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 |
| Summary: | 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. |
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| ISSN: | 2251-6107 2783-4425 |