CMOS-MEMS Gas Sensor Dubbed GMOS for SelectiveAnalysis of Gases with Tiny Edge Machine Learning

Krayden, Adir; Schohet, Maayan; Shmueli, Oz; Shlenkevitch, Dima; Blank, Tanya; Stolyarova, Sara; Nemirovsky, Y.

doi:10.3390/ecsa-9-13316

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…To improve GMOS ability of gas analysis (in real-time), which includes both gas types, as well as gas concentrations, we added a pre-trained neural network, which is written to an Arduino controller [ 19 ]. The system is shown in Figure 2 .…”

Section: Tinyml For Gas Classificationmentioning

confidence: 99%

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Selective Sensing of Mixtures of Gases with CMOS-SOI-MEMS Sensor Dubbed GMOS

Krayden

Shlenkevitch²,

Blank

et al. 2023

Micromachines

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The need to achieve digital gas sensing technology, namely, a technology to sense and transmit gas-enabled digital media, has been recognized as highly challenging. This challenge has motivated the authors to focus on complementary metal oxide semiconductor silicon on insulator micro electro-mechanical system (CMOS-SOI-MEMS) technologies, and the result is a new pellistor-like sensor, dubbed GMOS, with integrated signal processing. In this study, we describe the performance of such sensors for the selective detection of mixtures of gases. The novel key ideas of this study are: (i) the use of the GMOS for gas sensing; (ii) applying the Kalman filter to improve the signal-to-noise ratio; (iii) adding artificial intelligence (AI) with tiny edge approach.

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Section: Tinyml For Gas Classificationmentioning

confidence: 99%

“…Section 4 describes the role of the Kalman filter in improving the signal-to-noise ratio. Finally, the tiny edge revolution [ 18 , 19 ], which makes the GMOS smarter by applying machine learning (ML) techniques based on the tiny edge ML, is briefly described.…”

Section: Introductionmentioning

confidence: 99%

Selective Sensing of Mixtures of Gases with CMOS-SOI-MEMS Sensor Dubbed GMOS

Krayden

Shlenkevitch²,

Blank

et al. 2023

Micromachines

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A Gas Leakage Detection Device Based on the Technology of TinyML †

et al. 2023

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Internet of Things devices are frequently used as consumer devices to provide digital solutions, such as smart lighting and digital voice-activated assistants, but they are also employed to alert residents in the instance of an emergency. Given the increasingly costly nature of present neural network systems, it is necessary to transport information to the cloud for intelligent machine analysis. TinyML is a potential technology that has been presented by the research world for building fully independent and safe devices that can gather, analyze, and produce data, without transferring it to distant organizations. This paper describes a gas leakage detection system based on TinyML. The proposed solution can be programmed to identify anomalies and warn occupants via the utilization of the BLE technology, in addition to an incorporated LCD screen. Experiments have been employed to show and assess two distinct test situations. For the first occasion, the smoke detection test case, the system earned an F1-Score of 0.77, whereas the F1-Score for the ammonia test case was 0.70.

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CMOS-MEMS Gas Sensor Dubbed GMOS for SelectiveAnalysis of Gases with Tiny Edge Machine Learning

Cited by 2 publications

References 5 publications

Selective Sensing of Mixtures of Gases with CMOS-SOI-MEMS Sensor Dubbed GMOS

Selective Sensing of Mixtures of Gases with CMOS-SOI-MEMS Sensor Dubbed GMOS

A Gas Leakage Detection Device Based on the Technology of TinyML †

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