In recent years, deep learning (DL) is becoming an increasingly important tool for solving
inverse scattering problems (ISPs). This paper reviews methods, promises, and pitfalls of …

The fifth generation achieved tremendous success, which brings high hopes for the next
generation, as evidenced by the sixth generation (6G) key performance indicators, which …

This letter introduces a deep learning (DL) framework for the classification of multiple signals
in direction finding (DF) scenario via sensor arrays. Previous works in DL context mostly …

In recent years, artificial intelligence (AI) techniques have been developed rapidly. With the
help of big data, massive parallel computing, and optimization algorithms, machine learning …

A prior-knowledge-guided deep-learning-enabled (PK-DL) synthesis method is proposed for
enhancing the transmission bandwidth and phase shift range of metacells used for the …

Abstract Design closure and parameter optimisation are crucial in creating cutting-edge
antennas. Antenna performance can be improved by fine-tuning preliminary designs created …

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 …

Without phase information of the measured field data, the phaseless data inverse scattering
problems (PD-ISPs) counter more serious nonlinearity and ill-posedness compared with full …

Early results have shown the potential of Deep Learning (DL) to disrupt the fields of optical
inverse-design, particularly, the inverse design of nanostructures. In the last three years, the …

A prior-knowledge-guided deep-learning-enabled (PK-DL) synthesis method is proposed to
design the metacells with the full-range amplitude and phase control for suppressing the …