2025/12/15 by Mojtaba Joodaki, Joodaki, Mojtaba, Idriz Pelaj +1
Engineering · #Microwave and Dielectric Measurement Techniques #Microwave Imaging and Scattering Analysis #Electrical and Bioimpedance Tomography
paper · pdf · doi:10.48550/arxiv.2512.13233
A non-destructive, real-time method for estimating the volume fraction of a dielectric mixture inside a resonant cavity is presented. A convolutional neural network (CNN)-based approach is used to estimate the fractional composition of two-phase dielectric mixtures inside a resonant cavity using scattering parameter (S-parameter) measurements. A rectangular cavity sensor with a strip feed structure is characterized using a vector network analyzer (VNA) from 0.01--20~GHz. The CNN is trained using both simulated and experimentally measured S-parameters and achieves high predictive accuracy even without de-embedding or filtering, demonstrating robustness to measurement imperfections. The simulation results achieve a coefficient of determination (R2)=0.99 using k-fold cross-validation, while the experimental model using raw data achieves an R2=0.94 with a mean absolute error (MAE) below 6%. Data augmentation further improves the accuracy of the experimental prediction to above R2=0.998 (MAE<0.72%). The proposed method enables rapid, non-destructive, accurate, low-cost, and real-time estimation of material fractions, illustrating strong potential for sensing applications in microwave material characterization.