Prediction of Electromagnetic Field Exposure at 20–100 GHz for Clothed Human Body Using An Adaptively Reconfigurable Architecture Neural Network with Weight …

M Yao, Z Wei, K Li, GF Pedersen… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
In the context of forthcoming sixth-generation (6G) wireless communication, the sub-
terahertz and terahertz frequency spectrum are anticipated. At such high frequencies …

An Efficient Training Data Collection Method for Machine Learning-Based Frequency Selective Surface Design

YF Liu, LY Xiao, W Shao, L Peng… - IEEE Antennas and …, 2024 - ieeexplore.ieee.org
To enhance the efficiency of the training dataset construction and improve the machine
learning (ML) model performance for electromagnetic (EM) devices modeling and design …

UWB Transparent Metamaterial Absorber With Optimally Patterned Gold Nanolayer

S Soghi, H Heidar, MR Haraty… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
A novel thin, optically transparent microwave metamaterial absorber (TMMA) is presented.
The proposed structure utilizes a 3-mm-thick polycarbonate (PC) main substrate coated on …

Out of Distribution Domain Exploration by Multi-Fidelity Deep Learning Model to Estimate Electromagnetic Responses of Metasurfaces

N Wang, G Wan, Q Ding, X Ma - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The multifidelity approach is a promising way to efficiently train the neural network which can
be used to estimate the electromagnetic (EM) responses of metasurfaces. Empirical …

AI/ML: A powerful tool for future microwave engineering problems

S Bharti, U Dey, A Patnaik - 2023 IEEE Microwaves, Antennas …, 2023 - ieeexplore.ieee.org
The potential futuristic role of Artificial Intelligence/Machine Learning (AI/ML) in microwave
and antenna engineering is discussed in this paper. In context to electro-magnetic circuit …