Special Issue on Artificial Intelligence: New Frontiers in Real-Time Inverse Scattering and Electromagnetic Imaging
AUGUST 2022 | VOLUME 70 | NUMBER 8
This Special Issue highlights how AI, machine learning, and deep learning can be leveraged towards the development of fast and reliable techniques for solving electromagnetic inverse scattering and imaging problems. It features 19 high-quality works from top research teams around the world, including methodological, applicative and overview articles.
Guest Editorial: Artificial Intelligence: New Frontiers in Real-Time Inverse Scattering and Electromagnetic ImagingM. Arrebola, M. Li, and M. Salucci
Physics Embedded Deep Neural Network for Solving Volume Integral Equation: 2-D CaseR. Guo, T. Shan, X. Song, M. Li, F. Yang, S. Xu, and A. Abubakar
Physics Embedded Deep Neural Network for Solving Full-Wave Inverse Scattering ProblemsR. Guo, Z. Lin, T. Shan, X. Song, M. Li, F. Yang, S. Xu, and A. Abubakar
Cascaded Complex U-Net Model to Solve Inverse Scattering Problems With Phaseless-Data in the Complex DomainF. Luo, J. Wang, J. Zeng, L. Zhang, B. Zhang, K. Xu, and X. Luo
Machine Learning Target Count Prediction in Electromagnetics Using Neural NetworksM. Sabbaghi, J. Zhang, and G. W. Hanson
A Physics-Assisted Deep Learning Microwave Imaging Framework for Real-Time Shape Reconstruction of Unknown TargetsÁ. Yago Ruiz, M. Cavagnaro, and L. Crocco
Enhanced Supervised Descent Learning Technique for Electromagnetic Inverse Scattering Problems by the Deep Convolutional Neural NetworksH. M. Yao, R. Guo, M. Li, L. Jiang, and M. Ng
A Tailored Semiphysics-Driven Artificial Neural Network for Electromagnetic Full-Wave InversionY. Chen, M. Zhong, Z. Guan, and F. Han
Learning-Based Inversion Method for Solving Electromagnetic Inverse Scattering With Mixed Boundary ConditionsR. Song, Y. Huang, X. Ye, K. Xu, C. Li, and X. Chen
Fast 3-D Electromagnetic Full-Wave Inversion of Dielectric Anisotropic Objects Based on ResU-Net Enhanced by Variational Born Iterative MethodJ. Fei, Y. Chen, M. Zhong, and F. Han
Learned Global Optimization for Inverse Scattering Problems: Matching Global Search With Computational EfficiencyM. Salucci, L. Poli, P. Rocca, and A. Massa
Dielectric Breast Phantoms by Generative Adversarial NetworkW. Shao and B. Zhou
Breast Imaging by Convolutional Neural Networks From Joint Microwave and Ultrasonic DataY. Qin, P. Ran, T. Rodet, and D. Lesselier
A Hybrid Neural Network Electromagnetic Inversion Scheme (HNNEMIS) for Super-Resolution 3-D Microwave Human Brain ImagingL.-Y. Xiao, R. Hong, L.-Y. Zhao, H.-J. Hu, and Q. H. Liu
Learning Approach to FMCW Radar Target Classification With Feature Extraction From Wave PhysicsK. Tan, T. Yin, H. Ruan, S. Balon, and X. Chen
Artificial Intelligence-Based Low-Terahertz Imaging for Archaeological Shards’ ClassificationF. Zidane, V. L. Coli, J. Lanteri, J. Marot, L. Brochier, D. Binder, and C. Migliaccio
DMRF-UNet: A Two-Stage Deep Learning Scheme for GPR Data Inversion Under Heterogeneous Soil ConditionsQ. Dai, Y. H. Lee, H.-H. Sun, G. Ow, M. L. M. Yusof, and A. C. Yucel
Deep Complex Convolutional Neural Networks for Subwavelength Microstructure ImagingT.-F. Wei, X.-H. Wang, and C.-H. Qu
Deep-Learning-Enabled Microwave-Induced Thermoacoustic Tomography Based on Sparse Data for Breast Cancer DetectionJ. Zhang, C. Li, W. Jiang, Z. Wang, L. Zhang, and X. Wang
Artificial Intelligence: New Frontiers in Real-Time Inverse Scattering and Electromagnetic ImagingM. Salucci, M. Arrebola, T. Shan, and M. Li