Selective Microwave Zeroth-Order Resonator Sensor Aided by Machine Learning
Microwave sensors are principally sensitive to effective permittivity, and hence not selective to a specific material under test (MUT). In this work, a highly compact microwave planar sensor based on zeroth-order resonance is designed to operate at three distant frequencies of 3.5, 4.3, and 5 GHz, w...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/14/5362 |
| _version_ | 1797443836052504576 |
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| author | Nazli Kazemi Nastaran Gholizadeh Petr Musilek |
| author_facet | Nazli Kazemi Nastaran Gholizadeh Petr Musilek |
| author_sort | Nazli Kazemi |
| collection | DOAJ |
| description | Microwave sensors are principally sensitive to effective permittivity, and hence not selective to a specific material under test (MUT). In this work, a highly compact microwave planar sensor based on zeroth-order resonance is designed to operate at three distant frequencies of 3.5, 4.3, and 5 GHz, with the size of only <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>λ</mi><mrow><mi>g</mi><mo>−</mo><mi>m</mi><mi>i</mi><mi>n</mi></mrow></msub><mo>/</mo><mn>8</mn></mrow></semantics></math></inline-formula> per resonator. This resonator is deployed to characterize liquid mixtures with one desired MUT (here water) combined with an interfering material (e.g., methanol, ethanol, or acetone) with various concentrations (0%:10%:100%). To achieve a sensor with selectivity to water, a convolutional neural network (CNN) is used to recognize different concentrations of water regardless of the host medium. To obtain a high accuracy of this classification, Style-GAN is utilized to generate a reliable sensor response for concentrations between water and the host medium (methanol, ethanol, and acetone). A high accuracy of 90.7% is achieved using CNN for selectively discriminating water concentrations. |
| first_indexed | 2024-03-09T13:02:48Z |
| format | Article |
| id | doaj.art-6e55b411d4c94553bb07d1f282ab7d2b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T13:02:48Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6e55b411d4c94553bb07d1f282ab7d2b2023-11-30T21:52:22ZengMDPI AGSensors1424-82202022-07-012214536210.3390/s22145362Selective Microwave Zeroth-Order Resonator Sensor Aided by Machine LearningNazli Kazemi0Nastaran Gholizadeh1Petr Musilek2Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, CanadaElectrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, CanadaElectrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, CanadaMicrowave sensors are principally sensitive to effective permittivity, and hence not selective to a specific material under test (MUT). In this work, a highly compact microwave planar sensor based on zeroth-order resonance is designed to operate at three distant frequencies of 3.5, 4.3, and 5 GHz, with the size of only <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>λ</mi><mrow><mi>g</mi><mo>−</mo><mi>m</mi><mi>i</mi><mi>n</mi></mrow></msub><mo>/</mo><mn>8</mn></mrow></semantics></math></inline-formula> per resonator. This resonator is deployed to characterize liquid mixtures with one desired MUT (here water) combined with an interfering material (e.g., methanol, ethanol, or acetone) with various concentrations (0%:10%:100%). To achieve a sensor with selectivity to water, a convolutional neural network (CNN) is used to recognize different concentrations of water regardless of the host medium. To obtain a high accuracy of this classification, Style-GAN is utilized to generate a reliable sensor response for concentrations between water and the host medium (methanol, ethanol, and acetone). A high accuracy of 90.7% is achieved using CNN for selectively discriminating water concentrations.https://www.mdpi.com/1424-8220/22/14/5362microwave sensorselectivityresonatorsmachine learninggenerative adversarial network |
| spellingShingle | Nazli Kazemi Nastaran Gholizadeh Petr Musilek Selective Microwave Zeroth-Order Resonator Sensor Aided by Machine Learning Sensors microwave sensor selectivity resonators machine learning generative adversarial network |
| title | Selective Microwave Zeroth-Order Resonator Sensor Aided by Machine Learning |
| title_full | Selective Microwave Zeroth-Order Resonator Sensor Aided by Machine Learning |
| title_fullStr | Selective Microwave Zeroth-Order Resonator Sensor Aided by Machine Learning |
| title_full_unstemmed | Selective Microwave Zeroth-Order Resonator Sensor Aided by Machine Learning |
| title_short | Selective Microwave Zeroth-Order Resonator Sensor Aided by Machine Learning |
| title_sort | selective microwave zeroth order resonator sensor aided by machine learning |
| topic | microwave sensor selectivity resonators machine learning generative adversarial network |
| url | https://www.mdpi.com/1424-8220/22/14/5362 |
| work_keys_str_mv | AT nazlikazemi selectivemicrowavezerothorderresonatorsensoraidedbymachinelearning AT nastarangholizadeh selectivemicrowavezerothorderresonatorsensoraidedbymachinelearning AT petrmusilek selectivemicrowavezerothorderresonatorsensoraidedbymachinelearning |