Search Results (289)

The optimization of high-frequency circuits remains a computationally intensive task due to the need for repeated high-fidelity electromagnetic (EM) simulations. To address this challenge, we propose a novel integration of machine learning-generated inverse maps within the space mapping (SM) optimization framework to significantly accelerate circuit optimization while maintaining high accuracy. The proposed approach leverages Bayesian Neural Networks (BNNs) and surrogate modeling techniques to construct an inverse mapping function that directly predicts design parameters from target performance metrics, bypassing iterative forward simulations. The methodology was validated using a low-pass filter optimization scenario, where the inverse surrogate model was trained using electromagnetic simulations from COMSOL Multiphysics 2024 r6.3 and optimized using MATLAB R2024b r24.2 trust region algorithm. Experimental results demonstrate that our approach reduces the number of high-fidelity simulations by over 80% compared to conventional SM techniques while achieving high accuracy with a mean absolute error (MAE) of 0.0262 (0.47%). Additionally, convergence efficiency was significantly improved, with the inverse surrogate model requiring only 31 coarse model simulations, compared to 580 in traditional SM. These findings demonstrate that machine learning-driven inverse surrogate modeling significantly reduces computational overhead, accelerates optimization, and enhances the accuracy of high-frequency circuit design. This approach offers a promising alternative to traditional SM methods, paving the way for more efficient RF and microwave circuit design workflows. Full article

High moisture content (MC) harms wheat storage quality and readily leads to mold growth. Accurate localization of abnormal/high-moisture regions enables early warning, ensuring proper storage and reducing economic losses. The present study introduces the 2D microwave scanning method and investigates a novel localization method for addressing such a challenge. Both static and scanning experiments were performed on a developed mobile and non-destructive microwave detection system to quantify the MC of wheat and then locate abnormal moisture regions. For quantifying the wheat’s MC, a dual-parameter wheat MC prediction model with the random forest (RF) algorithm was constructed, achieving a high accuracy (R² = 0.9846, MSE = 0.2768, MAE = 0.3986). MC scanning experiments were conducted by synchronized moving waveguides; the maximum absolute error of MC prediction was 0.565%, with a maximum relative error of 3.166%. Furthermore, both one- and two-dimensional localizing methods were proposed for localizing abnormal moisture regions. The one-dimensional method evaluated two approaches—attenuation value and absolute attenuation gradient—using computer simulation technology (CST) modeling and scanning experiments. The experimental results confirmed the superior performance of the absolute gradient method, with a center detection error of less than 12 mm in the anomalous wheat moisture region and a minimum width detection error of 1.4 mm. The study performed two-dimensional antenna scanning and effectively imaged the high-MC regions using phase delay analysis. The imaging results coincide with the actual locations of moisture anomaly regions. This study demonstrated a promising solution for accurately localizing the wheat’s abnormal/high-moisture regions with the use of an emerging microwave transmission method. Full article

This study evaluates machine learning-based methods for retrieving thin Arctic sea ice thickness (SIT) from L-band radiometry, using data from the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite. In addition to the operational ESA product, three alternative approaches are assessed: a Random Forest (RF) algorithm, a Convolutional Neural Network (CNN) that incorporates spatial coherence, and a Long Short-Term Memory (LSTM) neural network designed to capture temporal coherence. Validation against in situ data from the Beaufort Gyre Exploration Project (BGEP) moorings and the ESA SMOSice campaign demonstrates that the RF algorithm achieves robust performance comparable to the ESA product, despite its simplicity and lack of explicit spatial or temporal modeling. The CNN exhibits a tendency to overestimate SIT and shows higher dispersion, suggesting limited added value when spatial coherence is already present in the input data. The LSTM approach does not improve retrieval accuracy, likely due to the mismatch between satellite resolution and the temporal variability of sea ice conditions. These results highlight the importance of L-band sea ice emission modeling over increasing algorithm complexity and suggest that simpler, adaptable methods such as RF offer a promising foundation for future SIT retrieval efforts. The findings are relevant for refining current methods used with SMOS and for developing upcoming satellite missions, such as ESA’s Copernicus Imaging Microwave Radiometer (CIMR). Full article

Snow plays a crucial role in global climate regulation, hydrological processes, and environmental change, making the accurate acquisition of snow depth data highly significant. In this study, we used Sentinel-1 radar data and employed a simulated annealing algorithm to select the optimal influencing factors from radar backscatter characteristics and spatiotemporal geographical parameters within the study area. Snow depth retrieval was subsequently performed using both random forest (RF) and Support Vector Machine (SVM) models. The retrieval results were validated against in situ measurements and compared with the long-term daily snow depth dataset of China for the period 2017–2019. The results indicate that the RF model achieves better agreement with the measured data than existing snow depth products. Specifically, in the Xinjiang region, the RF model demonstrates superior performance, with an R² of 0.92, a root mean square error (RMSE) of 2.61 cm, and a mean absolute error (MAE) of 1.42 cm. In contrast, the SVM regression model shows weaker agreement with the observations, with an R² lower than that of the existing snow depth product (0.51) in Xinjiang, and it performs poorly in other regions as well. Overall, the SVM model exhibits deficiencies in both predictive accuracy and spatial stability. This study provides a valuable reference for snow depth retrieval research based on active microwave remote sensing techniques. Full article

XSPICE models for a generic transmission line, a microstrip line, and coupled microstrips are presented. The developed models extend general-purpose circuit simulation tools using RF circuits design features. The models could be used for circuit simulation in frequency, DC, and time domains for any active or passive RF or microwave schematic (including microwave monolithic integrated circuits—MMICs) involving transmission lines. The presented models could be used with any circuit simulation backend supporting XSPICE extensions and could be integrated without patching the core simulator code. The presented XSPICE models for microstrip lines take into account the frequency dependency of characteristic impedance and dispersion. The models were designed using open-source circuit simulation software. This study provides a practical example of the low-noise RF amplifier (LNA) design with Ngspice simulation backend using the proposed models. Full article

Microwave energy is ideal for wearable devices due to its stable wireless power transfer capabilities. However, rigid receiving antennas in conventional RF energy harvesters compromise wearability. This study presents a wearable system using a flexible dual-band antenna (915 MHz/2.45 GHz) fabricated via conformal 3D printing on arm-mimicking curvatures, minimizing bending-induced performance loss. A hybrid microstrip–lumped element rectifier circuit enhances energy conversion efficiency. Tested with commercial 915 MHz transmitters and Wi-Fi routers, the system consistently delivers 3.27–3.31 V within an operational range, enabling continuous power supply for real-time physiological monitoring (e.g., pulse detection) and data transmission. This work demonstrates a practical solution for sustainable energy harvesting in flexible wearables. Full article

This systematic review explores recent advancements in antenna and rectifier systems for radio frequency (RF) energy harvesting within the gigahertz frequency range, aiming to support the development of sustainable and efficient low-power electronic applications. Conducted under the PRISMA methodology, our review filtered 2465 initial records down to 80 relevant studies, addressing three research questions focused on antenna design, operating frequency bands, and rectifier configurations. Key variables such as antenna type, resonant frequency, gain, efficiency, bandwidth, and physical dimensions were examined. Antenna designs including fractal, spiral, bow-tie, slot, and rectangular structures were analyzed, with fractal antennas showing the highest efficiency, while array antennas exhibited lower performance despite their compact dimensions. Frequency band analysis indicated a predominance of 2.4 GHz and 5.8 GHz applications. Evaluation of substrate materials such as FR4, Rogers, RT Duroid, textiles, and unconventional composites highlighted their impact on performance optimization. Rectifier systems including Schottky, full-wave, half-wave, microwave, multi-step, and single-step designs were assessed, with Schottky rectifiers demonstrating the highest energy conversion efficiency. Additionally, correlation analyses using boxplots explored the relationships among antenna area, efficiency, operating frequency, and gain across design variables. The findings identify current trends and design considerations crucial for enhancing RF energy harvesting technologies. Full article

This paper presents the development and analysis of a planar microfluidic microwave sensor featuring three circular complementary split-ring resonators (CSRRs) fabricated on an RO3035 substrate. The sensor demonstrates enhanced sensitivity in characterizing liquids contained in a fine glass capillary tube by leveraging a novel configuration: a central 5-split-ring CSRR with a drilled hole to suspend the capillary, flanked by two 2-split-ring CSRRs to improve the band-stop filtering effect. The sensor’s performance is benchmarked against another CSRR-based microwave sensor with a similar configuration. High linearity is observed (R² > 0.99), confirming its capability for precise ethanol concentration prediction. Compared to the replicated square CSRR design from the literature, the proposed sensor achieves a 35.22% improvement in sensitivity, with a frequency shift sensitivity of 567.41 kHz/% ethanol concentration versus 419.62 kHz/% for the reference sensor. The enhanced sensitivity is attributed to several key design strategies: increasing the intrinsic capacitance by enlarging the effective area and radial slot width to amplify edge capacitive effects, adding more split rings to intensify the resonance dip, placing additional CSRRs to improve energy extraction at resonance, and adopting circular CSRRs for superior electric field confinement. Additionally, the proposed design operates at a lower resonant frequency (2.234 GHz), which not only reduces dielectric and radiation losses but also enables the use of more cost-effective and power-efficient RF components. This advantage makes the sensor highly suitable for integration into portable and standalone sensing platforms. Full article

This paper presents a comprehensive methodology for impedance matching in analog circuits, integrating analytical methods with computer-aided design techniques. It focuses on maximizing power transfer through impedance adaptation and emphasizes the practical utility of the Smith chart for identifying optimal matching configurations. This study examines various impedance matching topologies—including L, T, and Pi networks—with an emphasis on using reactive components such as capacitors and inductors. A MATLAB-based tool is developed to automate the synthesis of matching networks, providing four equivalent circuit solutions for each scenario. Illustrative examples and simulations confirm the method’s efficiency, flexibility, and applicability to a broad range of radiofrequency (RF), microwave, and wireless power transfer systems. Full article

The study of the effects of 2.45 GHz electromagnetic fields on the health and safety of people and organisms as a whole is essential due to their widespread use in everyday life. It is known that they can cause thermal and non-thermal effects—at the molecular, cellular and organismal level. Yeast suspensions were treated with 2.45 GHz microwave radiation in the near-field of antenna at two distances (2 and 4 cm) and two time periods (20 and 60 min)—setups resembling the use of mobile devices. The release of UV-absorbing substances from the cells was studied as an indicator of membrane permeabilization, total intracellular antioxidant activity and reduced glutathione were determined, and a comet assay for damage to the DNA was performed. A correlation between reduced antioxidants and increased membrane permeability during EMF treatment was observed at a distance of 2 cm for 20 min, suggesting the presence of oxidative stress, while a similar effect was not observed with conventional heating. Slightly increased membrane permeability was observed after irradiation for 60 min at a distance of 4 cm, but this was not related to the antioxidant status of the cells. A trend towards increased DNA damage was observed under both conditions. Full article

Hydrogen is gaining prominence as a clean energy vector, yet its extreme flammability demands robust detection solutions for industrial safety. In this study, we present the development and experimental validation of a microwave hydrogen gas sensor based on a patch-type microstrip antenna with a silver sensing element. The device operates at 5.99 GHz and was tested under controlled environmental conditions (humidity: 20 ± 0.4%, temperature: 27 ± 0.2 °C). Hydrogen exposure induces measurable shifts in the antenna’s resonant frequency due to dielectric modulation of the silver layer. The sensor exhibited a linear sensitivity of 3 kHz/ppm in the 310–600 ppm concentration range, with a residual standard deviation of 31.1 kHz and a calculated limit of detection (LOD) of approximately 31 ppm. The reflection coefficient remained below −10 dB throughout, confirming that the antenna maintains functional RF performance during sensing. These results demonstrate the sensor’s dual functionality for gas detection and communication, offering a compact and scalable platform for hydrogen safety monitoring. Full article

The Total Precipitable Water (TPW) is a key variable of atmospheres, and its spatiotemporal distribution is of great importance in global climate change. This paper addresses the TPW retrieval over both sea and land surfaces from the data acquired by the Microwave Humidity Sounder II (MWHS-II) on Fengyun 3D (FY-3D) satellite. First, the Back Propagation Neural Network (BPNN) algorithms are developed with the spatiotemporal matching samples of the MWHS-II data with the fifth-generation European Centre for Medium-Range Weather Forecast (ECMWF) atmospheric reanalysis (ERA5) data. Then, the TPWs at spatial resolutions of 0.25° in longitude and latitude between 65°S and 65°N over both sea and land surfaces are retrieved from the pixel-aggregated FY-3D MWHS-II data in 2022. Finally, the TPWs retrieved in this work are validated with the radiosonde TPWs over both sea and land surfaces, and they are also compared to the F18 Special Sensor Microwave Imager Sounder (SSMIS) TPWs over sea surfaces. The results indicate that the BPNN algorithms developed in this work are valid and superior to the D-matrix method, the Ridge method, the Lasso method, the physical method, the random forest (RF) method, the support vector machine (SVM) method, and the eXtreme Gradient Boosting (XGBoost) method. Against the radiosonde TPWs, the mean error (ME), the root mean square error (RMSE), and mean absolute error (MAE) of the TPWs retrieved in this work are −1.17 mm, 3.46 mm, and 2.63 mm over sea surfaces, respectively, and they are −0.80 mm, 4.04 mm, and 3.13 mm over land surfaces, respectively. The TPWs retrieved in this work are much more accurate than the F18 SSMIS TPWs. Full article

Additive manufacturing is currently regarded as one of the enabling technologies for Space Economy since it allows for the reduction of lead time and costs of payloads and platforms. Typically, metal-based additive manufacturing technologies are considered for the development of microwave components for Space applications since they exhibit the best trade-off in radio-frequency performance, benefits, and withstanding adverse environmental conditions. In this view, composite polymers may further increase the benefits arising from the 3D printing of microwave components since lighter parts with the required thermal, mechanical, and RF performances can be placed on board satellites. This paper explores the feasibility of 3D-printed composite polymers, including Ultem and PEEK reinforced with carbon fiber, for the development of microwave waveguide devices intended for Space applications. To this end, three different manufacturing routes were investigated by selecting a specific composite polymer, the corresponding manufacturing system and post-processing, and the necessary metal-plating technique. Hence, relevant radio-frequency test vehicles operating at 10 ÷ 14 GHz were designed, manufactured, and tested. The experimental results prove that waveguide components operating in X and Ku bands can be developed through the material extrusion of PEEK reinforced with carbon fiber, which is subsequently metalized by means of a two-stage electroless/electroplating process. Full article

This paper presents the integration of memristors into N-path filter architectures to develop reconfigurable N-path filters with a tuneable bandwidth. Two different memristor-based N-path filter designs are proposed and systematically compared. One of the architectures was experimentally validated by interfacing it with a memristor package in a laboratory environment, demonstrating a tuneable bandwidth ranging from 1.5 kHz to 2 kHz at a centre frequency of 1 MHz, corresponding to a tuneable quality factor (Q factor) of between 500 and 667. Additionally, this design enables centre frequency tuning from 0.9 MHz to 1.2 MHz while maintaining a fixed Q factor of 600. The second architecture was evaluated through simulations in the Cadence Virtuoso environment using a memristor model. The results indicate a tuneable bandwidth from 0.99 MHz to 1.38 MHz at a centre frequency of 1 GHz, corresponding to a tuneable Q factor ranging from 730 to 1010. Furthermore, this design allows the centre frequency to be adjusted within the range of 0.99 GHz to 1.38 GHz while preserving a fixed Q factor of 1000. These findings highlight the potential of memristor-based N-path filters in achieving reconfigurable and high-Q filtering capabilities for RF applications. Full article