An Energy-Efficient Multi-Mode Multi-Channel Gas-Sensor System with Learning-Based Optimization and Self-Calibration Schemes
Cited 0 times inCited 0 times in
- An Energy-Efficient Multi-Mode Multi-Channel Gas-Sensor System with Learning-Based Optimization and Self-Calibration Schemes
- Park, Kyeonghwan; Choi, Subin; Chae, Hee Young; Park, Chan Sam; Lee, Seungwook; Lim, Yeongjin; Shin, Heungjoo; Kim, Jae Joon
- Issue Date
- Institute of Electrical and Electronics Engineers
- IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, v.67, no.3, pp.2402 - 2410
- This paper presents an energy-efficient intelligent multi-sensor system for hazardous gases, whose performance can be adaptively optimized through a multi-mode structure and a learning-based pattern recognition algorithm. The multi-mode operation provides control capability on trade-off relationship of accuracy and power consumption. In-house micro-electro-mechanical (MEMS) devices with a suspended nanowire structure are manufactured to provide desired characteristics of small size, low power, and high sensitivity. The pattern recognition to combine the dimensionality reduction and the neural network is adopted to improve the selectivity of MEMS gas sensors. Moreover, potential deviations in sensing characteristics are calibrated through a proposed self-calibration zooming structure. Reconfigurable circuits for these key features are integrated into an adaptive readout integrated circuit (ROIC) which is fabricated in a 180-nm complementary metal-oxide semiconductor (CMOS) process. For its system-level verification, a wireless multi-channel gas-sensor system prototype is implemented and experimentally verified to achieve 2.6 times efficiency improvement.
- Appears in Collections:
- MNE_Journal Papers
- Files in This Item:
- There are no files associated with this item.
can give you direct access to the published full text of this article. (UNISTARs only)
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.