期刊論文
| 學年 | 113 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2024-10-13 |
| 作品名稱 | A Novel Electromagnetic Sensing Generative Adversarial Network for Uniaxial Objects |
| 作品名稱（其他語言） | |
| 著者 | Chien-Ching Chiu; Po-Hsiang Chen; Hao Jiang; Bo-Yu Shi |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Electronics (13)20, 4027 |
| 摘要 | Electromagnetic imaging achieves enhanced resolution by leveraging the advanced sensing and data analysis capabilities of Internet of Things (IoT) systems. This paper introduces a novel learning approach for generative adversarial networks (GANs) to tackle significant challenges in electromagnetic sensing. The proposed method involves deploying additional transmitters and receivers to irradiate TM (transverse magnetic) and TE (transverse electric) polarization waves around uniaxial objects to capture the scattered field in free space. Subsequently, scattered field generative adversarial networks (SF-GANs) are utilized to simulate and learn the characteristics of Maxwell’s equations. Numerical simulations and experimental results demonstrate the superior performance of the SF-GANs compared to backpropagation generative adversarial networks (BP-GANs). Furthermore, it is worth noting that our method is capable of reconstructing high-dielectric-constant objects. |
| 關鍵字 | scattered field learning; generative adversarial networks; uniaxial objects; electromagnetic sensing; electromagnetic imaging |
| 語言 | en_US |
| ISSN | 2079-9292 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 是 |
| 國別 | CHE |
| 公開徵稿 | |
| 出版型式 | ,電子版,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/127509 ) |