenter:&nbsp; Emre Kurtoglu] \n<ul class=\"wp-block-list\">\n<li>Presentation of work conducted as part of Google Summer of Code (2021), Machine Learning for Science (ML4SCI) &#8211; Mentor: Dr. Sergei Gleyzer\u200b<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Physics-Based Generative Adversarial Models for Image Restoration&nbsp;&nbsp;<\/strong>&nbsp;[Presenter: Ladi Adeoluwa]\n<ul class=\"wp-block-list\">\n<li>J. Pan, J. Dong, Y. Liu, J. Zhang, J. Ren, J. Tang, Y.-W. Tai and M-H. Yang, &#8220;<a href=\"\/\/efaidnbmnnnibpcajpcglclefindmkaj\/viewer.html?pdfurl=https%3A%2F%2Farxiv.org%2Fpdf%2F1808.00605.pdf&amp;clen=9393506&amp;chunk=true\" target=\"_blank\" rel=\"noreferrer noopener\">Physics-based generative adversarial models for image restoration and beyond<\/a>,&#8221; IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Physics-Aware Machine Learning for Geosciences and Remote Sensing&nbsp;<\/strong>[Presenter: Deepak Elluru]&nbsp;\n<ul class=\"wp-block-list\">\n<li>\u200bG. Camps-Valls, D. H. Svendsen, J. Cortes-Andres, A. Mareno-Martinez, et. al., &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/9554521\" target=\"_blank\" rel=\"noreferrer noopener\">Physics-Aware Machine Learning for Geosciences and Remote Sensing<\/a>,&#8221; IEEE Geoscience and Remote Sensing Symposium, July, 2021.<\/li>\n\n\n\n<li>Z. Huang, C. O. Dumitru and J. Ren, &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/9554842\" target=\"_blank\" rel=\"noreferrer noopener\">Physics-Aware Feature Learning of Sar Images with Deep Neural Networks: A Case Study<\/a>,&#8221; 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 1264-1267.\u200b<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Physics-Aware GAN-based Generation of 3D Data<\/strong>&nbsp;[Presenter: Sean Kearney] \n<ul class=\"wp-block-list\">\n<li>Y. Xie, E. Franz, M. Chu, N. Thuerey, &#8220;<a href=\"https:\/\/arxiv.org\/abs\/1801.09710\" target=\"_blank\" rel=\"noreferrer noopener\">TempoGAN:&nbsp; A Temporally Coherent, Volumetric GAN for Super-Resolution Fluid Flow<\/a>,&#8221; ACM Transactions on Graphics, Vol. 37, Iss. 4, Aug. 2018.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Physics-Based Neural Network Architecture Design<\/strong>&nbsp;[Presenter: Eddie Hackett]\n<ul class=\"wp-block-list\">\n<li>N. Muralidhar, L. Bu, Z. Cao, et. al., &#8220;<a href=\"https:\/\/www.researchgate.net\/publication\/340218044_PhyNet_Physics_Guided_Neural_Networks_for_Particle_Drag_Force_Prediction_in_Assembly\" target=\"_blank\" rel=\"noreferrer noopener\">PhyNet: Physics Guided Neural Networks for Particle Drag Force Prediction in Assembly<\/a>&#8221; in Proc.&nbsp; SIAM International Conference on Data Mining, 2020.<\/li>\n\n\n\n<li>R. Stewart and S. Ermon, &#8220;<a href=\"\/\/efaidnbmnnnibpcajpcglclefindmkaj\/viewer.html?pdfurl=https%3A%2F%2Fwww.aaai.org%2FConferences%2FAAAI%2F2017%2FPreliminaryPapers%2F12-Stewart-14967.pdf&amp;clen=1031650&amp;chunk=true\" target=\"_blank\" rel=\"noreferrer noopener\">Label-free Supervision of Neural Networks with Physics and Domain Knowledge<\/a>,&#8221; in Proc. of 31st AAAI Conference on Artificial Intelligence, 2017.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<div style=\"height:46px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>MODULE 6 &#8211;&nbsp; SEQUENTIAL MODELING AND CONTINUAL LEARNING<\/strong><\/h3>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/research.ece.ncsu.edu\/ci4r\/wp-content\/uploads\/sites\/40\/2025\/05\/continuallifelonglearning_parisi2019.pdf\">CONTINUAL LIFELONG LEARNING:&nbsp; A REVIEW (PARISI, 2019)<\/a><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.cs.uic.edu\/~liub\/lifelong-learning\/continual-learning.pdf\">LIFELONG MACHINE LEARNING (CHEN &amp; LIU, 2018):&nbsp; CH. 4 &#8211; CONTINUAL LEARNING AND CATASTROPHIC FORGETTING<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Lecture 8 : <a href=\"https:\/\/research.ece.ncsu.edu\/ci4r\/wp-content\/uploads\/sites\/40\/2025\/05\/AI4R_Lecture8_ContinuousLearning-1.pdf\" data-type=\"attachment\" data-id=\"213\">Continual&nbsp;and Incremental Learning<\/a><\/p>\n<\/div>\n<\/div>\n\n\n\n<p><strong>Student Presentations:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Continual Learning in Radar Micro-Doppler<\/strong>&nbsp;[Presenter: Deepak Elluru]\n<ul class=\"wp-block-list\">\n<li>\u200bD. Lee, H. Park, T. Moon and Y. Kim, &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/9319850\" target=\"_blank\" rel=\"noreferrer noopener\">Continual Learning of Micro-Doppler Signature-Based Human Activity Classification<\/a>,&#8221; in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 3502405, doi: 10.1109\/LGRS.2020.3046015.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Continual Learning in GAN-based Image Synthesis<\/strong>&nbsp;[Presenter: Ladi Adeoluwa]\n<ul class=\"wp-block-list\">\n<li>M. Zhai, L.&nbsp;Chen, F. Tung, J.&nbsp;He, M.&nbsp;Nawhal and Greg Mori. \u201c<a href=\"https:\/\/arxiv.org\/abs\/1907.10107\" target=\"_blank\" rel=\"noreferrer noopener\">Lifelong GAN: Continual Learning for Conditional Image Generation<\/a>.\u201d&nbsp;<em>2019 IEEE\/CVF International Conference on Computer Vision (ICCV)<\/em>&nbsp;(2019): 2759-2768.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Incremental Classification&nbsp;<\/strong>[Presenter: Eddie Hackett]\n<ul class=\"wp-block-list\">\n<li>C.&nbsp;Guo, H.&nbsp;Zhou, &#8220;<a href=\"https:\/\/www.spiedigitallibrary.org\/conference-proceedings-of-spie\/10806\/2502994\/Synthetic-aperture-radar-target-identification-based-on-incremental-kernel-extreme\/10.1117\/12.2502994.full?SSO=1\" target=\"_blank\" rel=\"noreferrer noopener\">Synthetic aperture radar target identification based on incremental kernel extreme learning machine<\/a>,&#8221; Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108060L (9 August 2018)<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Iterative Re-Training in SAR Classification<\/strong>&nbsp;[Presenter: Sean Kearney]\n<ul class=\"wp-block-list\">\n<li>A.&nbsp;Ahmadibeni, B.&nbsp;Jones, D.&nbsp;Smith, and A.&nbsp;Shirkhodaie, &#8220;<a href=\"https:\/\/dl.acm.org\/doi\/abs\/10.1007\/978-3-030-61725-7_19\" target=\"_blank\" rel=\"noreferrer noopener\">Dynamic Transfer Learning from Physics-Based Simulated SAR Imagery for Automatic Target Recognition<\/a>,&#8221;&nbsp;In&nbsp;<em>Dynamic Data Driven Applications Systems: Third International Conference, DDDAS 2020, Boston, MA, USA, October 2-4, 2020, Proceedings<\/em>. Springer-Verlag, Berlin, Heidelberg, 152\u2013159.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Continual Learning for Activity Recognition<\/strong>&nbsp;[Presenter: Emre Kurtoglu]\n<ul class=\"wp-block-list\">\n<li>J.&nbsp;Ye, P.&nbsp;Nakwijit, M.&nbsp;Schiemer, S.&nbsp;Jha, and F.&nbsp;Zambonelli,&nbsp;&#8220;<a href=\"https:\/\/dl.acm.org\/doi\/fullHtml\/10.1145\/3440036\" target=\"_blank\" rel=\"noreferrer noopener\">Continual Activity Recognition with Generative Adversarial Networks<\/a>,&#8221;&nbsp;<em>ACM Trans. Internet Things<\/em>&nbsp;2, 2, Article 9 (March 2021)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<div style=\"height:42px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>MODULE 7 &#8211;&nbsp; COGNITIVE RADAR HARDWARE DESIGN<\/strong><\/h3>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.sto.nato.int\/publications\/STO%20Technical%20Reports\/STO-TR-SET-227\/$$TR-SET-227-ALL.pdf\">NATO SET 227 RTG FINAL REPORT ON COGNITIVE RADAR<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p>Lecture 9:&nbsp;<a href=\"https:\/\/research.ece.ncsu.edu\/ci4r\/wp-content\/uploads\/sites\/40\/2025\/05\/AI4R_Lecture9_CognitiveRadarHardware.pdf\" data-type=\"attachment\" data-id=\"214\">&nbsp;Enabling Hardware Technologies for Cognitive Radar<\/a><\/p>\n<\/div>\n<\/div>\n\n\n\n<p><strong>Student Presentations:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Reconfigurable RF Transceiver Design<\/strong>&nbsp;[Presenter: Deepak Elluru]\n<ul class=\"wp-block-list\">\n<li>\u200b&nbsp;A. Egbert, A. Goad, C. Baylis, A. F. Martone, B. H. Kirk and R. J. M. Ii, &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/9669986\" target=\"_blank\" rel=\"noreferrer noopener\">Continuous Real-Time Circuit Reconfiguration to Maximize Average Output Power in Cognitive Radar Transmitters,<\/a>&#8221; in IEEE Transactions on Aerospace and Electronic Systems.<\/li>\n\n\n\n<li>C. Baylis, R. J. Marks, A. Egbert and C. Latham, &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/9455195\" target=\"_blank\" rel=\"noreferrer noopener\">Artificially Intelligent Power Amplifier Array (AIPAA): A New Paradigm in Reconfigurable Radar Transmission,<\/a>&#8221; 2021 IEEE Radar Conference (RadarConf21), 2021.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Bayesian Framework for Optimal Cognitive Radar Tracking<\/strong>&nbsp;[Presenter: Emre Kurtoglu]\n<ul class=\"wp-block-list\">\n<li>\u200bK. L. Bell, C. J. Baker, G. E. Smith, J. T. Johnson and M. Rangaswamy, &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/7181639\" target=\"_blank\" rel=\"noreferrer noopener\">Cognitive Radar Framework for Target Detection and Tracking<\/a>,&#8221; in IEEE Journal of Selected Topics in Signal Processing, vol. 9, no. 8, pp. 1427-1439, Dec. 2015.<\/li>\n\n\n\n<li>G. E. Smith et al., &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/7383794\" target=\"_blank\" rel=\"noreferrer noopener\">Experiments with cognitive radar<\/a>,&#8221; 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cognitive Radar with Multi-Functional Reconfigurable Antenna Arrays (MRAAs)<\/strong>&nbsp;[Presenter:&nbsp; Sean Kearney]\n<ul class=\"wp-block-list\">\n<li>\u200bA. C. Gurbuz, R. Mdrafi and B. A. Cetiner, &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/9109790\" target=\"_blank\" rel=\"noreferrer noopener\">Cognitive Radar Target Detection and Tracking With Multifunctional Reconfigurable Antennas<\/a>,&#8221; in IEEE Aerospace and Electronic Systems Magazine, vol. 35, no. 6, pp. 64-76, 1 June 2020.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cognitive Radar Experiments with CODIR Testbed<\/strong>&nbsp;[Presenter:&nbsp; Ladi Adeoluwa]\n<ul class=\"wp-block-list\">\n<li>\u200bR. Oechslin, U. Aulenbacher, K. Rech, S. Hinrichsen, S. Wieland and P. Wellig, &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/8367471\" target=\"_blank\" rel=\"noreferrer noopener\">Cognitive radar experiments with CODIR<\/a>,&#8221; International Conference on Radar Systems (Radar 2017), 2017.<\/li>\n\n\n\n<li>R. Oechslin, P. Wellig, U. Aulenbacher, S. Wieland and S. Hinrichsen, &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/8835493\" target=\"_blank\" rel=\"noreferrer noopener\">Cognitive Radar Performance Analysis with Different Types of Targets<\/a>,&#8221; 2019 IEEE Radar Conference (RadarConf), 2019.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Deployed Canadian Cognitive Radar Example<\/strong>&nbsp;[Presenter: Eddie Hackett]\n<ul class=\"wp-block-list\">\n<li>\u200bPonsford, A.; McKerracher, R.; Ding, Z.; Moo, P.; Yee, D. &#8220;<a href=\"https:\/\/www.mdpi.com\/1424-8220\/17\/7\/1588\" target=\"_blank\" rel=\"noreferrer noopener\">Towards a Cognitive Radar: Canada\u2019s Third-Generation High Frequency Surface Wave Radar (HFSWR) for Surveillance of the 200 Nautical Mile Exclusive Economic Zone<\/a>.&#8221; Sensors 2017, 17, 1588.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<div style=\"height:45px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>MODULE 8 &#8211;&nbsp; COGNITIVE PROCESS MODELING AND IMPLEMENTATION OF A PERCEPTION-ACTION CYCLE<\/strong><\/h3>\n\n\n\n<p>Lecture 10:  <a href=\"https:\/\/research.ece.ncsu.edu\/ci4r\/wp-content\/uploads\/sites\/40\/2025\/05\/AI4R_Lecture10_CognitiveProcessModeling.pdf\" data-type=\"attachment\" data-id=\"215\">Cognitive Process Modeling<\/a><\/p>\n\n\n\n<p><strong>Student Presentations:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Levels of Cognition<\/strong>&nbsp;[Presenter: Eddie Hackett]\n<ul class=\"wp-block-list\">\n<li>Horne, C., Ritchie, M. and Griffiths, H. (2018), &#8220;<a href=\"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/full\/10.1049\/iet-rsn.2018.5280\" target=\"_blank\" rel=\"noreferrer noopener\">Proposed ontology for cognitive radar systems<\/a>.&#8221; IET Radar Sonar Navig., 12: 1363-1370.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Partially Observable Markov Decision Processes (POMDPs) for Cognitive Radar<\/strong>&nbsp;[Presenter:&nbsp; Sean Kearney]\n<ul class=\"wp-block-list\">\n<li>\u200bA. Charlish and F. Hoffmann, &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/7131271\" target=\"_blank\" rel=\"noreferrer noopener\">Anticipation in cognitive radar using stochastic control<\/a>,&#8221; 2015 IEEE Radar Conference, 2015, pp. 1692-1697.<\/li>\n\n\n\n<li>A. Charlish, K. Bell and C. Kreucher, &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/9266338\" target=\"_blank\" rel=\"noreferrer noopener\">Implementing Perception-Action Cycles using Stochastic Optimization<\/a>,&#8221; 2020 IEEE Radar Conference (RadarConf20), 2020, pp. 1-6.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Layers of Cognition<\/strong>&nbsp;[Presenter:&nbsp; Deepak Elluru]\n<ul class=\"wp-block-list\">\n<li>S. Br\u00fcggenwirth, M. Warnke, C. Br\u00e4u, S. SimonWagner, T. M\u00fcller, P. Marquardt, and F. Rial, &#8220;<a href=\"https:\/\/www.intechopen.com\/chapters\/57862\" target=\"_blank\" rel=\"noreferrer noopener\">Sense Smart, Not Hard: A Layered Cognitive Radar Architecture<\/a>&#8220;, in Topics in Radar Signal Processing. London, United Kingdom: IntechOpen, 2018 [Online].&nbsp;<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Relationship Between Cognitive Radar and Radar Resource Management<\/strong>&nbsp;[Presenter: Ladi Adeoluwa]\n<ul class=\"wp-block-list\">\n<li>A. Charlish, F. Hoffmann, C. Degen and I. Schlangen, &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/9109776\" target=\"_blank\" rel=\"noreferrer noopener\">The Development From Adaptive to Cognitive Radar Resource Management<\/a>,&#8221; in IEEE Aerospace and Electronic Systems Magazine, vol. 35, no. 6, pp. 8-19, 1 June 2020.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Language in Cognitive Radar<\/strong>&nbsp;[Presenter: Emre Kurtoglu]\n<ul class=\"wp-block-list\">\n<li>A. Wang and V. Krishnamurthy, &#8220;<a href=\"https:\/\/ieeexplore.ieee.org\/document\/4436036\" target=\"_blank\" rel=\"noreferrer noopener\">Signal Interpretation of Multifunction Radars: Modeling and Statistical Signal Processing With Stochastic Context Free Grammar<\/a>,&#8221; in IEEE Transactions on Signal Processing, vol. 56, no. 3, pp. 1106-1119, March 2008.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>PhD-level Special Topics Class [The University of Alabama, Spring 2022] Recent advances and applications of AI and deep learning as applied to radar system design,&#8230;<\/p>\n","protected":false},"author":149,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-53","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/research.ece.ncsu.edu\/ci4r\/wp-json\/wp\/v2\/pages\/53","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/research.ece.ncsu.edu\/ci4r\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/research.ece.ncsu.edu\/ci4r\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/research.ece.ncsu.edu\/ci4r\/wp-json\/wp\/v2\/users\/149"}],"replies":[{"embeddable":true,"href":"https:\/\/research.ece.ncsu.edu\/ci4r\/wp-json\/wp\/v2\/comments?post=53"}],"version-history":[{"count":14,"href":"https:\/\/research.ece.ncsu.edu\/ci4r\/wp-json\/wp\/v2\/pages\/53\/revisions"}],"predecessor-version":[{"id":228,"href":"https:\/\/research.ece.ncsu.edu\/ci4r\/wp-json\/wp\/v2\/pages\/53\/revisions\/228"}],"wp:attachment":[{"href":"https:\/\/research.ece.ncsu.edu\/ci4r\/wp-json\/wp\/v2\/media?parent=53"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}