eLibrary
  • Home
  • Sources
  • Search
  • About

Design of STM32 Air Quality Detection System

Zimo Su
Scientific Journal of Technology, (2025), Vol.7, No.1, pp.13-19
Published: January 21, 2025
DOI: 10.54691/3qk0q460
PDF: Download Full Text PDF
Abstract

With the acceleration of industrialization and the expansion of urbanization, environmental pollution issues have become increasingly severe, especially air pollution, which has become a global public health issue. Long-term exposure to high concentrations of fine particulate matter (such as MQ-2) and other pollutants can have a severe impact on human health, including respiratory and cardiovascular diseases. Therefore, monitoring environmental quality has become particularly important. Moreover, with the improvement of people's living standards and health awareness, more and more people are beginning to pay attention to the quality of the environment they are in. Whether it is at home, in the office, or outdoor activities, a tool that can monitor environmental quality in real-time is needed to help make healthier lifestyle decisions. Based on the above aspects, this system is designed for environmental monitoring.

Keywords: Temperature and Humidity; MQ-2; Atmospheric Pressure; OLED.
APA Citation: Zimo Su (2025). Design of STM32 Air Quality Detection System. Scientific Journal of Technology, 7(1), 13-19. https://doi.org/10.54691/3qk0q460

References

  1. Ren, S.; He, K.; Girshick, R.; Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. InProceedings of the International Conference on Neural Information Processing Systems, Montreal, QC, Canada, 7–12 December 2015; pp. 91–99.
  2. Zhu, X.; Hu, H.; Lin, S.; Dai, J. Deformable ConvNets v2: More deformable, better results. arXiv 2018, arXiv:1811.11168. Availableonline: https://arxiv.org/abs/1811.11168 (accessed on 8 July 2021).
  3. Cai, Y.; Li, H.; Yuan, G.; Niu, W.; Li, Y.; Tang, X.; Ren, B.; Wang, Y. YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design. arXiv 2020, arXiv:2009.05697. Available online: https://arxiv.org/abs/2009.05697.
  4. Xie, S.; Girshick, R.; Dollár, P.; Tu, Z.; He, K. Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 1492–1500.
  5. Ghiasi, G.; Lin, T.-Y.; Pang, R.; Le, Q.V. NAS-FPN: Learning scalable feature pyramid architecture for object detection. arXiv 2019,arXiv:1904.07392. Available online: https://arxiv.org/abs/ 1904. 07392 (accessed on 21 July 2021).
© 2026 eLibrary · Stand on the shoulders of giants