Employing Machine Learning Approach in Cavity Resonator Sensors for Characterization of Lossy Dielectrics
This work presents a novel microwave sensor that is specially designed for the retrieval of complex permittivity. The proposed sensor is designed to operate in the C band (4.54 GHz). By implementing a novel feeding structure, the proposed SIW cavity design improves the coupling and achieves a better...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Iran Telecom Research Center
2021-09-01
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| Series: | International Journal of Information and Communication Technology Research |
| Subjects: | |
| Online Access: | http://ijict.itrc.ac.ir/article-1-485-en.html |
| _version_ | 1811169214505943040 |
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| author | Kianoosh Kazemi Gholamreza Moradi |
| author_facet | Kianoosh Kazemi Gholamreza Moradi |
| author_sort | Kianoosh Kazemi |
| collection | DOAJ |
| description | This work presents a novel microwave sensor that is specially designed for the retrieval of complex permittivity. The proposed sensor is designed to operate in the C band (4.54 GHz). By implementing a novel feeding structure, the proposed SIW cavity design improves the coupling and achieves a better quality factor. Several techniques are used to enhance sensitivity, including a Photonic Band Gap (PBG), corner cut, and slow-wave vias. These techniques increase the interaction between the material under test and the electric field. By utilizing slow-wave vias, 35% size reduction is achieved. Achieving simultaneous miniaturization and sensitivity enhancement in this study introduces a new possibility and application for sensor design. The values of complex permittivities are extracted from scattering parameters obtained from simulation of the structure in CST Microwave Studio (MWS) using a machine learning approaches. Our sensor has 0.8% sensitivity, which is better than that of other sensors. Moreover, the maximum error rate in our method is lower than other existing methods. This ratio for the proposed method is 2.31% while for curve fitting and analytical solutions are 26% and 16%, respectively. |
| first_indexed | 2024-04-10T16:38:42Z |
| format | Article |
| id | doaj.art-3630fc7217f2460abd777e707b2e715e |
| institution | Directory Open Access Journal |
| issn | 2251-6107 2783-4425 |
| language | English |
| last_indexed | 2024-04-10T16:38:42Z |
| publishDate | 2021-09-01 |
| publisher | Iran Telecom Research Center |
| record_format | Article |
| series | International Journal of Information and Communication Technology Research |
| spelling | doaj.art-3630fc7217f2460abd777e707b2e715e2023-02-08T08:00:26ZengIran Telecom Research CenterInternational Journal of Information and Communication Technology Research2251-61072783-44252021-09-01133111Employing Machine Learning Approach in Cavity Resonator Sensors for Characterization of Lossy DielectricsKianoosh Kazemi0Gholamreza Moradi1 Department of Electrical Engineering Amirkabir University of Technology Tehran, Iran Department of Electrical Engineering Amirkabir University of Technology Tehran, Iran This work presents a novel microwave sensor that is specially designed for the retrieval of complex permittivity. The proposed sensor is designed to operate in the C band (4.54 GHz). By implementing a novel feeding structure, the proposed SIW cavity design improves the coupling and achieves a better quality factor. Several techniques are used to enhance sensitivity, including a Photonic Band Gap (PBG), corner cut, and slow-wave vias. These techniques increase the interaction between the material under test and the electric field. By utilizing slow-wave vias, 35% size reduction is achieved. Achieving simultaneous miniaturization and sensitivity enhancement in this study introduces a new possibility and application for sensor design. The values of complex permittivities are extracted from scattering parameters obtained from simulation of the structure in CST Microwave Studio (MWS) using a machine learning approaches. Our sensor has 0.8% sensitivity, which is better than that of other sensors. Moreover, the maximum error rate in our method is lower than other existing methods. This ratio for the proposed method is 2.31% while for curve fitting and analytical solutions are 26% and 16%, respectively.http://ijict.itrc.ac.ir/article-1-485-en.htmlcomplex permittivitymachine learning (ml)photonic band gapslow-wavesubstrate integrated waveguide (siw). |
| spellingShingle | Kianoosh Kazemi Gholamreza Moradi Employing Machine Learning Approach in Cavity Resonator Sensors for Characterization of Lossy Dielectrics International Journal of Information and Communication Technology Research complex permittivity machine learning (ml) photonic band gap slow-wave substrate integrated waveguide (siw). |
| title | Employing Machine Learning Approach in Cavity Resonator Sensors for Characterization of Lossy Dielectrics |
| title_full | Employing Machine Learning Approach in Cavity Resonator Sensors for Characterization of Lossy Dielectrics |
| title_fullStr | Employing Machine Learning Approach in Cavity Resonator Sensors for Characterization of Lossy Dielectrics |
| title_full_unstemmed | Employing Machine Learning Approach in Cavity Resonator Sensors for Characterization of Lossy Dielectrics |
| title_short | Employing Machine Learning Approach in Cavity Resonator Sensors for Characterization of Lossy Dielectrics |
| title_sort | employing machine learning approach in cavity resonator sensors for characterization of lossy dielectrics |
| topic | complex permittivity machine learning (ml) photonic band gap slow-wave substrate integrated waveguide (siw). |
| url | http://ijict.itrc.ac.ir/article-1-485-en.html |
| work_keys_str_mv | AT kianooshkazemi employingmachinelearningapproachincavityresonatorsensorsforcharacterizationoflossydielectrics AT gholamrezamoradi employingmachinelearningapproachincavityresonatorsensorsforcharacterizationoflossydielectrics |