MO-SS.1P.2

Feasibility of Alzheimer’s Disease Early Detection through Machine Learning Applied to Microwave Sensing Data Collected from a Realistic Phantom

Leonardo Cardinali, Valeria Mariano, Jorge Alberto Tobon-Vasquez, Politecnico di Torino, Italy; Lorenzo Crocco, Consiglio Nazionale delle Ricerche, Italy; Francesca Vipiana, Politecnico di Torino, Italy
Session:
Machine-Learning as Applied to EM - Trends, Advances, and Applications Oral
Track:
Special Sessions
Location:
Spadolini: 0-01
Session Time:
Mon, 15 Jul, 13:40 - 17:20
Presentation Time:
Mon, 15 Jul, 14:00 - 14:20
Session Co-Chairs:
Marco Salucci, ELEDIA@UniTN - University of Trento and Zhi Ning Chen, National University of Singapore
Presentation
Discussion
Session MO-SS.1P
MO-SS.1P.1: Target Classification Using Physics-Assisted Machine Learning in 77 GHz Automotive Radar
Tiantian Yin, National University of Singapore, Singapore; Kai Tan, Fudan University, China; Xudong Chen, National University of Singapore, Singapore
MO-SS.1P.2: Feasibility of Alzheimer’s Disease Early Detection through Machine Learning Applied to Microwave Sensing Data Collected from a Realistic Phantom
Leonardo Cardinali, Valeria Mariano, Jorge Alberto Tobon-Vasquez, Politecnico di Torino, Italy; Lorenzo Crocco, Consiglio Nazionale delle Ricerche, Italy; Francesca Vipiana, Politecnico di Torino, Italy
MO-SS.1P.3: Optimization and Machine Learning for Antenna Array Healing
Jacob T. Young, Ryan J. Chaky, Ronald P. Jenkins, Sawyer D. Campbell, Pingjuan L. Werner, Douglas H. Werner, The Pennsylvania State University, United States
MO-SS.1P.4: Dual-Beam Forming Based on Physics-Driven Deep Learning Method for Programmable Metasurface
Jianghan Bao, Qiang Xiao, Che Liu, Tie Jun Cui, Southeast University, China
MO-SS.1P.5: Machine Learning-Enabled Wide-Beam Metasurface Antenna for Wide-Angle Scanning Antenna Array
Yanhe Lyu, Xinyu Wang, Zhi Ning Chen, National University of Singapore, Singapore
MO-SS.1P.6: Machine Learning as Applied to EM - Trends, Advances, and Applications
Parvathy Chittur Subramanianprasad, Yang Hao, Queen Mary University of London, United Kingdom
MO-SS.1P.7: A Machine Learning-based Inverse Scattering Method for Biomedical Imaging Segmentation
Naike Du, Xiuzhu Ye, Beijing Institute of Technology, China
MO-SS.1P.8: Solving Combined Field Integral Equations of Electrically Large Targets Based on Physics-informed Graph Residual Learning
Tao Shan, Beihang University, China; Maokun Li, Fan Yang, Shenheng Xu, Tsinghua University, China
MO-SS.1P.9: Some Advances of Machine Learning as Applied to Computational EM, Remote Sensing, and Medical Diagnostics
Branislav Notaroš, Cam Key, Hein Thant, Stephen Kasdorf, Colorado State University, United States
MO-SS.1P.10: Deep Learning Assisted Design of EM Skin Meta-Atoms within the System-by-Design
Federico Albi, Marco Salucci, ELEDIA@UniTN - University of Trento, Italy; Zhichao Lin, ELEDIA@TSINGHUA - Tsinghua University, China; Giacomo Oliveri, Andrea Massa, ELEDIA@UniTN - University of Trento, Italy
Resources
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