Ngoc Viet Nguyen, Viet Chien Nguyen, Huy Minh Le, Van Hieu Nguyen
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引用次数: 0
Abstract
Accurate and miniaturized gas sensing has become increasingly essential for real-time environmental monitoring and industrial safety. This study proposes a computationally enhanced microelectronic gas sensing platform that combines a compact metal oxide semiconductor (MOS) sensor array with machine learning algorithms for selective multi-gas detection. The core of the system is the MICS4514 sensor, which integrates two miniaturized MOS sensing elements into a single package, enabling detection of carbon monoxide, nitrogen dioxide (NO₂), ammonia (NH₃), and hydrogen (H₂) across various concentration levels. Sensor output data were processed using several supervised machine learning models, including Decision Tree, Random Forest, Quadratic Discriminant Analysis (QDA), and Gradient Boosting. While QDA yielded the highest accuracy in initial classifications, data augmentation strategies significantly improved GB's performance, achieving 100% accuracy in gas discrimination. In addition, linear regression analysis was employed to estimate gas concentrations, demonstrating its feasibility for quantitative sensing. This integration of microscale sensor technology and data-driven computational modeling underscores the potential of embedded intelligence in low-power, cost-effective gas sensors. The approach presented here supports the development of scalable on-chip sensing solutions for smart electronics and Internet-of-Things (IoT)-enabled environmental surveillance systems.
期刊介绍：
he Journal of Computational Electronics brings together research on all aspects of modeling and simulation of modern electronics. This includes optical, electronic, mechanical, and quantum mechanical aspects, as well as research on the underlying mathematical algorithms and computational details. The related areas of energy conversion/storage and of molecular and biological systems, in which the thrust is on the charge transport, electronic, mechanical, and optical properties, are also covered.
In particular, we encourage manuscripts dealing with device simulation; with optical and optoelectronic systems and photonics; with energy storage (e.g. batteries, fuel cells) and harvesting (e.g. photovoltaic), with simulation of circuits, VLSI layout, logic and architecture (based on, for example, CMOS devices, quantum-cellular automata, QBITs, or single-electron transistors); with electromagnetic simulations (such as microwave electronics and components); or with molecular and biological systems. However, in all these cases, the submitted manuscripts should explicitly address the electronic properties of the relevant systems, materials, or devices and/or present novel contributions to the physical models, computational strategies, or numerical algorithms.
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