This work demonstrates multiple gases identification using a heated MEMS resonator and machine learning. The working principle of the gas sensor is based on the cooling/heating effect of the injected gases on the electrothermally actuated micro beam. As a case study, we demonstrate the concept using two analytes: Acetone and Helium. Machine learning algorithms and Principal Component Analysis are employed to classify each gas with its specific concentration level. The results show that a 100% accuracy rate is achieved for the identification of the different analytes with their concentration levels.
|Original language||English (US)|
|Title of host publication||2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - 2022|
|Event||2022 IEEE Sensors Conference, SENSORS 2022 - Dallas, United States|
Duration: Oct 30 2022 → Nov 2 2022
|Name||Proceedings of IEEE Sensors|
|Conference||2022 IEEE Sensors Conference, SENSORS 2022|
|Period||10/30/22 → 11/2/22|
Bibliographical noteFunding Information:
This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST).
© 2022 IEEE.
- Data processing
- Machine Learning
- Smart sensing
ASJC Scopus subject areas
- Electrical and Electronic Engineering