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Development and Testing of a 5G Multichannel Intelligent Seismograph Based on Raspberry Pi

A seismograph was designed based on Raspberry Pi. Although comprising 8 channels, the seismograph can be expanded to 16, 24, or 32 channels by using a USB interfacing with a microcontroller. In addition, by clustering more than one Raspberry Pi, the number of possible channels can be extended beyond...

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Autores principales: Idehen, Igbinigie Philip, You, Qingyu, Xu, Xiqiang, Li, Shaoqing, Zhang, Yan, Hu, Yaoxing, Wang, Yuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185420/
https://www.ncbi.nlm.nih.gov/pubmed/35684810
http://dx.doi.org/10.3390/s22114193
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author Idehen, Igbinigie Philip
You, Qingyu
Xu, Xiqiang
Li, Shaoqing
Zhang, Yan
Hu, Yaoxing
Wang, Yuan
author_facet Idehen, Igbinigie Philip
You, Qingyu
Xu, Xiqiang
Li, Shaoqing
Zhang, Yan
Hu, Yaoxing
Wang, Yuan
author_sort Idehen, Igbinigie Philip
collection PubMed
description A seismograph was designed based on Raspberry Pi. Although comprising 8 channels, the seismograph can be expanded to 16, 24, or 32 channels by using a USB interfacing with a microcontroller. In addition, by clustering more than one Raspberry Pi, the number of possible channels can be extended beyond 32. In this study, we also explored the computational intelligence of Raspberry Pi for running real-time systems and multithreaded algorithms to process raw seismic data. Also integrated into the seismograph is a Huawei MH5000-31 5G module, which provided high-speed internet real-time operations. Other hardware peripherals included a 24 bit ADS1251 analog-to-digital converter (ADC) and a STM32F407 microcontroller. Real-time data were acquired in the field for ambient noise tomography. An analysis tool called spatial autocorrelation (SPAC) was used to analyze the data, followed by inversion, which revealed the subsurface velocity of the site location. The proposed seismograph is prospective for small, medium, or commercial data acquisition. In accordance with the processing power and stability of Raspberry Pi, which were confirmed in this study, the proposed seismograph is also recommended as a template for developing high-performance computing applications, such as artificial intelligence (AI) in seismology and other related disciplines.
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spelling pubmed-91854202022-06-11 Development and Testing of a 5G Multichannel Intelligent Seismograph Based on Raspberry Pi Idehen, Igbinigie Philip You, Qingyu Xu, Xiqiang Li, Shaoqing Zhang, Yan Hu, Yaoxing Wang, Yuan Sensors (Basel) Article A seismograph was designed based on Raspberry Pi. Although comprising 8 channels, the seismograph can be expanded to 16, 24, or 32 channels by using a USB interfacing with a microcontroller. In addition, by clustering more than one Raspberry Pi, the number of possible channels can be extended beyond 32. In this study, we also explored the computational intelligence of Raspberry Pi for running real-time systems and multithreaded algorithms to process raw seismic data. Also integrated into the seismograph is a Huawei MH5000-31 5G module, which provided high-speed internet real-time operations. Other hardware peripherals included a 24 bit ADS1251 analog-to-digital converter (ADC) and a STM32F407 microcontroller. Real-time data were acquired in the field for ambient noise tomography. An analysis tool called spatial autocorrelation (SPAC) was used to analyze the data, followed by inversion, which revealed the subsurface velocity of the site location. The proposed seismograph is prospective for small, medium, or commercial data acquisition. In accordance with the processing power and stability of Raspberry Pi, which were confirmed in this study, the proposed seismograph is also recommended as a template for developing high-performance computing applications, such as artificial intelligence (AI) in seismology and other related disciplines. MDPI 2022-05-31 /pmc/articles/PMC9185420/ /pubmed/35684810 http://dx.doi.org/10.3390/s22114193 Text en © 2022 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
Idehen, Igbinigie Philip
You, Qingyu
Xu, Xiqiang
Li, Shaoqing
Zhang, Yan
Hu, Yaoxing
Wang, Yuan
Development and Testing of a 5G Multichannel Intelligent Seismograph Based on Raspberry Pi
title Development and Testing of a 5G Multichannel Intelligent Seismograph Based on Raspberry Pi
title_full Development and Testing of a 5G Multichannel Intelligent Seismograph Based on Raspberry Pi
title_fullStr Development and Testing of a 5G Multichannel Intelligent Seismograph Based on Raspberry Pi
title_full_unstemmed Development and Testing of a 5G Multichannel Intelligent Seismograph Based on Raspberry Pi
title_short Development and Testing of a 5G Multichannel Intelligent Seismograph Based on Raspberry Pi
title_sort development and testing of a 5g multichannel intelligent seismograph based on raspberry pi
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185420/
https://www.ncbi.nlm.nih.gov/pubmed/35684810
http://dx.doi.org/10.3390/s22114193
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