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Table of Content

Data Acquisition and Electromagnetic Interference Detection by Internet of Things

Submission Deadline: 20 December 2022 (closed)

Guest Editors

Dr. Yu Bai, California State University, USA
Dr. Lin Meng, Ritsumeikan University, Japan
Dr. Hao Wu, Beijing Normal University, China


The collection, transmission, and processing of field data have become an indispensable link in industrial control, production, and scientific research. The data acquisition system mainly completes the collection, A/D (analog to digital) conversion, and processing of data. Then, it sends the data to the computer for further processing and storage or feeds back the processing results to the equipment for related control operations. At present, the data are generally collected by various sensors, with the characteristics of wide spatial distribution and large amounts. Therefore, it is impossible to obtain data manually in some special environments with high risk and high strength. At this time, it is necessary to obtain temperature, humidity, pressure, and other data at the worksite through sensors and remotely transmit collected data through wireless networks for subsequent processing. The peculiarities of various components and sensors lead to various working performances in different engineering environments. In addition, there are various interferences, such as electromagnetic radiation in the environment where the system works, which will affect the accuracy and reliability of each acquisition parameter.


Data acquisition terminals are often disturbed when working, including noise interference caused by the module itself, interference caused by the layout and wiring of system modules, and some electromagnetic interference in the working environment. All interference will have a certain impact on data acquisition, transmission, and processing. The working environment of the data acquisition system is complex and vulnerable to interference in the external environment, which may cause the deviation of the collected data and unpredictable results. Furthermore, strong external interference can easily bring a destructive impact on the circuit. The working environment of the data processing system is relatively stable, but there are still noise problems. The interference mainly originates from inside the system, and the liquid crystal drive circuit is the main interference source, which causes plenty of interference to the USB data transmission, and even leads to its failure to work properly. Therefore, it is essential to optimize the interference suppression design of the liquid crystal drive circuit. Moreover, the system has a high operating frequency and many high-speed circuits, facing another challenge of the layout and wiring of the system. It is necessary to find and eliminate the problems existing in the system through simulation analysis and circuit and PCB (printed circuit board) design to improve the reliability and stability of the system.


The IoT (Internet of Things) is usually applied to the collection of analog signals and digital signals, and the current acquisition system is generally developed for a particular system. This special issue aims to collect the latest technical progress and application modes of data acquisition and electromagnetic interference detection based on IoT, to explore a general and reliable data acquisition system, and effectively detect and timely intervene in electromagnetic interference in the environment. This Special Issue will also collect the best paper from IIKI2021: http://bigdata.bnu.edu.cn/IIKI2021/.


Research topics include but are not limited to the following:

• IoT-based Remote Wireless Electromagnetic Valve Detection

• Building Electromagnetic Detection Based on Wireless Sensor Network

• Electromagnetic Compatibility of Wireless Terminal under IoT Environment

• Electromagnetic Interference Monitoring of Computer Room Based on IoT

• Traceable Data Collection and Modeling of the IoT

• IoT Agricultural Meteorological Data Acquisition Based on Microprocessor

• On-site IoT Data Acquisition and Transmission of High Voltage Transformers

• IoT Data Acquisition for Industrial Digital Factory

• Interference Monitoring System of Power Wireless Private Network Based on IoT

• Data Processing jointly with Video Surveillance, Authentication and Voice Alarm

• Hardware Design of Coordinator Node, Router Node and Terminal Node of IoT Detection System

Published Papers

  • Open Access


    Refined Sparse Representation Based Similar Category Image Retrieval

    Xin Wang, Zhilin Zhu, Zhen Hua
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 893-908, 2023, DOI:10.32604/cmes.2022.021287
    (This article belongs to this Special Issue: Data Acquisition and Electromagnetic Interference Detection by Internet of Things)
    Abstract Given one specific image, it would be quite significant if humanity could simply retrieve all those pictures that fall into a similar category of images. However, traditional methods are inclined to achieve high-quality retrieval by utilizing adequate learning instances, ignoring the extraction of the image’s essential information which leads to difficulty in the retrieval of similar category images just using one reference image. Aiming to solve this problem above, we proposed in this paper one refined sparse representation based similar category image retrieval model. On the one hand, saliency detection and multi-level decomposition could contribute to taking salient and spatial… More >

  • Open Access


    Modeling and Prediction of Inter-System Bias for GPS/BDS-2/BDS-3 Combined Precision Point Positioning

    Zejie Wang, Qianxin Wang, Sanxi Li
    CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 823-843, 2022, DOI:10.32604/cmes.2022.020106
    (This article belongs to this Special Issue: Data Acquisition and Electromagnetic Interference Detection by Internet of Things)
    Abstract The combination of Precision Point Positioning (PPP) with Multi-Global Navigation Satellite System (MultiGNSS), called MGPPP, can improve the positioning precision and shorten the convergence time more effectively than the combination of PPP with only the BeiDou Navigation Satellite System (BDS). However, the Inter-System Bias (ISB) measurement of Multi-GNSS, including the time system offset, the coordinate system difference, and the inter-system hardware delay bias, must be considered for Multi-GNSS data fusion processing. The detected ISB can be well modeled and predicted by using a quadratic model (QM), an autoregressive integrated moving average model (ARIMA), as well as the sliding window strategy… More >

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