Evidence map›Paper›PMID 35684810›Full record

ArticleSensors (Basel, Switzerland)2022

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

Igbinigie Philip Idehen, Qingyu You, Xiqiang Xu, Shaoqing Li, Yan Zhang, Yaoxing Hu, Yuan Wang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Igbinigie Philip IdehenKey Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China.
Qingyu YouKey Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China.
Xiqiang XuKey Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China.
Shaoqing LiKey Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China.
Yan ZhangKey Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China.
Yaoxing HuKey Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China.
Yuan WangKey Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

5G moduleambient noise tomographyIoTmachine learningmultichannel seismographRaspberry Pireal-time data transfer

Identifiers

PMID35684810
PMCPMC9185420

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.