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International Journal of Information and Communication Technology Research (IJICT، جلد ۱۳، شماره ۳، صفحات ۱-۱۱
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عنوان انگلیسی Employing Machine Learning Approach in Cavity Resonator Sensors for Characterization of Lossy Dielectrics
چکیده انگلیسی مقاله 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.
کلیدواژه‌های انگلیسی مقاله Complex Permittivity, Machine Learning (ML), Photonic Band Gap, Slow-Wave, Substrate Integrated Waveguide (SIW).
نویسندگان مقاله | Kianoosh Kazemi
Department of Electrical Engineering Amirkabir University of Technology Tehran, Iran


| Gholamreza Moradi
Department of Electrical Engineering Amirkabir University of Technology Tehran, Iran
نشانی اینترنتی http://ijict.itrc.ac.ir/browse.php?a_code=A-10-503-1&slc_lang=other&sid=1
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