Recent Trends in AI Based Antennas and Resonators Sensing Systems

Part A: Machine Learning Enhanced Microwave Biosensor for Glucose Detection in Fruit Juices

Rapid assessment of glucose in physiologically and commercially consumed liquids is important to health and food-quality monitoring. However, conventional enzyme-based methods are invasive and inefficient for continuous or real-time glucose quantification. This work proposes a miniature U-shaped microwave biosensor integrated with an interdigital capacitor (IDC) and fabricated on a Rogers RO4003C substrate for in vitro dielectric characterization of glucose based liquids. To realize bandpass response and enhance selectivity, a 1.5 pF series capacitor is incorporated into the feedline. The sensor demonstrates distinct and sharp resonance dips in both reflection coefficient (S11) and transmission coefficient (S21), validating its strong resonant response suitable for dielectric sensing. Glucose-water solutions with concentrations of 5%, 10%, 15%, and 25% were measured, demonstrating minimal resonant frequency drift and a repeatable 0.8 dB variation in the magnitude of S11, corresponding to an average sensitivity of approximately 0.05 dB/% glucose. Comparative measurements of fresh and processed mango, guava, orange, and grape juices indicated that different S-parameter responses are related to the concentration of sugar and the effect of processing. For automated data interpretation, a hybrid machine learning (ML) framework combining Random Forest (RF)–based feature selection, XGBoost refinement, and artificial neural network (ANN) classification was employed for juice-type discrimination. Glucose concentration is predicted using a deep neural network (128–64–32–1), achieving MSE of 0.2297, MAE of 0.21%, and R2 of 0.979, with over 95% of predictions within ± 2% error, demonstrating reliable in vitro glucose sensing and food-quality assessment.

Part B: Sensing Antenna System for Termites Detection and Control

Termites infestation is considered one of the main challenges affecting the wood integrity, particularly in damp environments, causing extensive damage to wooden structures and resulting in heavy economic losses worldwide A Novel sensing antenna system for termites detection and control is proposed for combating termites. Considering the effect of the moisture content in the presence of termites, a non-invasive technique that is based on detecting the moist spots of wood is introduced. The proposed sensing antenna system consists of two identical microstrip-fed H-slot antennas and a reflector at a distance of 1 cm.
An artificial intelligence-based solution is proposed to improve design efficiency and alleviate the burden on human efforts. By leveraging artificial intelligence (AI), this approach aims to streamline H-slot antenna design process. Based on the transmission coefficient level between the antennas, the moisture within the wood is detected. By utilizing the reflector, the system can achieve multiband operation and enhance the simulated gain to approximately 8.76 dBi, thereby improving its detection capability. The proposed single antenna was built on a commercial low cost FR4 substrate with compact dimensions of 20 mm × 20 mm × 1.6 mm. Through optimization of the stub dimensions, the proposed antenna resonates effectively at 4.2 GHz. Simulation results demonstrate that the system can achieve excellent moisture sensing capabilities not only across the wood surface but also to a depth of 0.95 inch using a perfect electric conductor (PEC) reflector. Regardless of the wood type, the proposed sensor also can identify moisture levels of 30%. The complete sensing system has been manufactured, and simulation results have been validated through measurements, confirming the efficiency of the proposed system as a moisture sensor for wooden structures.