Artificial intelligence: New frontiers in real-time inverse scattering and electromagnetic imaging
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 …
help of big data, massive parallel computing, and optimization algorithms, machine learning …
Applying deep learning to medical imaging: a review
H Zhang, Y Qie - Applied Sciences, 2023 - mdpi.com
Deep learning (DL) has made significant strides in medical imaging. This review article
presents an in-depth analysis of DL applications in medical imaging, focusing on the …
presents an in-depth analysis of DL applications in medical imaging, focusing on the …
Joint inversion of audio-magnetotelluric and seismic travel time data with deep learning constraint
Deep learning is applied to assist the joint inversion for audio-magnetotelluric and seismic
travel time data. More specifically, deep residual convolutional neural networks (DRCNNs) …
travel time data. More specifically, deep residual convolutional neural networks (DRCNNs) …
[PDF][PDF] Ultrasound medical images classification based on deep learning algorithms: a review
With the development of technology and smart devices in the medical field, the computer
system has become an essential part of this development to learn devices in the medical …
system has become an essential part of this development to learn devices in the medical …
A YOLOv3 deep neural network model to detect brain tumor in portable electromagnetic imaging system
This paper presents the detection of brain tumors through the YOLOv3 deep neural network
model in a portable electromagnetic (EM) imaging system. YOLOv3 is a popular object …
model in a portable electromagnetic (EM) imaging system. YOLOv3 is a popular object …
Enhanced two-step deep-learning approach for electromagnetic-inverse-scattering problems: Frequency extrapolation and scatterer reconstruction
The electromagnetic-inverse-scattering (EMIS) problem is solved by a novel two-step deep-
learning (DL) approach in this article. The newly proposed two-step DL approach not only …
learning (DL) approach in this article. The newly proposed two-step DL approach not only …
3DInvNet: A deep learning-based 3D ground-penetrating radar data inversion
The reconstruction of the 3D permittivity map from ground-penetrating radar (GPR) data is of
great importance for map** subsurface environments and inspecting underground …
great importance for map** subsurface environments and inspecting underground …
The automated prediction of solar flares from SDO images using deep learning
In the last few years, there has been growing interest in near-real-time solar data
processing, especially for space weather applications. This is due to space weather impacts …
processing, especially for space weather applications. This is due to space weather impacts …
A deep neural network for general scattering matrix
The scattering matrix is the mathematical representation of the scattering characteristics of
any scatterer. Nevertheless, except for scatterers with high symmetry like spheres or …
any scatterer. Nevertheless, except for scatterers with high symmetry like spheres or …
Unrolled convolutional neural network for full-wave inverse scattering
An unrolled deep learning scheme for solving full-wave nonlinear inverse scattering
problems (ISPs) is proposed. Inspired by the so-called unrolled method, an iterative neural …
problems (ISPs) is proposed. Inspired by the so-called unrolled method, an iterative neural …