TOPIC

• Titile: AI-Enhanced Low-Power Gas Sensor System for Addressing Measurement Challenges of Dynamic Range in Portable Applications

INFORMATION

• Producer: Soo-Bin Yang
• Published: IEEE Sensors Journal
• Paper: Link

IMAGE AND VIDEO

OVERVIEW

• This work presents an AI-enhanced gas sensor system optimized for portable applications, addressing the challenges of low power consumption and wide dynamic range. By integrating a one-dimensional convolutional neural network (1D-CNN) for gas concentration prediction and a gas exposure detection algorithm, the proposed system enhances prediction accuracy, robustness, and energy efficiency. The system detects gas exposure in real-time, reducing unnecessary computations and improving overall efficiency. Experimental results demonstrate a quantification average error of only 2.56 % and a power consumption reduction of 73.66 % compared to conventional systems. These improvements make the proposed sensor system suitable for battery-operated portable devices, offering a practical solution for accurate real-time gas monitoring under varying environmental conditions. The proposed approach thus meets the needs of portable sensing technologies, combining high performance with low power requirements for effective and reliable gas detection.