Research Information System
IEEE USNC-URSI Radio Science Meeting / Joint IEEE Antennas-and-Propagation-Society (AP-S) International Symposium, Colorado, United States Of America, 10 - 15 July 2022, pp.34-35
The implementation of machine and deep learning algorithms to solve microwave related problems have been of interest to researchers. These algorithms require large amount of data to design a robust model. In this work, a cost-effective and fast method to retrieve the reflection coefficients of a material under test using the admittance model of the open-ended coaxial probe is presented. The reflection coefficients of three pure liquids and four mixture solutions generated, measured and analyzed. The results from the admittance model is compared with CST Microwave studio simulations and experimental measurements. The reflection coefficients retrieved via the admittance model indicate a good agreement with the simulated and measured results.