淡江大學機構典藏:Item 987654321/124891
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    题名: An Enhanced Deep Learning Scheme for Electromagnetic Imaging of Uniaxial Objects
    作者: Chiu, Chien-ching
    关键词: Image reconstruction;Dielectric constant;Imaging;Electromagnetics;Dielectrics;Microwave theory and techniques;Microwave imaging;Deep learning;inverse scattering problem (ISP);modified contrast scheme (MCS);transverse electric (TE);U-Net
    日期: 2023-12-22
    上传时间: 2024-01-08 12:05:39 (UTC+8)
    出版者: IEEE
    摘要: This article utilizes artificial intelligence (AI) combined with a modified contrast scheme (MCS) technique to reconstruct microwave imaging of uniaxial objects. The 2-D backscattering problem of uniaxial objects is exposed to the transverse magnetic (TM) and transverse electric (TE) incident waves. The TE polarization problem is more severe than TM. The preliminary distribution of the dielectric constant is computed by using MCS and then compared with the dominant current scheme (DCS). MCS adds a local wave amplification coefficient to rectify the contrast and thus solves the inverse scattering problem (ISP) effectively. Moreover, U-Net is employed to reconstruct the distribution of the dielectric constants of the uniaxial objects. Different noises are added to compare the reconstruction results for MCS and DCS. We also verify the effectiveness of our proposed method by the measurement data provided by the Fresnel Institute. Numerical results show that the reconstruction is almost the same for small dielectric constants for both MCS and DCS. However, MCS overwhelms DCSs for dielectric constant reconstruction when the dielectric constant is large. MCS has successfully reconstructed uniaxial objects for higher dielectric constant distributions with better performance and high precision capability.
    關聯: IEEE Transactions on Microwave Theory and Techniques (Early Access),　p.1-15
    DOI: 10.1109/TMTT.2023.3337826
    显示于类别:[電機工程學系暨研究所] 期刊論文

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