Article “Comparative Analysis of Machine Learning Techniques for Temperature Compensation in Microwave Sensors” was accepted for publication in IEEE Transactions on Microwave Theory and Techniques
Nazli's article "Comparative Analysis of Machine Learning Techniques for Temperature Compensation in Microwave Sensors" was accepted for publication in IEEE Transactions on Microwave Theory and Techniques. Congratulations! Abstract: The planar nature of microwave sensors leaves them vulnerable to ambient temperature changes with potential impact on the perception of the material under test. A temperature compensation technique is required to consider its direct effect on the dielectric property of materials. In this article, machine learning algorithms are employed in two configurations of classifier and regressor on frequency response of a split-ring resonator operating at 1.19 GHz. A wide range of dielectric constant is covered with concentrations of [0:20%:100%]-methanol/acetone in water with a temperature cycle of 25 °C-50 °C. This broad variety of cases captures the complicacy of entangled trends that are…
