Electromagnetic (EM) imaging is widely applied in sensing for security, biomedicine,
geophysics, and various industries. It is an ill-posed inverse problem whose solution is …

This work presents a comprehensive review of the developments in using Machine Learning
(ML)-based algorithms for the modeling and design optimization of switched reluctance …

In this work, we design an iterative deep neural network to solve full-wave inverse scattering
problems (ISPs) in the 2-D case. Forward modeling neural networks that predict the …

Electrical Impedance Tomography (EIT) has emerged as a valuable medical imaging
modality, which visualizes the conductivity distribution of a subject by performing multi …

In this article, an unrolling algorithm of the iterative subspace-based optimization method
(SOM) is proposed for solving full-wave inverse scattering problems (ISPs). The unrolling …

This paper studies the feasibility of a deep neural network (DNN) approach for bone fracture
diagnosis based on the non-invasive propagation of radio frequency waves. In contrast to …

In this study, a recurrent convolutional neural network (RCNN) is designed for full-wave
electromagnetic (EM) modeling. This network is equivalent to the finite difference time …

In this work, we present a deep-learning-based low-frequency (LF) data prediction scheme
to solve the highly nonlinear inverse scattering problem (ISP) with strong scatterers. The …

Recently, device-free human activity recognition has become a research hotspot, and great
progress has been made in ubiquitous computing. Among the different kinds of …

This paper proposes an efficient and fast method to create large datasets for machine
learning algorithms applied to brain stroke classification via microwave imaging systems …