Fast Measurements with MOX Sensors: A Least-Squares Approach to Blind Deconvolution
Metal oxide (MOX) sensors are widely used for chemical sensing due to their low cost, miniaturization, low power consumption and durability. Yet, getting instantaneous measurements of fluctuating gas concentration in turbulent plumes is not possible due to their slow response time. In this paper, we...
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MDPI AG
2019-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/18/4029 |
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| author | Dominique Martinez Javier Burgués Santiago Marco |
| author_facet | Dominique Martinez Javier Burgués Santiago Marco |
| author_sort | Dominique Martinez |
| collection | DOAJ |
| description | Metal oxide (MOX) sensors are widely used for chemical sensing due to their low cost, miniaturization, low power consumption and durability. Yet, getting instantaneous measurements of fluctuating gas concentration in turbulent plumes is not possible due to their slow response time. In this paper, we show that the slow response of MOX sensors can be compensated by deconvolution, provided that an invertible, parametrized, sensor model is available. We consider a nonlinear, first-order dynamic model that is mathematically tractable for MOX identification and deconvolution. By transforming the sensor signal in the log-domain, the system becomes linear in the parameters and these can be estimated by the least-squares techniques. Moreover, we use the MOX diversity in a sensor array to avoid training with a supervised signal. The information provided by two (or more) sensors, exposed to the same flow but responding with different dynamics, is exploited to recover the ground truth signal (gas input). This approach is known as blind deconvolution. We demonstrate its efficiency on MOX sensors recorded in turbulent plumes. The reconstructed signal is similar to the one obtained with a fast photo-ionization detector (PID). The technique is thus relevant to track a fast-changing gas concentration with MOX sensors, resulting in a compensated response time comparable to that of a PID. |
| first_indexed | 2024-04-11T13:03:18Z |
| format | Article |
| id | doaj.art-f11893c3c37243e2836777ea6628bfd4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:03:18Z |
| publishDate | 2019-09-01 |
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| series | Sensors |
| spelling | doaj.art-f11893c3c37243e2836777ea6628bfd42022-12-22T04:22:52ZengMDPI AGSensors1424-82202019-09-011918402910.3390/s19184029s19184029Fast Measurements with MOX Sensors: A Least-Squares Approach to Blind DeconvolutionDominique Martinez0Javier Burgués1Santiago Marco2Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), CNRS, INRIA, 54506 Vandoeuvre-lès-Nancy, FranceInstitute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Baldiri Reixac 10-12, 08028 Barcelona, SpainInstitute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Baldiri Reixac 10-12, 08028 Barcelona, SpainMetal oxide (MOX) sensors are widely used for chemical sensing due to their low cost, miniaturization, low power consumption and durability. Yet, getting instantaneous measurements of fluctuating gas concentration in turbulent plumes is not possible due to their slow response time. In this paper, we show that the slow response of MOX sensors can be compensated by deconvolution, provided that an invertible, parametrized, sensor model is available. We consider a nonlinear, first-order dynamic model that is mathematically tractable for MOX identification and deconvolution. By transforming the sensor signal in the log-domain, the system becomes linear in the parameters and these can be estimated by the least-squares techniques. Moreover, we use the MOX diversity in a sensor array to avoid training with a supervised signal. The information provided by two (or more) sensors, exposed to the same flow but responding with different dynamics, is exploited to recover the ground truth signal (gas input). This approach is known as blind deconvolution. We demonstrate its efficiency on MOX sensors recorded in turbulent plumes. The reconstructed signal is similar to the one obtained with a fast photo-ionization detector (PID). The technique is thus relevant to track a fast-changing gas concentration with MOX sensors, resulting in a compensated response time comparable to that of a PID.https://www.mdpi.com/1424-8220/19/18/4029MOX sensorsblind deconvolutionblind identificationleast-squaresturbulent plumes |
| spellingShingle | Dominique Martinez Javier Burgués Santiago Marco Fast Measurements with MOX Sensors: A Least-Squares Approach to Blind Deconvolution Sensors MOX sensors blind deconvolution blind identification least-squares turbulent plumes |
| title | Fast Measurements with MOX Sensors: A Least-Squares Approach to Blind Deconvolution |
| title_full | Fast Measurements with MOX Sensors: A Least-Squares Approach to Blind Deconvolution |
| title_fullStr | Fast Measurements with MOX Sensors: A Least-Squares Approach to Blind Deconvolution |
| title_full_unstemmed | Fast Measurements with MOX Sensors: A Least-Squares Approach to Blind Deconvolution |
| title_short | Fast Measurements with MOX Sensors: A Least-Squares Approach to Blind Deconvolution |
| title_sort | fast measurements with mox sensors a least squares approach to blind deconvolution |
| topic | MOX sensors blind deconvolution blind identification least-squares turbulent plumes |
| url | https://www.mdpi.com/1424-8220/19/18/4029 |
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