Adel Bedair Abdel-Rahman

Low-emissivity glazing reduces heat transfer, but its continuous fluorine-doped tin oxide (FTO) coating attenuates wireless signals. A two-scale coating-budget framework treats coating removal as a cost to minimize inside the patterned window and over the whole pane. A laboratory insulating-glass unit of commercial K-Glass was laser-patterned over a 50 mm × 50 mm window on both cavity-facing surfaces with hexagonal-ring and square-patch arrays. The coating was modeled as a lossy sheet (design sheet resistance 17 Ω/sq; measured 12.4 ± 0.2 Ω/sq). Full-wave simulation predicts an intrinsic 0.7–6 GHz passband with ≈1.5 dB mean insertion loss, reproduced by a fitted equivalent-circuit model to 0.37 dB. Angular modeling keeps the passband to 40° for both polarizations (transverse-electric loss up to ≈3.5 dB); oblique incidence was not measured. Single-mounting free-space measurements show ≈15 dB mean transmission recovery over 1–6 GHz relative to the unpatterned unit; an independent campaign gives ≈19 dB at 2.4 GHz. Inside the patterned window, 47% of the coating is removed, yet 98.3% of the front-pane and 99.1% of the rear-pane coating remain on the 300 mm × 300 mm panes (98.7% combined). Area-averaged estimates bound the per-pane visible-transmittance change to 1.7 percentage points and emissivity rise to 0.012; neither was measured.

Vehicular networks (VNs) enable information exchange among vehicles and infrastructure, however, secure and reliable communication is challenged by high mobility, energy constraints, and mobile eavesdroppers. Reconfigurable intelligent surfaces can improve coverage and security, but, their reliance on real-time channel state information and active control limits practicality in energy-constrained VNs. As a low-complexity option, non-reconfigurable metasurfaces (NRMS) enable passive electromagnetic focusing. This work analyzes the security-reliability trade-off (SRT) of a wireless energy transfer-enabled VN using NRMS-integrated antennas. Closed-form expressions for outage probability (OP), intercept probability (IP), and the SRT are derived. The analysis is extended in the high signal to noise ratio regime, where the asymptotic expressions for the OP (AOP) and IP (AIP) are obtained. Monte Carlo simulations are used to verify analytical accuracy and validate the reliability and security gains of NRMS-integrated over standalone antennas.

This paper presents the design, optimization, and validation of a non-invasive planar microwave resonator for early detection of malignant tissue based on dielectric contrast. Operating in the 3–4 GHz band, the sensor was optimized through fullwave simulations to maximize the frequency–permittivity sensitivity while maintaining compact geometry and safe exposure levels. The optimized design exhibits a resonant downshift of 40– 60 MHz for malignant inclusions with ε_r ≈ 50 and diameters of 8–10 mm located at depths up to 12–15 mm. The measured detection uncertainty (u_f ≈ 8–10 MHz) is significantly smaller than the tumor-induced perturbation, enabling reliable differentiation between healthy and malignant phantoms. Experimental validation demonstrates close agreement with simulations, with resonance deviations typically below 5%. The specific absorption rate (SAR) remained well under IEEE C95.1 limits for an input power of 1 mW, confirming safe operation. These results establish the proposed sensor as a compact and sensitive platform for microwave-based tissue abnormality detection and provide a quantitative framework for future clinical extensions targeting smaller or deeper lesions.

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 paper 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 fruit 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.

The security of a vehicular network utilizing wireless energy transfer in conjunction with near-field non-reconfigurable metasurface (NRMS) technology is analyzed in this article. The system involves an energy-limited mobile source that collects energy from a roadside power beacon using the time-switching (TS) scheme, after which it transmits signals to the destination while a passive eavesdropper tries to intercept them. Two cases of antennas are analyzed, namely, NRMS-coated and standalone antennas. We derived realistic statistical models for double Nakagami-m faded channels to model the direct and reflected signal paths influenced by the NRMS-coated antenna. Analytical expressions are derived for secrecy outage probability (SOP), asymptotic SOP, secrecy diversity order (SDO), average secrecy outage duration, and average secrecy outage rate. A SOP minimization problem is formed and solved to find the optimal TS ratio using particle swarm optimization. Monte Carlo simulations validated the accuracy of our analytical closed-form expressions. The NRMS-coated antenna reaches the same SOP with 10 dB less power and achieves a higher SDO based on its number of reflective elements compared to the standalone antenna.

Microwave-based non-invasive blood glucose monitoring (NIBGM) has emerged as a promising alternative to traditional finger-prick and enzymatic methods, offering painless, continuous, and real-time glucose tracking. This paper presents a comprehensive technical review and analytical framework for the design, modeling, and evaluation of microwave NIBGM sensors. Critical sensor architectures such as split-ring resonators (SRRs), complementary SRRs (CSRRs), dielectric resonator antennas (DRAs), and microfluidic electric-LC (ELC) structures are reviewed in terms of sensitivity, operating frequency, coupling efficiency, and material integration. We develop analytical and empirical models to simulate dielectric behavior, resonant frequency shifts, and permittivity changes across physiological glucose concentrations. Safety compliance is addressed using specific absorption rate (SAR) metrics aligned with IEEE C95.1 and ICNIRP standards. To enhance prediction accuracy, we apply machine learning models artificial neural networks (ANN), support vector regression (SVR), and long short-term memory (LSTM) to sensor outputs. These models are evaluated using mean absolute relative difference (MARD), coefficient of determination ( $R^{2}$ ), and Clarke Error Grid zones, benchmarked against commercial glucometer data from phantom and preliminary in vivo measurements. The validation strategy includes cross-validation and repeatability assessments, with plans for future deployment on standardized clinical datasets. Structured visual workflows and comparative performance graphs are presented to aid interpretability. Overall, the proposed framework synthesizes electromagnetic theory, safety constraints, and data-driven intelligence into a cohesive architecture for next-generation wearable glucose monitors that are accurate, safe, adaptive, and clinically aligned.

This study introduces a comprehensive system for glucose level measurement using a compact two-port rectangular dielectric resonator (RDR) to validate the system’s accuracy against traditional invasive glucometer measurements. The system features a voltage-controlled oscillator (VCO) that generates a 2.47 GHz frequency. A power detector converts the RF signal to DC voltage levels, and a multimeter facilitates the interpretation. The primary sensor component is energized via a rectangular aperture-coupling mechanism in the ground plane. The RDR acts as a sensor because of the varying dielectric permittivity linked to different glucose concentrations, leading to unique resonance frequencies and magnitude shifts. The bare sensor's resonance frequency was designed to be at 3.28 GHz, shifting to 2.47 GHz when loaded with a human finger. The Cole-Cole method modeled the human thumb with the blood layer in simulation. An electrical prototype enhanced detection, providing a 39 E-02 MHz/mg/dL resolution at 2.47 GHz. The RDR sensors' S-parameters highly correlated with laboratory-based testing, achieving 92.68% accuracy. Compared to home-based invasive glucometer measurements, the proposed Continuous Glucose Monitoring (CGM) system with a two-port RDR sensor measures diabetes value changes with 92.08% precision.

In this study we present the design and realization of a tunable dual band wireless power transfer (TDB-WPT) coupled resonator system. The frequency respon