TinyML-Based Real-Time Drift Compensation for Gas Sensors Using Spectral–Temporal Neural Networks
The implementation of low-cost sensitive and selective gas sensors for monitoring fruit ripening and quality strongly depends on their long-term stability. Gas sensor drift undermines the long-term reliability of low-cost sensing platforms, particularly in precision agriculture. We present a real-ti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Chemosensors |
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| Online Access: | https://www.mdpi.com/2227-9040/13/7/223 |
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| author | Adir Krayden M. Avraham H. Ashkar T. Blank S. Stolyarova Yael Nemirovsky |
| author_facet | Adir Krayden M. Avraham H. Ashkar T. Blank S. Stolyarova Yael Nemirovsky |
| author_sort | Adir Krayden |
| collection | DOAJ |
| description | The implementation of low-cost sensitive and selective gas sensors for monitoring fruit ripening and quality strongly depends on their long-term stability. Gas sensor drift undermines the long-term reliability of low-cost sensing platforms, particularly in precision agriculture. We present a real-time drift compensation framework based on a lightweight Temporal Convolutional Neural Network (TCNN) combined with a Hadamard spectral transform. The model operates causally on incoming sensor data, achieving a mean absolute error below 1 mV on long-term recordings (equivalent to <1 particle per million (ppm) gas concentration). Through quantization, we compress the model by over 70%, without sacrificing accuracy. Demonstrated on a combustion-type gas sensor system (dubbed GMOS) for ethylene monitoring, our approach enables continuous, drift-corrected operation without the need for recalibration or dependence on cloud-based services, offering a generalizable solution for embedded environmental sensing—in food transportation containers, cold storage facilities, de-greening rooms and directly in the field. |
| format | Article |
| id | doaj-art-6785156e2bf64ac7800b23451c5b31d3 |
| institution | DOAJ |
| issn | 2227-9040 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Chemosensors |
| spelling | doaj-art-6785156e2bf64ac7800b23451c5b31d32025-08-20T03:08:00ZengMDPI AGChemosensors2227-90402025-06-0113722310.3390/chemosensors13070223TinyML-Based Real-Time Drift Compensation for Gas Sensors Using Spectral–Temporal Neural NetworksAdir Krayden0M. Avraham1H. Ashkar2T. Blank3S. Stolyarova4Yael Nemirovsky5Electrical and Computer Engineering Department, Technion—Israel Institute of Technology, Haifa 3200003, IsraelElectrical and Computer Engineering Department, Technion—Israel Institute of Technology, Haifa 3200003, IsraelElectrical and Computer Engineering Department, Technion—Israel Institute of Technology, Haifa 3200003, IsraelElectrical and Computer Engineering Department, Technion—Israel Institute of Technology, Haifa 3200003, IsraelTodos Technologies, Kinneret 12 Street, Airport City 7019900, IsraelElectrical and Computer Engineering Department, Technion—Israel Institute of Technology, Haifa 3200003, IsraelThe implementation of low-cost sensitive and selective gas sensors for monitoring fruit ripening and quality strongly depends on their long-term stability. Gas sensor drift undermines the long-term reliability of low-cost sensing platforms, particularly in precision agriculture. We present a real-time drift compensation framework based on a lightweight Temporal Convolutional Neural Network (TCNN) combined with a Hadamard spectral transform. The model operates causally on incoming sensor data, achieving a mean absolute error below 1 mV on long-term recordings (equivalent to <1 particle per million (ppm) gas concentration). Through quantization, we compress the model by over 70%, without sacrificing accuracy. Demonstrated on a combustion-type gas sensor system (dubbed GMOS) for ethylene monitoring, our approach enables continuous, drift-corrected operation without the need for recalibration or dependence on cloud-based services, offering a generalizable solution for embedded environmental sensing—in food transportation containers, cold storage facilities, de-greening rooms and directly in the field.https://www.mdpi.com/2227-9040/13/7/223gas sensor driftTinyMLconvolutional neural networksspectral filteringhadamard transformreal-time signal processing |
| spellingShingle | Adir Krayden M. Avraham H. Ashkar T. Blank S. Stolyarova Yael Nemirovsky TinyML-Based Real-Time Drift Compensation for Gas Sensors Using Spectral–Temporal Neural Networks Chemosensors gas sensor drift TinyML convolutional neural networks spectral filtering hadamard transform real-time signal processing |
| title | TinyML-Based Real-Time Drift Compensation for Gas Sensors Using Spectral–Temporal Neural Networks |
| title_full | TinyML-Based Real-Time Drift Compensation for Gas Sensors Using Spectral–Temporal Neural Networks |
| title_fullStr | TinyML-Based Real-Time Drift Compensation for Gas Sensors Using Spectral–Temporal Neural Networks |
| title_full_unstemmed | TinyML-Based Real-Time Drift Compensation for Gas Sensors Using Spectral–Temporal Neural Networks |
| title_short | TinyML-Based Real-Time Drift Compensation for Gas Sensors Using Spectral–Temporal Neural Networks |
| title_sort | tinyml based real time drift compensation for gas sensors using spectral temporal neural networks |
| topic | gas sensor drift TinyML convolutional neural networks spectral filtering hadamard transform real-time signal processing |
| url | https://www.mdpi.com/2227-9040/13/7/223 |
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