no code implementations • 15 Nov 2023 • YiFan Li, Feng Shu, Jun Zou, Wei Gao, Yaoliang Song, Jiangzhou Wang
To satisfy the high-resolution requirements of direction-of-arrival (DOA) estimation, conventional deep neural network (DNN)-based methods using grid idea need to significantly increase the number of output classifications and also produce a huge high model complexity.
no code implementations • 18 Aug 2023 • Bangti Jin, Zehui Zhou, Jun Zou
Furthermore, we develop an approach for approximating bi-Lipschitz maps on infinite-dimensional spaces that simultaneously approximate the forward and inverse maps, by combining model reduction with principal component analysis and INNs for approximating the reduced map, and we analyze the overall approximation error of the approach.
no code implementations • 29 Apr 2023 • Jianfeng Ning, Fuqun Han, Jun Zou
In this work, we focus on the inverse medium scattering problem (IMSP), which aims to recover unknown scatterers based on measured scattered data.
no code implementations • 21 Oct 2020 • Yat Tin Chow, Fuqun Han, Jun Zou
We propose a novel direct sampling method (DSM) for the effective and stable inversion of the Radon transform.
Numerical Analysis Numerical Analysis 44A12, 65R32, 92C55, 94A08
