Large-Scale Analysis of the Head Proximity Effects on Antenna Performance Using Machine Learning Based Models
Owing to the variations in subject-specific body morphology and anatomy, the radiation performance of a wireless device in the presence of human body is different across subjects. To quantify the inter-subject variations, a large number of highly realistic human models are required. One recent appro...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9171235/ |
| _version_ | 1818857138553880576 |
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| author | Yinliang Diao Essam A. Rashed Akimasa Hirata |
| author_facet | Yinliang Diao Essam A. Rashed Akimasa Hirata |
| author_sort | Yinliang Diao |
| collection | DOAJ |
| description | Owing to the variations in subject-specific body morphology and anatomy, the radiation performance of a wireless device in the presence of human body is different across subjects. To quantify the inter-subject variations, a large number of highly realistic human models are required. One recent approach is the fast development of body models directly from medical images with machine learning. In this study, a total of eighteen anatomical head models were developed using a fast machine learning approach and were then adopted for large-scale evaluation of the inter-subject variations in antenna performance. The antenna impedance, return loss (RL), total radiated power (TRP), directivity, radiation patterns, and specific absorption rate (SAR) were investigated. The results show rather large variations in impedance, RL, and SAR across subjects, while TRP, directivity, and radiation pattern are less likely to be affected by internal tissue distributions when compared with homogeneous models. |
| first_indexed | 2024-12-19T08:35:38Z |
| format | Article |
| id | doaj.art-609e6e56e1384d4d90db875924d16045 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T08:35:38Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-609e6e56e1384d4d90db875924d160452022-12-21T20:29:03ZengIEEEIEEE Access2169-35362020-01-01815406015407110.1109/ACCESS.2020.30177739171235Large-Scale Analysis of the Head Proximity Effects on Antenna Performance Using Machine Learning Based ModelsYinliang Diao0https://orcid.org/0000-0002-6492-4515Essam A. Rashed1https://orcid.org/0000-0001-6571-9807Akimasa Hirata2https://orcid.org/0000-0001-8336-1140Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, JapanDepartment of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, JapanDepartment of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, JapanOwing to the variations in subject-specific body morphology and anatomy, the radiation performance of a wireless device in the presence of human body is different across subjects. To quantify the inter-subject variations, a large number of highly realistic human models are required. One recent approach is the fast development of body models directly from medical images with machine learning. In this study, a total of eighteen anatomical head models were developed using a fast machine learning approach and were then adopted for large-scale evaluation of the inter-subject variations in antenna performance. The antenna impedance, return loss (RL), total radiated power (TRP), directivity, radiation patterns, and specific absorption rate (SAR) were investigated. The results show rather large variations in impedance, RL, and SAR across subjects, while TRP, directivity, and radiation pattern are less likely to be affected by internal tissue distributions when compared with homogeneous models.https://ieeexplore.ieee.org/document/9171235/Antenna performanceelectromagnetic exposureFDTD methodmachine learning |
| spellingShingle | Yinliang Diao Essam A. Rashed Akimasa Hirata Large-Scale Analysis of the Head Proximity Effects on Antenna Performance Using Machine Learning Based Models IEEE Access Antenna performance electromagnetic exposure FDTD method machine learning |
| title | Large-Scale Analysis of the Head Proximity Effects on Antenna Performance Using Machine Learning Based Models |
| title_full | Large-Scale Analysis of the Head Proximity Effects on Antenna Performance Using Machine Learning Based Models |
| title_fullStr | Large-Scale Analysis of the Head Proximity Effects on Antenna Performance Using Machine Learning Based Models |
| title_full_unstemmed | Large-Scale Analysis of the Head Proximity Effects on Antenna Performance Using Machine Learning Based Models |
| title_short | Large-Scale Analysis of the Head Proximity Effects on Antenna Performance Using Machine Learning Based Models |
| title_sort | large scale analysis of the head proximity effects on antenna performance using machine learning based models |
| topic | Antenna performance electromagnetic exposure FDTD method machine learning |
| url | https://ieeexplore.ieee.org/document/9171235/ |
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