Negin Shariati

This paper presents an ultra-compact interdigital-loaded microstrip line-array lens (MALL) for passive beam steering and direction finding. The proposed structure implements a discrete fractional Fourier transform through engineered coupling between microstrip lines, enabling fixed-angle beam steering without active phase shifters. A 9-port prototype operating at 2.1 GHz was designed, fabricated, and experimentally validated. The antenna array achieves beam steering from −60° to +60° with gain variation within ±0.8 dB across the steering range, sidelobe levels below −11 dB, and beam-pointing error less than 5°. The measured reflection coefficient S₁₁ remains below −10 dB over a 10% fractional bandwidth. The overall footprint is 0.48λ₀ × 0.52λ₀, demonstrating a compact and fully passive alternative to conventional Butler matrix-based beamforming networks with reduced complexity and low insertion loss.

Soil moisture is a key variable in the hydrological cycle, essential for drought monitoring, water resource management, and agriculture decision-making. However, accurate forecasting of soil moisture remains a challenge due to the nonlinear nature of soil water dynamics and the complex influencing factors. While deep learning models such as Long Short-Term Memory and Transformers have advanced temporal modelling, they struggle with spatial dependencies across diverse geographic locations, as their architecture primarily focuses on temporal dynamics. To address this limitation, we propose a novel hybrid framework that integrates graph neural networks with state-of-the-art time series models. We introduce a Graph-Transformer architecture for improved soil moisture forecasting by jointly modelling spatiotemporal dependencies. Our model forecasts soil moisture at two depths including 0–7 cm, referred to as surface soil moisture (SSM), and 7–28 cm, representing the root-zone soil moisture (RZSM). Evaluation using four years of multimodal data from 19 sites across Australia shows that the proposed Graph-Transformer model outperforms the standard Transformer, achieving improvements of up to 10% in the coefficient of determination ( $R^{2}$ ) and 8% in Root Mean Square Error (RMSE) for SSM, and up to 11% and 9% in $R^{2}$ and RMSE, respectively, for RZSM. These findings highlight that integrating graph architecture with temporal models can potentially enhance soil moisture forecasting across diverse geographic regions, benefiting effective drought monitoring and agricultural planning.

A dynamic beamforming system has been developed to enhance vehicular connectivity in rural and remote areas, addressing critical social, economic, and safety challenges. The system features a reconfigurable, roof rack-mounted, ventilated log-periodic antenna (DRV-LPA), providing full 360° azimuthal coverage around the vehicle. Beamforming is implemented on the forward (north) and backward (south) sides, with three LTE and three WiFi antennas per side, enabling dynamic directional control of beams. The DRV-LPA electronically steers its beam from 0° to ±30°, based on electric field theory, and is validated through full-wave electromagnetic simulations. System performance was evaluated with two setups: anechoic chamber measurements and field trials in regional Australia. The system supports three beam configurations θ₁ = 0°, θ₂ = ±30°, and θ₃ = +30°, 0°, -30° adapted to communication needs and vehicle orientation. A compact wireless router integrated with the switching module manages continuous data exchange with a remote base station, ensuring stable connectivity and optimized bandwidth. The switching module dynamically activates the appropriate beam based on the vehicle's position relative to the base station. Simulated and measured results show strong agreement in beam direction, gain, VSWR, and overall performance within the roof rack. Field trials confirm the system's robustness and effectiveness in real-world vehicular scenarios, offering a cost-effective, scalable solution for improving vehicle-based connectivity, enabling precision agriculture, and strengthening rural mobility infrastructure.

This industry-driven research presents a comprehensive frequency-domain framework for improving the simultaneous estimation of soil salinity and volumetric water content (VWC) through the analysis of soil dielectric permittivity and electrical conductivity (EC). While traditional sensors rely on single-frequency measurements, this study introduces a multi-frequency approach by identifying the frequencies most sensitive to variations in salinity and moisture, demonstrating that using more measurement frequencies increases predictive accuracy. The dataset consisted of twenty soil samples prepared with five salinity levels (0–2 g/kg) and four VWC levels (1–15%). Measurements were conducted using a DAK (Dielectric Assessment Kit) system coupled with a vector network analyzer (VNA). To expand the dataset for neural network training, empirical models were employed to generate additional samples, increasing the total dataset size to over 3000. By analyzing EC and permittivity across the 10–295 MHz range, six optimal frequencies were identified as primary candidates. These six frequencies were used to form 57 combinations, including single, dual, triple, four-frequency, and six-frequency sets. In total, 57 neural network models were trained to evaluate each frequency’s contribution to prediction accuracy. The results show that single-frequency models exhibited average prediction errors of approximately 30% for VWC and 15% for salinity, whereas two-frequency models reduced these errors to about 10% and 5%. Three-frequency combinations achieved nearly zero prediction errors (<1%) for both parameters. These findings show that only a few strategically selected frequencies are sufficient to capture the key electromagnetic behavior of soil and significantly enhance prediction performance.

Diabetes disrupts glucose regulation in both humans and animals, severely impacting liver and muscle function. Commonly used monitoring methods are invasive and cumbersome to implant, highlighting the need for non-invasive alternatives. This study focuses on developing a non-invasive, lightweight, and highly sensitive three-dimensional ear-conformed resonator (3D-ECR) sensor for glucose monitoring. Using a dielectric assessment kit (DAK), this work measures permittivity in standard aqueous glucose samples (35 to 110 mg/dL), covering the majority of the physiologically observed glucose range in humans and cattle. The 3D-ECR sensor analyzes resonance frequency and phase variations of S₂₁, achieving phase sensitivities of 1°/(mg/dL) at 2.45 GHz and 0.7°/(mg/dL) at 4.3 GHz, and frequency sensitivity of 3.2 MHz/(mg/dL). This innovative sensor offers a promising solution for glucose monitoring in biomedical and agricultural applications. This work serves as a laboratory-level proof of concept using standard aqueous glucose samples, with future studies planned for tissue-mimicking phantoms and clinical validation.

This industry-driven paper introduces a novel multi-frequency soil sensor for precision agriculture applications. The sensor simultaneously measures Volumetric Water Content (VWC) and salinity by leveraging the soil’s electrical properties. Unlike traditional Time Domain Reflectometry (TDR) sensors, which rely on pulse signals and time-delay measurements, the proposed sensor utilizes continuous waves, phase shifts, and amplitude variations to enhance accuracy and reduce system complexity. Operating simultaneously at frequencies of 70 MHz, 140 MHz, and 210 MHz , the multi-frequency approach mitigates the errors inherent in single-frequency measurements, ensuring improved precision even in heterogeneous soils. A cost-effective frequency generation method utilizing a single voltage-controlled oscillator (VCO) and its harmonics was implemented, significantly reducing hardware requirements while maintaining high performance. Additionally, machine learning models, trained on a dedicated calibration dataset, were used to accurately interpret raw sensor data and predict both VWC and salinity values. Machine learning models, including a Neural Network (NN) and Support Vector Regression (SVR) with a radial basis function (RBF) kernel, were developed and compared to estimate soil volumetric water content and salinity from phase and amplitude data. Experimental results demonstrated the proposed MMSS sensor achieves high accuracy in predicting soil parameters, with mean absolute error (MAE) of 0.4% for VWC and 0.05 g/kg for potassium concentration, outperforming existing soil moisture sensors, and demonstrating robust performance for precision agriculture applications and environmental monitoring. Its accuracy, scalability, and cost-effectiveness make it a valuable tool for optimizing resource usage and enhancing crop yields.

Matrix operations are at the core of signal processing in radiofrequency and microwave networks. While analog matrix computations can dramatically speed up signal processing in multiport networks, they can also reduce the size, weight, and power of radiofrequency and microwave devices by partially eliminating the need for power-hungry electronics. These computing devices exploit fundamental properties of electromagnetic waves, enabling parallel signal processing at the speed of light. Here, we propose and demonstrate a microwave-integrated circuit capable of implementing universal unitary matrix transformations. The proposed device operates by alternating non-reconfigurable and reconfigurable layers of basic RF components, comprising cascaded power dividers and programmable phase elements, respectively. The controllable multipath interference through conjunctive use of linear wave mixing with active phase control enables creating complex transformations in this device. We experimentally demonstrate this device concept using a four-port integrated circuit operating across the frequency range of 1.5–3.0 GHz and at hundreds of micro-Watt power levels. The proposed device can pave the way for universal analog radiofrequency and microwave processors and preprocessors with programmable functionalities for multipurpose applications in advanced communications and radar systems.

This paper presents a technique for reducing the sensitivity threshold (ST) and improving the shielding efficiency (SE) of a novel nonlinear metasurface under high-power microwave (HPM) excitation. HPM sources and pulse jammers can significantly disrupt electronic systems, such as radars and sensors, by emitting high-intensity bursts that may saturate or damage the receiver front ends. The nonlinear power-selective surface exhibits intensity dependent transmission characteristics that can enable the passage of low-power signals and reject high-power interference signals at the same operational frequency. Reducing the ST is critical to ensure effective shielding even at low-level threats, thereby improving protection for highly sensitive equipment. Additionally, improved SE offers greater shielding under high-power exposure. To address this, an innovative technique is introduced that achieves a 3 dB reduction in ST and enhances SE by up to 18 dB, without modifying the diode model, an approach not previously reported. This work also features a novel, polarization and incidence angle-insensitive design with a peak SE of 36 dB in a compact 0.145λ₀ × 0.145λ₀ unit cell and 0.01λ₀ thickness, at 3 GHz. The structure is cost-effective, passive, and single-layer, making it suitable for integration in space-constrained S-band applications.

Accurate soil moisture monitoring is vital for precision agriculture, flood prediction, geoscience and environmental management, but existing In-Ground Sensor Systems (IGSS) are limited by high cost, complexity, power consumption, and scalability issues. This paper presents a novel, low-cost, non-intrusive hybrid solution that estimates real-time soil moisture and detects floods using Bluetooth Low Energy (BLE) RSSI signals. The approach combines in-situ sensing for deep soil layers with remote sensing for shallow layers, overcoming key limitations of traditional IGSS. In this approach, the BLE communication infrastructure serves a dual purpose: transmitting deep soil sensor data while simultaneously enabling soil moisture estimation through variations in the Received Signal Strength Indicator (RSSI), implementing the concept of joint communication and sensing (JCAS). Without deploying additional sensors, both shallow soil moisture and surface water levels can be inferred from the RSSI fluctuations. A slave BLE module (SBM) is strategically positioned at the boundary between the shallow and deep soil layers, transmitting data to a master BLE module (MBM) located above ground. Machine Learning (ML) algorithms are applied for data fusion and signal interpretation, enabling dynamic soil moisture estimation and real-time flood detection. The MBM can be mounted on drones for large-scale farms or fixed on stands for greenhouses, offering a cost-effective, low-power, and scalable solution. This JCAS framework provides continuous, real-time monitoring, significantly improving the efficiency and accuracy of soil moisture estimation and flood prediction compared to traditional methods.