Practical Value and Optimization Directions of Sensor Intelligent Perception Scheme on STM32 Platform

Yuxiang Li

doi:10.62051/ijcsit.v8n6.07

Authors

Yuxiang Li College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China

DOI:

https://doi.org/10.62051/ijcsit.v8n6.07

Keywords:

STM32, Intelligent perception, Smart sensors, Sensor fusion, Embedded systems, Edge intelligence

Abstract

Analyze the practical value and optimisation directions of sensor intelligent perception schemes based on the STM32 platform in this paper. STM32 is not only a low-cost control chip but also suitable for implementing integrated sensing and local processing, as well as communication and lightweight intelligent decision-making in embedded systems, as this paper explores. Based on smart sensor theory, Internet of Things architecture, multisensor fusion studies and recent STM32 artificial intelligence tools, this essay explores the prospects of STM32-based schemes for real-time response, deployment flexibility, system integration and edge-side data utilization. The following are the general optimisation directions of such schemes: sensor selection, interface coordination, fusion algorithms, energy management, memory optimisation, edge AI deployment and reliability enhancement. Therefore, the value of STM32-based intelligent perception lies not only in hardware cost but also in the combined design of a sensing architecture, embedded computing resources and application-specific optimisation strategies.

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References

[1] Meijer, G. C. M. (Ed.). (2008). Smart sensor systems. John Wiley & Sons.

[2] Yurish, S. Y. (2008). Smart sensors systems design. IEEE Sensors Council Tutorial.

[3] Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645 1660.

[4] Al Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys & Tutorials, 17(4), 2347 2376.

[5] Mendes Jr., J. J. A., Vieira, M. E. M., Pires, M. B., & Stevan Jr., S. L. (2016). Sensor fusion and smart sensor in sports and biomedical applications. Sensors, 16(10), 1569.

[6] Kong, L., Peng, X., Chen, Y., Wang, P., & Xu, M. (2020). Multi sensor measurement and data fusion technology for manufacturing process monitoring: A literature review. International Journal of Extreme Manufacturing, 2(2), 022001.

[7] Sakr, F., Bellotti, F., Berta, R., & De Gloria, A. (2020). Machine learning on mainstream microcontrollers. Sensors, 20(9), 2638.

[8] Merenda, M., Porcaro, C., & Iero, D. (2020). Edge machine learning for AI enabled IoT devices: A review. Sensors, 20(9), 2533.

[9] Tsanousa, A., Bektsis, E., Kyriakopoulos, C., Gómez González, A. G., Leturiondo, U., Gialampoukidis, I., Karakostas, A., Vrochidis, S., & Kompatsiaris, I. (2022). A review of multisensor data fusion solutions in smart manufacturing: Systems and trends. Sensors, 22(5), 1734.

[10] Zagan, I., & Găitan, V. G. (2024). BIoT smart switch embedded system based on STM32 and Modbus RTU—Concept, theory of operation and implementation. Buildings, 14(10), 3076.