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    <title>Mingjie Pang | Hai Lin</title>
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    <description>Mingjie Pang</description>
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      <title>A GPU-based Radio Wave Propagation Prediction  with Progressive Processing on Point Cloud</title>
      <link>/home/lin/project/mingjiepang-agp/</link>
      <pubDate>Thu, 15 Sep 2022 16:54:40 +0800</pubDate>
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      <description>&lt;p&gt;Point cloud data (PCD) can record a high-resolution model of the environment much more efficiently than the conventional geometrical mesh. This feature makes PCD suitable for the radio channel prediction of a new environment, especially for the millimeter-wave (mmWave). Since the multiple propagations and visibility computation using PCD are time consuming, progressive processing on PCD is introduced to make the prediction compatible with GPU-based frameworks such as OptiX and CUDA. Compared with the surface reconstruction from PCD, progressive data from the processing reserves more domain features such as diffraction wedge and the label of planar or nonplanar points. The numerical result of an outdoor environment shows that the proposed method has a close agreement with measurement, as well as two impressive speedups, 49.8 for the ray tracing and field calculation and 18.8 for the total prediction, compared with the original method using PCD.&lt;/p&gt;
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      <title>Deep Learning with Attention Mechanism for Electromagnetic Inverse Scattering</title>
      <link>/home/lin/project/lizhenyang-dlw/</link>
      <pubDate>Fri, 15 Jul 2022 17:04:51 +0800</pubDate>
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      <description>&lt;p&gt;The inverse scattering problems (ISPs) aim for reconstructing the unknown targets from the measured scattered fields. Due to its highly nonlinear and ill-posed properties, solving ISPs is a challenging task. In this paper, a new deep learning technique is investigated to improve the solution of electromagnetic inverse scattering problems (ISPs). The basic idea of this technique is to introduce the attention mechanism to U-Net. By doing this, the deep learning model can automatically learn to focus on target areas of the measured scattered field matrix. In this way, the proposed technique can offer higher accuracy and faster convergence compared with the traditional deep learning networks. Numerical results validate the efficacy of the proposed technique.&lt;/p&gt;
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      <title>Enhanced solution to the surface-volume-surface EFIE for arbitrary metal-dielectric composite objects</title>
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      <pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate>
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      <title>A GPU-based radio wave propagation prediction with progressive processing on point cloud</title>
      <link>/home/lin/publication/pang-2021-gpu/</link>
      <pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate>
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      <title>Accuracy improvement of the algebraic fast methods for the volume-surface integral equation</title>
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      <pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate>
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      <title>ELF near-field propagation of a vertical electric dipole due to lightning discharges</title>
      <link>/home/lin/publication/xu-2021-elf/</link>
      <pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate>
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      <title>Iterative MLFMA-MADBT technique for analysis of antenna mounted on large platforms</title>
      <link>/home/lin/publication/pang-2020-iterative/</link>
      <pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate>
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      <title>Acceleration of shadowing detection with octree and improved specular model for indoor propagation using point cloud data</title>
      <link>/home/lin/publication/pang-2018-acceleration/</link>
      <pubDate>Mon, 01 Jan 2018 00:00:00 +0000</pubDate>
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      <title>Versatile solver of nonconformal volume integral equation based on SWG basis function</title>
      <link>/home/lin/publication/luo-2018-versatile/</link>
      <pubDate>Mon, 01 Jan 2018 00:00:00 +0000</pubDate>
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