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Full-Text Articles in Engineering
Two-Step Enhanced Deep Learning Approach For Electromagnetic Inverse Scattering Problems, He Ming Yao, Wei E.I. Sha, Lijun Jiang
Two-Step Enhanced Deep Learning Approach For Electromagnetic Inverse Scattering Problems, He Ming Yao, Wei E.I. Sha, Lijun Jiang
Electrical and Computer Engineering Faculty Research & Creative Works
In this letter, a new deep learning (DL) approach is proposed to solve the electromagnetic inverse scattering (EMIS) problems. The conventional methods for solving inverse problems face various challenges including strong ill-conditions, high contrast, expensive computation cost, and unavoidable intrinsic nonlinearity. To overcome these issues, we propose a new two-step machine learning based approach. In the first step, a complex-valued deep convolutional neural network is employed to retrieve initial contrasts (permittivity's) of dielectric scatterers from measured scattering data. In the second step, the previously obtained contrasts are input into a complex-valued deep residual convolutional neural network to refine the reconstruction …
Source Reconstruction Method Based On Machine Learning Algorithms, He Ming Yao, Lijun Jiang, Wei E.I. Sha
Source Reconstruction Method Based On Machine Learning Algorithms, He Ming Yao, Lijun Jiang, Wei E.I. Sha
Electrical and Computer Engineering Faculty Research & Creative Works
This paper proposes a new source reconstruction method (SRM) based on deep learning. The conventional SRM usually requires oversampled measurements data to ensure higher accuracy. Thus, conventional SRM numerical system is usually highly singular. A deep convolutional neural network (ConvNet) is proposed to reconstruct the equivalent sources of the target to overcome difficulty. The deep ConvNet allows us to employ less data samples. Besides, the ill-conditioned numerical system can be effectively avoided. Numerical examples are presented to demonstrate the feasibility and accuracy of the proposed method. Its performance is also compared with the traditional neural network and interpolation method. Moreover, …
Applying Deep Learning Approach To The Far-Field Subwavelength Imaging Based On Near-Field Resonant Metalens At Microwave Frequencies, He Ming Yao, Min Li, Lijun Jiang
Applying Deep Learning Approach To The Far-Field Subwavelength Imaging Based On Near-Field Resonant Metalens At Microwave Frequencies, He Ming Yao, Min Li, Lijun Jiang
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, we utilize the deep learning approach for the subwavelength imaging in far-field, which is realized by the near-field resonant metalens at microwave frequencies. The resonating metalens consisting of split-ring resonators (SRRs) are equipped with the strong magnetic coupling ability and can convert evanescent waves into propagating waves using the localized resonant modes. The propagating waves in the far-field are utilized as the input of a trained deep convolutional neural network (CNN) to realize the imaging. The training data for establishing the deep CNN are obtained by the EM simulation tool. Besides, the white Gaussian noise is added …