TREFEX: Trend Estimation and Change Detection in the Response of MOX Gas Sensors
Many applications of metal oxide gas sensors can benefit from reliable algorithms to detect significant changes in the sensor response. Significant changes indicate a change in the emission modality of a distant gas source and occur due to a sudden change of concentration or exposure to a different...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2013-06-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/13/6/7323 |
| _version_ | 1811184717949566976 |
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| author | Marco Trincavelli Erik Schaffernicht Achim J. Lilienthal Sepideh Pashami |
| author_facet | Marco Trincavelli Erik Schaffernicht Achim J. Lilienthal Sepideh Pashami |
| author_sort | Marco Trincavelli |
| collection | DOAJ |
| description | Many applications of metal oxide gas sensors can benefit from reliable algorithms to detect significant changes in the sensor response. Significant changes indicate a change in the emission modality of a distant gas source and occur due to a sudden change of concentration or exposure to a different compound. As a consequence of turbulent gas transport and the relatively slow response and recovery times of metal oxide sensors, their response in open sampling configuration exhibits strong fluctuations that interfere with the changes of interest. In this paper we introduce TREFEX, a novel change point detection algorithm, especially designed for metal oxide gas sensors in an open sampling system. TREFEX models the response of MOX sensors as a piecewise exponential signal and considers the junctions between consecutive exponentials as change points. We formulate non-linear trend filtering and change point detection as a parameter-free convex optimization problem for single sensors and sensor arrays. We evaluate the performance of the TREFEX algorithm experimentally for different metal oxide sensors and several gas emission profiles. A comparison with the previously proposed GLR method shows a clearly superior performance of the TREFEX algorithm both in detection performance and in estimating the change time. |
| first_indexed | 2024-04-11T13:18:11Z |
| format | Article |
| id | doaj.art-866afa29b4b446e88ae8c9d8d26329a5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:18:11Z |
| publishDate | 2013-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-866afa29b4b446e88ae8c9d8d26329a52022-12-22T04:22:20ZengMDPI AGSensors1424-82202013-06-011367323734410.3390/s130607323TREFEX: Trend Estimation and Change Detection in the Response of MOX Gas SensorsMarco TrincavelliErik SchaffernichtAchim J. LilienthalSepideh PashamiMany applications of metal oxide gas sensors can benefit from reliable algorithms to detect significant changes in the sensor response. Significant changes indicate a change in the emission modality of a distant gas source and occur due to a sudden change of concentration or exposure to a different compound. As a consequence of turbulent gas transport and the relatively slow response and recovery times of metal oxide sensors, their response in open sampling configuration exhibits strong fluctuations that interfere with the changes of interest. In this paper we introduce TREFEX, a novel change point detection algorithm, especially designed for metal oxide gas sensors in an open sampling system. TREFEX models the response of MOX sensors as a piecewise exponential signal and considers the junctions between consecutive exponentials as change points. We formulate non-linear trend filtering and change point detection as a parameter-free convex optimization problem for single sensors and sensor arrays. We evaluate the performance of the TREFEX algorithm experimentally for different metal oxide sensors and several gas emission profiles. A comparison with the previously proposed GLR method shows a clearly superior performance of the TREFEX algorithm both in detection performance and in estimating the change time.http://www.mdpi.com/1424-8220/13/6/7323metal oxide sensorsopen sampling systemchange point detection, trend filtering |
| spellingShingle | Marco Trincavelli Erik Schaffernicht Achim J. Lilienthal Sepideh Pashami TREFEX: Trend Estimation and Change Detection in the Response of MOX Gas Sensors Sensors metal oxide sensors open sampling system change point detection, trend filtering |
| title | TREFEX: Trend Estimation and Change Detection in the Response of MOX Gas Sensors |
| title_full | TREFEX: Trend Estimation and Change Detection in the Response of MOX Gas Sensors |
| title_fullStr | TREFEX: Trend Estimation and Change Detection in the Response of MOX Gas Sensors |
| title_full_unstemmed | TREFEX: Trend Estimation and Change Detection in the Response of MOX Gas Sensors |
| title_short | TREFEX: Trend Estimation and Change Detection in the Response of MOX Gas Sensors |
| title_sort | trefex trend estimation and change detection in the response of mox gas sensors |
| topic | metal oxide sensors open sampling system change point detection, trend filtering |
| url | http://www.mdpi.com/1424-8220/13/6/7323 |
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