@inproceedings{8ea1e1276a6042fea2263585a31b58c4,
title = "Selective gas detection using conductivity-based MEMS resonator and machine learning",
abstract = "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.",
keywords = "Classification, Data processing, Machine Learning, Smart sensing",
author = "Lenz, {Wagner B.} and Usman Yaqoob and Rocha, {Rodrigo T.} and Younis, {Mohammad I.}",
note = "Funding Information: This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST). Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Sensors Conference, SENSORS 2022 ; Conference date: 30-10-2022 Through 02-11-2022",
year = "2022",
doi = "10.1109/SENSORS52175.2022.9967178",
language = "English (US)",
series = "Proceedings of IEEE Sensors",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings",
address = "United States",
}