Search Results (281)

Spaceborne Synthetic Aperture Radar (SAR) can be applied for monitoring tropical cyclones (TCs), but co-polarized C-band SAR suffers from signal saturation such that it is improper for high wind-speed conditions. In contrast, cross-polarized SAR data does not suffer from this issue, but the retrieval algorithm needs more deliberation. Previously, many geophysical model functions (GMFs) have been developed using cross-polarized data, which obtain wind speeds using the complex relationships described by radar backscatter, incidence angle, wind direction, and radar look direction. In this regard, the rapid development of artificial intelligence technology has provided versatile machine learning methods for such a nonlinear inversion problem. In this study, we comprehensively compare the wind-speed retrieval performance of several models including Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN), which were developed based on spatio-temporal matching and correlation analysis of stepped frequency microwave radiometer (SFMR) and dual-polarized Sentinel-1 SAR data after noise removal. A data set with ~2800 samples is generated during TCs for training and validating the inversion model. The generalization ability of different models is tested by the reserved independent data. When using similar parameters with GMFs, RF inversion has the highest accuracy with a Root Mean Square Error (RMSE) of 3.40 m/s and correlation coefficient of 0.94. Furthermore, considering that the sea surface temperature is a crucial factor for generating TCs and influencing ocean backscattering, its effects on the proposed RF model are also explored, the results of which show improved wind-speed retrieval performances. Full article

Wheat powdery mildew poses a serious threat to crop growth and yield, highlighting the critical need for accurate detection to ensure food security and maintain agricultural productivity. This study explores the integration of polarization remote sensing with a Multi-Verse Optimizer (MVO)–enhanced Random Forest (RF) model for disease detection. Polarization imaging equipment was used to extract key polarization parameters, including the degree of polarization (DOP) and angle of polarization (AOP), from wheat leaves to capture subtle structural differences between healthy and diseased tissues. The MVO algorithm was employed to optimize RF hyperparameters, thereby improving classification performance and addressing the limitations of manual parameter tuning and conventional machine learning methods. Several machine learning algorithms were also evaluated for comparison. The results indicate that the proposed MVO_RF approach outperformed traditional methods, achieving an F1-score of 0.9715, a Kappa coefficient of 0.9797, and an overall accuracy of 0.9878. These findings demonstrate that the integration of polarization characteristics with MVO-optimized machine learning establishes a robust and efficient framework for monitoring wheat powdery mildew. More importantly, it facilitates early in-field disease warnings, enhances the accuracy and efficiency of targeted pesticide application, and offers quantitative decision-making support for smart agricultural management and disease prevention strategies. Full article

Soil organic carbon (SOC) is a critical component of soil health, influencing soil structure, soil water retention capacity, and nutrient cycling while playing a key role in the global carbon cycle. Accurate SOC estimation over croplands is essential for sustainable land management and climate change mitigation. This study explores a novel approach to SOC estimation using multi-frequency synthetic aperture radar (SAR) data, specifically Sentinel-1 and ALOS-2/PALSAR-2 imagery, combined with advanced machine learning techniques for cropland SOC estimation. Diverse agricultural practices, with major crop types such as rice (Oryza sativa), finger millet (Eleusine coracana), Niger (Guizotia abyssinica), maize (Zea mays), and vegetable cultivation, characterize the study region. By integrating C-band (Sentinel-1) and L-band (ALOS-2/PALSAR-2) SAR data with key polarimetric features such as the C2 matrix, entropy, and degree of polarization, this study enhances SOC estimation. These parameters help distinguish variations in soil moisture, texture, and mineral composition, reducing their confounding effects on SOC estimation. An ensemble model incorporating Random Forest (RF) and neural networks (NNs) was developed to capture the complex relationships between SAR data and SOC. The NN component effectively models complex non-linear relationships, while the RF model helps prevent overfitting. The proposed model achieved a correlation coefficient (r) of 0.64 and a root mean square error (RMSE) of 0.18, demonstrating its predictive capability. In summary, our results offer an efficient approach for enhanced SOC mapping in diverse agricultural landscapes, with ongoing work targeting challenges in data availability to facilitate large-scale SOC mapping. Full article

Orthomode transducers (OMTs) are critical for communications, enabling frequency reuse through orthogonal polarization separation, yet traditional manufacturing faces challenges in cost, weight, and complexity at high frequencies. This study explores additive manufacturing (AM) for fabricating high-performance OMTs for X-band and Ka-band applications. Two turnstile-junction-based OMTs were designed, optimizing a symmetrical architecture for low return loss, high isolation, and broadband operation. Both OMTs were fabricated using 3D printing technologies, with a comparative analysis of different AM techniques on performance. Measurements validated simulation results, achieving good return loss for both OMTs and isolation above 35 dB for the X-band and above 25 dB for the Ka-band. These designs meet the requirements of modern communication antenna feed systems across multiple frequency bands. Additionally, 3D printing demonstrates the promising potential of AM in RF component manufacturing, offering performance comparable to traditional metal-machined parts. Full article

This work investigates and compare the corrosion behavior in artificial saliva of oxide thin films grown on commercially pure titanium (cp-Ti), via electrochemical oxidation (EO) in sulphate bath at 1 V and thermal treatment (TT) at 450 °C, for durations between 20 min and 4 h. The goal is to determine which method and duration provide the optimal protection for titanium against degradation in dental environment particularly in varying fluoride concentration and acidity. Surface characterizations were performed through morphological and microstructural analysis using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD). Electrochemical behavior was conducted in Fusayama-Meyer solution (pH = 6.50 and T = 37 °C) using potentiodynamic polarization curve (PPC) and electrochemical impedance spectroscopy (EIS), under varying pH and fluoride ion concentrations. The results demonstrated that a 3-h duration treatment provided the optimal corrosion resistance for both EO and TT processes. The pH of the environment influenced corrosion performance markedly: both acidic (pH 2.5) and basic (pH 9.0) conditions increased I_corr and decreased R_p, indicating degradation of the passive oxide layer outside neutral conditions. Similarly, increasing fluoride concentrations (1000; 5000; and 12,300 ppm) significantly impaired corrosion resistance. At 12,300 ppm F⁻, untreated Ti showed severe degradation, with EIS revealing the formation of a porous outer layer and a weakened inner barrier layer (R_f = 33 W·cm² for the outer layer and R_ct = 21 kW·cm² for the barrier layer). In contrast, the TT-treated surface remained highly protective even under these aggressive conditions, with minimal surface damage and the highest resistances for both the outer and the inner layers (R_f = 1610 kW·cm²; R_ct = 1583 kW·cm²), significantly outperforming the EO film. These findings highlight the superior performance of thermal oxidation at 450 °C for 3 h as a promising surface treatment for enhancing the corrosion resistance of titanium in fluoride-rich oral environments. Understanding these strategies helps improve the longevity and security of titanium dental implants. Full article

Rice is one of the most important staple crops in China, and the rapid and accurate extraction of rice planting areas plays a crucial role in the agricultural management and food security assessment. However, the existing rice field identification methods faced the significant challenges in mountainous regions due to the severe cloud contamination, insufficient utilization of multi-dimensional features, and limited classification accuracy. This study presented a novel rice field identification method based on the Graph Convolutional Networks (GCN) that effectively integrated multi-source remote sensing data tailored for the complex mountainous terrain. A coarse-to-fine cloud removal strategy was developed by fusing the synthetic aperture radar (SAR) imagery with temporally adjacent optical remote sensing imagery, achieving high cloud removal accuracy, thereby providing reliable and clear optical data for the subsequent rice mapping. A comprehensive multi-feature library comprising spectral, texture, polarization, and terrain attributes was constructed and optimized via a stepwise selection process. Furthermore, the 19 key features were established to enhance the classification performance. The proposed method achieved an overall accuracy of 98.3% for the rice field identification in Huoshan County of the Dabie Mountains, and a 96.8% consistency compared to statistical yearbook data. The ablation experiments demonstrated that incorporating terrain features substantially improved the rice field identification accuracy under the complex topographic conditions. The comparative evaluations against support vector machine (SVM), random forest (RF), and U-Net models confirmed the superiority of the proposed method in terms of accuracy, local performance, terrain adaptability, training sample requirement, and computational cost, and demonstrated its effectiveness and applicability for the high-precision rice field distribution mapping in mountainous environments. Full article

The Shizhuyuan polymetallic deposit in Hunan Province, China, is a world-class ore field rich in tungsten (W), tin (Sn), molybdenum (Mo), and bismuth (Bi), now facing resource depletion due to prolonged exploitation. This study addresses the limitations of traditional geological prediction methods in complex terrain by integrating multi-source datasets—including γ-ray spectrometry, high-precision magnetometry, induced polarization (IP), and soil radon measurements—across 5049 samples. Unsupervised factor analysis was employed to extract five key ore-indicating factors, explaining 82.78% of data variance. Based on these geological features, predictive models including Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were constructed and compared. SHAP values were employed to quantify the contribution of each geological feature to the prediction outcomes, thereby transforming the machine learning “black-box models” into an interpretable geological decision-making basis. The results demonstrate that machine learning, particularly when integrated with multi-source data, provides a powerful and interpretable approach for deep mineral prospectivity mapping in concealed terrains. Full article

This paper presents the design, manufacturing, testing, and validation of the MASKARA (Multiple Apertures for high-resolution SAR based on Ka-band Reflectarray) Breadboard Model (BBM), a large Ka-band reflectarray antenna developed for Synthetic Aperture Radar (SAR) applications. The BBM features a dual-offset antenna configuration intended for a high-resolution, wide-swath SAR instrument. At the core of the system is a 1.5 m × 0.55 m reflectarray operating between 35.5–36.0 GHz in the Ka-band. To our knowledge, this is the first demonstration of a reflectarray antenna designed to support two distinct modes of operation, exploiting the inherent advantages of reflectarrays—such as reduced cost and compact stowage—over traditional solutions. The antenna provides a high-resolution mode requiring a higher-gain beam in one polarization and a low-resolution mode covering a larger swath with broader beam coverage in the orthogonal polarization. The design process follows a holistic, multidisciplinary approach, integrating RF and thermomechanical considerations through iterative and concurrent design reviews. The BBM has been successfully manufactured and experimentally tested, and the measurement results show good agreement with simulations, confirming the validity of the proposed concept and demonstrating its potential for future high-performance SAR missions. Full article

A bias-free antenna tuning technique that eliminates conventional DC biasing networks is presented. The tuning mechanism is based on a Light-Dependent Resistor (LDR) embedded within the antenna structure. Optical illumination is used to modulate the LDR’s resistance, thereby altering the antenna’s effective electrical length and enabling tuning of its resonant frequency and operating bands. By removing the need for bias lines, RF chokes, blocking capacitors, and control circuitry, the proposed approach minimizes parasitic effects, losses, biasing energy, and routing complexity. This makes it particularly suitable for compact and energy-constrained platforms, such as Internet of Things (IoT) devices. As proof of concept, an LDR is integrated into a ring monopole antenna, achieving tri-band operation in both high and low resistance states. In the high-resistance (OFF) state, the fabricated prototype operates across 2.1–3.1 GHz, 3.5–4 GHz, and 5–7 GHz. In the low-resistance (ON) state, the LDR bridges the two arcs of the monopole, extending the current path and shifting the lowest band to 1.36–2.35 GHz, with only minor changes to the mid and upper bands. The antenna maintains linear polarization across all bands and switching states, with measured gains reaching up to 5.3 dBi. Owing to its compact, bias-free, and low-cost architecture, the proposed design is well-suited for integration into portable wireless devices, low-power IoT nodes, and rapidly deployable communications systems where electrical biasing is impractical. Full article

With the booming development of the 5G market in recent years, super-high frequency (SHF) resonators will play an increasingly critical role in 5G and future communication systems. Facing the growing market demand for miniaturized, high-bandwidth, and low insertion loss filters, the design of SHF resonators and filters with a high effective electromechanical coupling coefficient (K²_eff) and quality factor, low insertion loss, high passband flatness, strong out-of-band rejection, and high power handling capacity has placed high demands on piezoelectric material preparation, process optimization, and resonator design. The polarity-inverted Al(Sc)N multilayer substrate has become one of the key solutions for SHF resonators. This review provides a comprehensive overview of the recent advances in SHF Al(Sc)N bulk acoustic wave (BAW) resonators. It systematically discusses the device design methodologies, structural configurations, and material synthesis techniques for high-quality Al(Sc)N thin films. Particular emphasis is placed on the underlying mechanisms and engineering strategies for polarity control in Al(Sc)N-based periodically poled multilayer structures. The progress in periodically poled piezoelectric film (P3F) BAW resonators is also examined, with special attention to their ability to significantly boost the operating frequency of BAW devices without reducing the thickness of the piezoelectric layer, while maintaining a high K²_eff. Finally, the review outlines current challenges and future directions for achieving a higher quality factor (Q), improved frequency scalability, and greater integration compatibility in SHF acoustic devices, paving the way for next-generation radio frequency (RF) front-end technologies in 5G/6G and beyond. Full article

This paper proposes a new behavioral model for radio-frequency power amplifiers (RF PAs) by extending the two-dimensional Polar Volterra series to fractional derivative order, using a numerical Mittag–Leffler-based formulation of fractional orthonormal generating functions. The motivation stems from the increasing need for accurate and computationally efficient models to represent nonlinearities and memory effects in wideband RF PAs, especially in energy-efficient 5G systems. The proposed method significantly reduces model complexity by lowering the number of estimated parameters while maintaining or improving modeling fidelity. To evaluate its performance, three different RF PA devices were used as test cases. The results demonstrated that the proposed approach achieved an over 81.5% reduction in the number of model parameters and improved modeling accuracy. Besides that, in a scenario with the same number of parameters, normalized mean square error (NMSE) gains of up to 8.72 dB were obtained. These findings support the method’s potential for practical use in RF PA behavioral modeling and digital predistortion applications. Full article

Dynamic environments pose significant challenges for visual SLAM, including feature ambiguity, weak textures, and map inconsistencies caused by moving objects. We present a robust SLAM framework integrating image enhancement, a mixed-precision quantized feature detection network, semantic-driven dynamic feature filtering, and NeRF-based static scene reconstruction. The system reliably extracts features under challenging conditions, removes dynamic points using instance segmentation combined with polar geometric constraints, and reconstructs static scenes with enhanced structural fidelity. Extensive experiments on TUM RGB-D, BONN RGB-D, and a custom dataset demonstrate notable improvements in the RMSE, mean, median, and standard deviation. Compared with ORB-SLAM3, our method achieves an average RMSE reduction of 93.4%, demonstrating substantial improvement, and relative to other state-of-the-art dynamic SLAM systems, it improves the average RMSE by 49.6% on TUM and 23.1% on BONN, highlighting its high accuracy, robustness, and adaptability in complex and highly dynamic environments. Full article

Accurate and non-invasive assessment of fat and muscle composition is crucial for biomedical monitoring to track health conditions in humans and pets, as well as for classifying meats in the meat industry. This study introduces a cost-effective, multifunctional ultra-wideband microwave system operating from 2.4 to 4.4 GHz, designed for rapid and non-destructive quantification of fat thickness, muscle thickness, and fat-to-muscle ratio in diverse ex vivo samples, including pork, beef, and oil–water mixtures. The compact handheld device integrates essential RF components such as a frequency synthesizer, directional coupler, logarithmic power detector, and a dual-polarized Vivaldi antenna. Bluetooth telemetry enables seamless real-time data transmission to mobile- or PC-based platforms, with each measurement completed in a few seconds. To enhance signal quality, a two-stage denoising pipeline combining low-pass filtering and Savitzky–Golay smoothing was applied, effectively suppressing noise while preserving key spectral features. Using a random forest regression model trained on resonance frequency and signal-loss features, the system demonstrates high predictive performance even under limited sample conditions. Correlation coefficients for fat thickness, muscle thickness, and fat-to-muscle ratio consistently exceeded 0.90 across all sample types, while mean absolute errors remained below 3.5 mm. The highest prediction accuracy was achieved in homogeneous oil–water samples, whereas biologically complex tissues like pork and beef introduced greater variability, particularly in muscle-related measurements. The proposed microwave system is highlighted as a highly portable and time-efficient solution, with measurements completed within seconds. Its low cost, ability to analyze multiple tissue types using a single device, and non-invasive nature without the need for sample pre-treatment or anesthesia make it well suited for applications in agri-food quality control, point-of-care diagnostics, and broader biomedical fields. Full article

Arctic sea ice can be regarded as a sensitive indicator of climate change, and it has declined dramatically in recent decades. The swift decline in Arctic sea ice coverage leads to an expansion of the marginal ice zone (MIZ). In this study, an ice-based buoy with an imaging system is designed for the long-term observation of the changes in sea ice from the packed ice zone to the marginal ice zone in polar regions. The system composition, main buoy, image system, and buoy load were analyzed. An underwater camera supports a 640 × 480 resolution image acquisition, RS485 communication, stable operation at –40 °C, and long-term underwater sealing protection through a titanium alloy housing. During a continuous three-month field deployment in the Arctic, the system successfully captured images of ice-bottom morphology and biological attachment, demonstrating imaging reliability and operational stability under extreme conditions. In addition, the buoy employed a battery state estimation method based on the Extreme Learning Machine (ELM). Compared with LSTM, BP, BiLSTM, SAELSTM, and RF models, the ELM achieved a test set performance of RMSE = 0.05 and MAE = 0.187, significantly outperforming the alternatives and thereby improving energy management and the reliability of long-term autonomous operation. Laboratory flume tests further verified the power generation performance of the wave energy-assisted supply system. However, due to the limited duration of Arctic deployment, full year-round performance has not yet been validated, and the imaging resolution remains insufficient for biological classification. The results indicate that the buoy demonstrates strong innovation and application potential for long-term polar observations, while further improvements are needed through extended deployments and enhanced imaging capability. Full article

Accurate crop mapping is vital for monitoring agricultural resources, food security, and ecosystem sustainability. Advances in high-resolution sensing technologies now enable precise, large-scale crop mapping, improving agricultural management and decision-making. However, in scenarios where balancing precision and computational resources is important, obtaining the optimal feature combination (especially newly proposed features) and strategies from the rich feature sets contained in multi-source remote sensing imagery remains one of the challenges. In this paper, we propose a hierarchical feature optimization method, incorporating a newly reported vegetation feature, for mapping crop types by combining the Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery within the Google Earth Engine (GEE) platform. The method first calculates spectral features, texture features, polarization features, vegetation index features, and crop phenological features, with a particular focus on infrared band features and the newly developed Kernel Normalized Difference Vegetation Index (kNDVI). These 126 features are then selected to construct 15 crop type mapping models based on different feature combinations and a random forest (RF) classifier. Feature selection was performed using the feature correlation analysis and random forest recursive feature elimination (RF-RFE) to identify the optimal subset. The experiment was conducted in the Linhe region, covering an area of 2333 km². The resulting 10 m crop map, generated by the optimal model (Model 15) with 34 key features, demonstrated that integrating multi-source features significantly enhances mapping accuracy. The model achieved an overall accuracy of 90.10% across five crop types (corn, wheat, sunflower, soybean, and beet), outperforming other representative feature optimization methods, Relief-F (87.50%) and CFS (89.60%). The study underscores the importance of feature optimization and reduction of redundant features while also showcasing the effectiveness of red edge and infrared features, as well as the kNDVI, in mapping crop type. Full article