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Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part III

Research Article

Design of Iot Data Acquisition System Based on Neural Network Combined with STM32 Microcontroller

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-50577-5_4,
        author={Xinyue Chen},
        title={Design of Iot Data Acquisition System Based on Neural Network Combined with STM32 Microcontroller},
        proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part III},
        proceedings_a={ICMTEL PART 3},
        year={2024},
        month={2},
        keywords={Neural Network STM32 Microcontroller Arm Chip Internet of Things Data Acquisition System},
        doi={10.1007/978-3-031-50577-5_4}
    }
    
  • Xinyue Chen
    Year: 2024
    Design of Iot Data Acquisition System Based on Neural Network Combined with STM32 Microcontroller
    ICMTEL PART 3
    Springer
    DOI: 10.1007/978-3-031-50577-5_4
Xinyue Chen1,*
  • 1: The Hong Kong University of Science and Technology
*Contact email: cxyyy689y@yeah.net

Abstract

Aiming at the low efficiency and high power consumption of data acquisition in the Internet of Things, a data acquisition system based on the combination of neural network and STM32 microcontroller is designed. In the control module, according to the system functional design and performance requirements, the STM32F103C8T6 minimum system module is used as the control core of the system. The data acquisition terminal adopts the baseplate + core board structure, and is composed of high-speed data acquisition module, AT91SAM9X25 platform and various expansion function modules. Eight types of environmental data sensor modules are used to build sensor modules. The k-means convolution neural network is used to realize the data classification of the Internet of Things, and the design of the data classification module is completed. The performance of the system is tested. The test results show that the system can realize the real-time collection of indoor environmental data, the communication rate is higher than 80 mbps, and the power consumption of the data acquisition terminal is low.

Keywords
Neural Network STM32 Microcontroller Arm Chip Internet of Things Data Acquisition System
Published
2024-02-21
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-50577-5_4
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