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Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
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MineVisual: A Battery-Free Visual Perception Scheme in Coal Mine.

Ming Li1, Zhongxu Bao1, Shuting Li1

  • 1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
Summary
This summary is machine-generated.

MineVisual offers battery-free visual sensing for underground coal mines, using a lightweight deep neural network and energy-aware pruning for efficient, accurate safety monitoring in harsh environments.

Keywords:
battery-free visual sensingdeep learning inferenceenergy harvestingenergy-aware adaptive pruningunderground coal mine safety

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Area of Science:

  • Engineering
  • Computer Science
  • Mining Engineering

Background:

  • Underground coal mines require robust safety monitoring systems.
  • Traditional methods are limited by energy supply and maintenance needs.
  • High computational demands and energy constraints challenge existing solutions.

Purpose of the Study:

  • To propose MineVisual, a battery-free visual sensing scheme for underground coal mines.
  • To address energy constraints and computational demands in resource-limited environments.
  • To enable intelligent safety monitoring in complex underground settings.

Main Methods:

  • Developed an optimized lightweight deep neural network with depthwise separable convolutions.
  • Introduced an energy-aware dynamic pruning network (EADP-Net) for sustained accuracy.
  • Integrated supercapacitor buffering and voltage regulation for stable operation.

Main Results:

  • MineVisual achieved high accuracy (91.5% Top-1) on mine-specific tasks.
  • Significantly enhanced energy efficiency, reducing inference energy to 6.89 mJ.
  • Demonstrated robustness under varying wind speeds and fluctuating power conditions.

Conclusions:

  • MineVisual provides an effective technical pathway for intelligent safety monitoring.
  • Proved the feasibility of battery-free deep learning inference in extreme environments like coal mines.
  • Enables sustained performance and energy efficiency in challenging underground settings.