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A Mixture-Gas Edge-Computing Multisensor Device With Generative Learning Framework
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DOI：10.1109/JSEN.2024.3374358.png)
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Abstract
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This article presents a mixture-gas detectable edge-computing device with a generative learning framework for selectivity and accuracy. Mixture-gas detection capability is enabled through two proposed schemes of temperature modulation (TM) and cross-iterative-tuning artificial neural network (CIT-ANN). Their related computations are facilitated inside the edge device level, applying analog normalization concepts in the readout integrated circuit (ROIC). This proposed edge platform provides generative training data for mixture-gas detection, allowing much less empirical data for its learning process, especially under a mixture-gas environment. An edge-computing the Internet of Things (IoT) device prototype was manufactured based on a fabricated ROIC and in-house metal-oxide-semiconductor (MOS) sensor arrays embedding heater modulation function. Under mixture-gas experiments of NO2 and CO gases, the proposed CIT-ANN together with the heater modulation demonstrated 44% higher recognition performance than in the conventional artificial neural network (ANN). The proposed generative learning method showed higher relative label coincidence, achieving a 17% higher correlation with real training data than in the conventional mathematical interpolation method.
Keywords:
Sensors
Gas detectors
Image edge detection
Heating systems
Temperature sensors
Modulation
Resistance
Cross-iterative-tuning artificial neural network (CIT-ANN)
edge computing
generative adversarial networks (GANs)
metal-oxide semiconductor
mixture gas sensor
readout integrated circuit (ROIC)
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4.5
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2.2W
Citations:
7.3W
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