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Wireless Signal Propagation Prediction Based on Computer Vision Sensing Technology for Forestry Security Monitoring

In this paper, Computer Vision (CV) sensing technology based on Convolutional Neural Network (CNN) is introduced to process topographic maps for predicting wireless signal propagation models, which are applied in the field of forestry security monitoring. In this way, the terrain-related radio propa...

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Autores principales: He, Jialuan, Xing, Zirui, Xiang, Tianqi, Zhang, Xin, Zhou, Yinghai, Xi, Chuanyu, Lu, Hai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8433789/
https://www.ncbi.nlm.nih.gov/pubmed/34502579
http://dx.doi.org/10.3390/s21175688
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author He, Jialuan
Xing, Zirui
Xiang, Tianqi
Zhang, Xin
Zhou, Yinghai
Xi, Chuanyu
Lu, Hai
author_facet He, Jialuan
Xing, Zirui
Xiang, Tianqi
Zhang, Xin
Zhou, Yinghai
Xi, Chuanyu
Lu, Hai
author_sort He, Jialuan
collection PubMed
description In this paper, Computer Vision (CV) sensing technology based on Convolutional Neural Network (CNN) is introduced to process topographic maps for predicting wireless signal propagation models, which are applied in the field of forestry security monitoring. In this way, the terrain-related radio propagation characteristic including diffraction loss and shadow fading correlation distance can be predicted or extracted accurately and efficiently. Two data sets are generated for the two prediction tasks, respectively, and are used to train the CNN. To enhance the efficiency for the CNN to predict diffraction losses, multiple output values for different locations on the map are obtained in parallel by the CNN to greatly boost the calculation speed. The proposed scheme achieved a good performance in terms of prediction accuracy and efficiency. For the diffraction loss prediction task, 50% of the normalized prediction error was less than 0.518%, and 95% of the normalized prediction error was less than 8.238%. For the correlation distance extraction task, 50% of the normalized prediction error was less than 1.747%, and 95% of the normalized prediction error was less than 6.423%. Moreover, diffraction losses at 100 positions were predicted simultaneously in one run of CNN under the settings in this paper, for which the processing time of one map is about 6.28 ms, and the average processing time of one location point can be as low as 62.8 us. This paper shows that our proposed CV sensing technology is more efficient in processing geographic information in the target area. Combining a convolutional neural network to realize the close coupling of a prediction model and geographic information, it improves the efficiency and accuracy of prediction.
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spelling pubmed-84337892021-09-12 Wireless Signal Propagation Prediction Based on Computer Vision Sensing Technology for Forestry Security Monitoring He, Jialuan Xing, Zirui Xiang, Tianqi Zhang, Xin Zhou, Yinghai Xi, Chuanyu Lu, Hai Sensors (Basel) Article In this paper, Computer Vision (CV) sensing technology based on Convolutional Neural Network (CNN) is introduced to process topographic maps for predicting wireless signal propagation models, which are applied in the field of forestry security monitoring. In this way, the terrain-related radio propagation characteristic including diffraction loss and shadow fading correlation distance can be predicted or extracted accurately and efficiently. Two data sets are generated for the two prediction tasks, respectively, and are used to train the CNN. To enhance the efficiency for the CNN to predict diffraction losses, multiple output values for different locations on the map are obtained in parallel by the CNN to greatly boost the calculation speed. The proposed scheme achieved a good performance in terms of prediction accuracy and efficiency. For the diffraction loss prediction task, 50% of the normalized prediction error was less than 0.518%, and 95% of the normalized prediction error was less than 8.238%. For the correlation distance extraction task, 50% of the normalized prediction error was less than 1.747%, and 95% of the normalized prediction error was less than 6.423%. Moreover, diffraction losses at 100 positions were predicted simultaneously in one run of CNN under the settings in this paper, for which the processing time of one map is about 6.28 ms, and the average processing time of one location point can be as low as 62.8 us. This paper shows that our proposed CV sensing technology is more efficient in processing geographic information in the target area. Combining a convolutional neural network to realize the close coupling of a prediction model and geographic information, it improves the efficiency and accuracy of prediction. MDPI 2021-08-24 /pmc/articles/PMC8433789/ /pubmed/34502579 http://dx.doi.org/10.3390/s21175688 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
He, Jialuan
Xing, Zirui
Xiang, Tianqi
Zhang, Xin
Zhou, Yinghai
Xi, Chuanyu
Lu, Hai
Wireless Signal Propagation Prediction Based on Computer Vision Sensing Technology for Forestry Security Monitoring
title Wireless Signal Propagation Prediction Based on Computer Vision Sensing Technology for Forestry Security Monitoring
title_full Wireless Signal Propagation Prediction Based on Computer Vision Sensing Technology for Forestry Security Monitoring
title_fullStr Wireless Signal Propagation Prediction Based on Computer Vision Sensing Technology for Forestry Security Monitoring
title_full_unstemmed Wireless Signal Propagation Prediction Based on Computer Vision Sensing Technology for Forestry Security Monitoring
title_short Wireless Signal Propagation Prediction Based on Computer Vision Sensing Technology for Forestry Security Monitoring
title_sort wireless signal propagation prediction based on computer vision sensing technology for forestry security monitoring
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8433789/
https://www.ncbi.nlm.nih.gov/pubmed/34502579
http://dx.doi.org/10.3390/s21175688
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