Metallic Pattern Prediction for Surface Wave Antennas Using Bidirectional Gated Recurrent Unit Neural Network

Jiashu Yang; Kin Fai Tong

doi:10.1109/APWC52648.2021.9539634

Metallic Pattern Prediction for Surface Wave Antennas Using Bidirectional Gated Recurrent Unit Neural Network

Jiashu Yang
, Kin Fai Tong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This work presents a surface wave antenna metallic pattern prediction from electric field in near-field by applying Bidirectional Gated Recurrent Unit neural network prediction model. The metallic pattern of the proposed antenna has been predicted by using Bi-GRU neural network model with prediction accuracy 100% at 34.5GHz. Different uniform mark-space-ratios (MSR) of the metallic pattern do not affect the metallic pattern prediction accuracy.

Original language	English
Title of host publication	2021 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, APWC 2021
Pages	82-86
Number of pages	5
ISBN (Electronic)	9781665413886
DOIs	https://doi.org/10.1109/APWC52648.2021.9539634
Publication status	Published - 9 Aug 2021
Externally published	Yes
Event	10th IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, APWC 2021 - Honolulu, United States Duration: 9 Aug 2021 → 13 Aug 2021

Publication series

Name	2021 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, APWC 2021

Conference

Conference	10th IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, APWC 2021
Country/Territory	United States
City	Honolulu
Period	9/08/21 → 13/08/21

Keywords

bidirectional gated recurrent unit (Bi-GRU)
electric field (E-field) prediction
holographic antennas
recurrent neural network (RNN)
Surface wave antennas

Access to Document

10.1109/APWC52648.2021.9539634

Cite this

Yang, J., & Tong, K. F. (2021). Metallic Pattern Prediction for Surface Wave Antennas Using Bidirectional Gated Recurrent Unit Neural Network. In 2021 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, APWC 2021 (pp. 82-86). (2021 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, APWC 2021). https://doi.org/10.1109/APWC52648.2021.9539634

@inproceedings{2e7d878e44a2478caacbb87e9b0ac956,

title = "Metallic Pattern Prediction for Surface Wave Antennas Using Bidirectional Gated Recurrent Unit Neural Network",

abstract = "This work presents a surface wave antenna metallic pattern prediction from electric field in near-field by applying Bidirectional Gated Recurrent Unit neural network prediction model. The metallic pattern of the proposed antenna has been predicted by using Bi-GRU neural network model with prediction accuracy 100\% at 34.5GHz. Different uniform mark-space-ratios (MSR) of the metallic pattern do not affect the metallic pattern prediction accuracy.",

keywords = "bidirectional gated recurrent unit (Bi-GRU), electric field (E-field) prediction, holographic antennas, recurrent neural network (RNN), Surface wave antennas",

author = "Jiashu Yang and Tong, \{Kin Fai\}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 10th IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, APWC 2021 ; Conference date: 09-08-2021 Through 13-08-2021",

year = "2021",

month = aug,

day = "9",

doi = "10.1109/APWC52648.2021.9539634",

language = "English",

series = "2021 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, APWC 2021",

pages = "82--86",

booktitle = "2021 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, APWC 2021",

}

Yang, J & Tong, KF 2021, Metallic Pattern Prediction for Surface Wave Antennas Using Bidirectional Gated Recurrent Unit Neural Network. in 2021 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, APWC 2021. 2021 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, APWC 2021, pp. 82-86, 10th IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, APWC 2021, Honolulu, United States, 9/08/21. https://doi.org/10.1109/APWC52648.2021.9539634

Metallic Pattern Prediction for Surface Wave Antennas Using Bidirectional Gated Recurrent Unit Neural Network. / Yang, Jiashu; Tong, Kin Fai.
2021 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, APWC 2021. 2021. p. 82-86 (2021 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, APWC 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Metallic Pattern Prediction for Surface Wave Antennas Using Bidirectional Gated Recurrent Unit Neural Network

AU - Yang, Jiashu

AU - Tong, Kin Fai

PY - 2021/8/9

Y1 - 2021/8/9

N2 - This work presents a surface wave antenna metallic pattern prediction from electric field in near-field by applying Bidirectional Gated Recurrent Unit neural network prediction model. The metallic pattern of the proposed antenna has been predicted by using Bi-GRU neural network model with prediction accuracy 100% at 34.5GHz. Different uniform mark-space-ratios (MSR) of the metallic pattern do not affect the metallic pattern prediction accuracy.

AB - This work presents a surface wave antenna metallic pattern prediction from electric field in near-field by applying Bidirectional Gated Recurrent Unit neural network prediction model. The metallic pattern of the proposed antenna has been predicted by using Bi-GRU neural network model with prediction accuracy 100% at 34.5GHz. Different uniform mark-space-ratios (MSR) of the metallic pattern do not affect the metallic pattern prediction accuracy.

KW - bidirectional gated recurrent unit (Bi-GRU)

KW - electric field (E-field) prediction

KW - holographic antennas

KW - recurrent neural network (RNN)

KW - Surface wave antennas

UR - https://www.scopus.com/pages/publications/85116292934

U2 - 10.1109/APWC52648.2021.9539634

DO - 10.1109/APWC52648.2021.9539634

M3 - Conference contribution

AN - SCOPUS:85116292934

T3 - 2021 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, APWC 2021

SP - 82

EP - 86

BT - 2021 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, APWC 2021

T2 - 10th IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, APWC 2021

Y2 - 9 August 2021 through 13 August 2021

ER -

Yang J, Tong KF. Metallic Pattern Prediction for Surface Wave Antennas Using Bidirectional Gated Recurrent Unit Neural Network. In 2021 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, APWC 2021. 2021. p. 82-86. (2021 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, APWC 2021). doi: 10.1109/APWC52648.2021.9539634

Metallic Pattern Prediction for Surface Wave Antennas Using Bidirectional Gated Recurrent Unit Neural Network

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