A novel 3D measurement of RFID multi‐tag network based on MWCNN and ELM

Abstract In the field of RFID, the reading performance of tags is an important performance indicator for measuring tag. Related studies have shown that the tags’ geometrical distribution has an important influence on the tags’ reading performance. In order to optimize the tags’ geometrical distribut...

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Autores principales:	Xiaolei Yu, Xiao Zhuang, Zhenlu Liu, Zhimin Zhao, Lin Li, Wenjie Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2022
Materias:	3D position measurement ELM MWCNN RFID tag group structure prediction Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/2229f6367b174cacbd5713a554c05909
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spelling	oai:doaj.org-article:2229f6367b174cacbd5713a554c059092021-12-01T10:55:36ZA novel 3D measurement of RFID multi‐tag network based on MWCNN and ELM1751-88301751-882210.1049/smt2.12078https://doaj.org/article/2229f6367b174cacbd5713a554c059092022-01-01T00:00:00Zhttps://doi.org/10.1049/smt2.12078https://doaj.org/toc/1751-8822https://doaj.org/toc/1751-8830Abstract In the field of RFID, the reading performance of tags is an important performance indicator for measuring tag. Related studies have shown that the tags’ geometrical distribution has an important influence on the tags’ reading performance. In order to optimize the tags’ geometrical distribution and improve the tags’ reading performance, this paper proposes a tag distribution optimization method based on multi‐level wavelet‐CNN (MWCNN) and extreme learning machine (ELM). First, this paper designs a tag distribution optimization system based on stereo‐vision. Second, the stereo‐cameras are used to capture the images of the tags. Aiming at the degradation phenomenon in the acquired images, MWCNN is used to recover the degraded tag images. On the basis of the image restoration, the template matching method is used to obtain the 3D coordinates of the tags. Then, ELM is used to model and predict the nonlinear relationship between 3D coordinates of the tags and the corresponding reading distance. The results show that the average prediction relative error is 0.56% and the time cost is 2.0 s. The average prediction relative error of ELM is smaller than GA‐BP and PSO‐BP. The time cost of ELM is smaller than the wavelet neural network.Xiaolei YuXiao ZhuangZhenlu LiuZhimin ZhaoLin LiWenjie ZhangWileyarticle3D position measurementELMMWCNNRFID tag groupstructure predictionElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIET Science, Measurement & Technology, Vol 16, Iss 1, Pp 15-27 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	3D position measurement ELM MWCNN RFID tag group structure prediction Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	3D position measurement ELM MWCNN RFID tag group structure prediction Electrical engineering. Electronics. Nuclear engineering TK1-9971 Xiaolei Yu Xiao Zhuang Zhenlu Liu Zhimin Zhao Lin Li Wenjie Zhang A novel 3D measurement of RFID multi‐tag network based on MWCNN and ELM
description	Abstract In the field of RFID, the reading performance of tags is an important performance indicator for measuring tag. Related studies have shown that the tags’ geometrical distribution has an important influence on the tags’ reading performance. In order to optimize the tags’ geometrical distribution and improve the tags’ reading performance, this paper proposes a tag distribution optimization method based on multi‐level wavelet‐CNN (MWCNN) and extreme learning machine (ELM). First, this paper designs a tag distribution optimization system based on stereo‐vision. Second, the stereo‐cameras are used to capture the images of the tags. Aiming at the degradation phenomenon in the acquired images, MWCNN is used to recover the degraded tag images. On the basis of the image restoration, the template matching method is used to obtain the 3D coordinates of the tags. Then, ELM is used to model and predict the nonlinear relationship between 3D coordinates of the tags and the corresponding reading distance. The results show that the average prediction relative error is 0.56% and the time cost is 2.0 s. The average prediction relative error of ELM is smaller than GA‐BP and PSO‐BP. The time cost of ELM is smaller than the wavelet neural network.
format	article
author	Xiaolei Yu Xiao Zhuang Zhenlu Liu Zhimin Zhao Lin Li Wenjie Zhang
author_facet	Xiaolei Yu Xiao Zhuang Zhenlu Liu Zhimin Zhao Lin Li Wenjie Zhang
author_sort	Xiaolei Yu
title	A novel 3D measurement of RFID multi‐tag network based on MWCNN and ELM
title_short	A novel 3D measurement of RFID multi‐tag network based on MWCNN and ELM
title_full	A novel 3D measurement of RFID multi‐tag network based on MWCNN and ELM
title_fullStr	A novel 3D measurement of RFID multi‐tag network based on MWCNN and ELM
title_full_unstemmed	A novel 3D measurement of RFID multi‐tag network based on MWCNN and ELM
title_sort	novel 3d measurement of rfid multi‐tag network based on mwcnn and elm
publisher	Wiley
publishDate	2022
url	https://doaj.org/article/2229f6367b174cacbd5713a554c05909
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A novel 3D measurement of RFID multi‐tag network based on MWCNN and ELM

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