Search Results (5,887)

Female soccer is rapidly becoming a widely practiced sport at different levels: this opens up a new demand for systems meant to protect athletes from head impacts or to monitor their effects. The market is offering some solutions in similar sports, but the specificity and high relevance of soccer encourage the development of a dedicated solution. From market analysis, technology scouting, and ethnographic research a set of functional and technical requirements have been defined and proposed. The designed instrumented head band is equipped with one Inertial Measurement Unit (IMU) in the occipital area and four contact pressure sensors on the sides. The concept design is low-cost and open-architecture, prioritizing accessibility over complexity. The modularity also ensures that each component (sensing, battery, communication) can be replaced or upgraded independently, enabling iterative refinement and integration into future sports safety systems. In addition to safety monitoring for injury prevention or detection of the traumatic impact, the system is relevant for supporting performance monitoring, rehabilitation or post-injury recovery and other important applications. System engineering has started and the next step is building the prototypes for testing and validation. Full article

This paper proposes a compact UHF microstrip divider with wideband harmonic suppression. A combined resonator, formed by a T-shaped resonator and a pair of coupled compact microstrip resonant cells (CCMRCs), is embedded into each divider branch to replace the conventional quarter-wavelength transmission lines. The divider is designed on an FR4 substrate (ε_r = 4.4, thickness = 60 mil) for a center frequency of 570 MHz. Full-wave electromagnetic simulations indicate equal power division at 570 MHz with return loss better than 39 dB and output-port isolation higher than 47 dB. Moreover, a wide stopband from 1.5 GHz to 3.5 GHz is obtained, yielding strong attenuation for the third-to-sixth harmonics. The proposed layout occupies 19.6 mm × 21.6 mm, which is about 76% smaller than a conventional 570 MHz divider (42.7 mm × 41 mm). The proposed design is suitable for modern wireless communication systems. Full article

Compact antennas with ultra-wideband operation and stable radiation are essential for portable and airborne ground-penetrating radar (GPR), yet miniaturization in the sub 3 GHz region is strongly constrained by the wavelength-driven aperture requirement and often leads to impedance discontinuity and radiation instability. This paper presents a compact aperture-slot antipodal Vivaldi antenna (AS-AVA) designed under a radiation stability-driven co-design strategy, where the miniaturization features are organized along the energy propagation path from the feed to the flared aperture. The proposed structure combines (i) aperture-slot current-path engineering with controlled meandering to extend the low-frequency edge, (ii) four tilted rectangular slots near the aperture to restrain excessive edge currents and suppress sidelobes, and (iii) back-loaded parasitic patches for coupling-based impedance refinement to eliminate residual mismatch pockets. A fabricated prototype on FR-4 (thickness 1.93 mm) occupies $111.15\times 156.82$ mm² and achieves a measured $S_{11}$ below $-10$ dB from 0.63 to 2.03 GHz (fractional bandwidth 105.26%). The measured realized gain increases from 2.1 to 7.5 dBi across the operating band, with stable far-field radiation patterns; the group delay measured over 0.6–2.1 GHz remains within 4–8 ns, indicating good time-domain fidelity for stepped-frequency continuous-wave (SFCW) operation. Finally, the antenna pair is integrated into an SFCW-GPR testbed and validated in sandbox and outdoor experiments, where buried metallic targets and a subgrade void produce clear B-scan signatures after standard processing. These results confirm that the proposed AS-AVA provides a practical trade-off among miniaturization, broadband matching, and radiation robustness for compact sub 3 GHz GPR platforms. Full article

We present an advanced statistical framework for estimating the relative intensity of astrophysical event distributions (e.g., Gamma-Ray Bursts, GRBs) on the sky tofacilitate population studies and large-scale structure analysis. In contrast to the traditional approach based on the ratio of Kernel Density Estimation (KDE), which is characterized by numerical instability and bandwidth sensitivity, this work applies a logistic regression embedded in a Bayesian framework to directly model selection effects. It reformulates the problem as a logistic regression task within a Generalized Additive Model (GAM) framework, utilizing isotropic Splines on the Sphere (SOS) to map the conditional probability of redshift measurement. The model complexity and smoothness are objectively optimized using Restricted Maximum Likelihood (REML) and the Akaike Information Criterion (AIC), ensuring a data-driven bias-variance trade-off. We benchmark this approach against an Adaptive Kernel Density Estimator (AKDE) using von Mises–Fisher kernels and Abramson’s square root law. The comparative analysis reveals strong statistical evidence in favor of this Preconditioned (Precon) Estimator, yielding a log-likelihood improvement of $\Delta \mathcal{L}\approx 74.3$ (Bayes factor $>{10}^{30}$) over the adaptive method. We show that this Precon Estimator acts as a spectral bandwidth extender, effectively decoupling the wideband exposure map from the narrowband selection efficiency. This provides a tool for cosmologists to recover high-frequency structural features—such as the sharp cutoffs—that are mathematically irresolvable by direct density estimators due to the bandwidth limitation inherent in sparse samples. The methodology ensures that reconstructions of the cosmic web are stable against Poisson noise and consistent with observational constraints. Full article

Direct-starting of industrial motors has problems such as large current impact (five to eight times the rated current), mechanical stress damage, and low energy efficiency. This paper explores the technological innovations in motor soft-start driven by intelligent control and wide-bandgap semiconductors, and constructs a highly reliable and low energy consumption solution. Firstly, based on a material–device–algorithm system framework, a comparative study is conducted on the performance breakthroughs of SiC/GaN in replacing silicon-based devices. Secondly, an intelligent control model is established and a highly reliable system architecture is developed. A comprehensive review of recent literature indicates that SiC devices can reduce switching losses by up to 80%, and intelligent algorithms significantly improve control accuracy. System-level solutions reported in the industry demonstrate the capability to limit current to 1.5–3 times the rated current and achieve substantial carbon emission reductions. These technologies provide key technical support for the intelligent upgrading of industrial motor systems and the dual-carbon goal. In the future, development will continue to evolve in the direction of device miniaturization and other directions. Full article

Accurate spatiotemporal forecasting underpins high-stakes decision making in smart urban systems, from traffic control and energy scheduling to environment monitoring. Yet two persistent gaps limit current models: (i) spatial modules are often biased toward low-pass smoothing and struggle to reconcile slow global trends with sharp local dynamics; and (ii) the graph structure required for forecasting is frequently latent, while learned graphs can be unstable when built from temporally derived node features alone. We propose SpeQNet, a query-enhanced spectral graph filtering framework that jointly strengthens node representations and graph construction while enabling frequency-selective spatial reasoning. SpeQNet injects global spatial context into temporal embeddings via lightweight learnable spatiotemporal queries, learns a task-oriented adaptive adjacency matrix, and refines node features with an enhanced ChebNetII-based spectral filtering block equipped with channel-wise recalibration and nonlinear refinement. Across twelve real-world benchmarks spanning traffic, electricity, solar power, and weather, SpeQNet achieves state-of-the-art performance and delivers consistent gains on large-scale graphs. Beyond accuracy, SpeQNet is interpretable and robust: the learned spectral operators exhibit a consistent band-stop-like frequency shaping behavior, and performance remains stable across a wide range of Chebyshev polynomial orders. These results suggest that query-enhanced spatiotemporal representation learning and adaptive spectral filtering form a complementary and effective foundation for effective spatiotemporal forecasting. Full article

The present work addresses the critical challenge of designing a lightweight antenna suitable for remote sensing applications specifically aimed at the identification of buried objects from Unmanned Aerial Vehicles (UAVs). The stability of the phase center and the radiation pattern are critical factors for enabling synthetic aperture radar (SAR) processing on moving platforms. The presented antenna structure is characterized by a simple, lightweight geometry, and allows for achieving a fractional bandwidth of nearly 100% with an excellent stability of the radiation pattern, that exhibits minimal variation within the operating band of the antenna. Specifically, the gain is in the range 4.4–6.3 dBi and the group delay spread is about 200 ps in the frequency range 1–2 GHz. We illustrate numerical simulations and measurements of an antenna prototype that validate the proposed approach, demonstrating the suitability of the design for the intended operational scenario. Full article

The spaceborne full-polarimetric (FP) synthetic aperture radar (SAR) is an advanced sensor for high-resolution Earth observation. However, FP data acquired by such a system are prone to distortions induced by ionospheric Faraday rotation (FR). From the perspective of exploiting these distortions, this enables the estimation of the ionospheric FR angle (FRA), and consequently the total electron content, across most global regions (including the extensive ocean areas) using spaceborne FP SAR measurements. The accuracy of FRA estimation, however, is highly sensitive to noise interference. This study addresses denoising in FRA retrieval based on the Bickel–Bates estimator, with a specific focus on noise reduction methods built upon the adaptive Goldstein filter (AGF) that was originally designed for radar interferometric processing. For the first time, three signal-to-noise ratio (SNR)-based AGFs suitable for FRA estimation are investigated. A key feature of these filters is that their SNRs are all defined using the amplitude of the Bickel–Bates estimator signal rather than the FRA estimates themselves. Accordingly, these AGFs are applied to the estimator signal instead of the estimated FRAs. Two of the three AGFs are developed by adopting the mathematical forms of SNRs and filter parameters consistent with the existing SNR-based AGFs for interferogram. The third AGF is newly proposed by utilizing more general mathematical forms of SNR and filter parameter that differ from the first two. Specifically, its SNR definition aligns with that widely used in image processing, and its filter parameter is derived as a function of the defined SNR plus an additionally introduced adjustable factor. The three SNR-based AGFs tailored for FRA estimation are tested and evaluated against existing AGF variants and classical image denoising methods using three sets of FP SAR Datasets acquired by the L-band ALOS PALSAR sensor, encompassing an ocean-only scene, a plain land–ocean combined scene, and a more complex land–ocean combined scene. Experimental results demonstrate that all three filters can effectively mitigate noise, with the newly proposed AGF achieving the best performance among all denoising methods included in the comparison. Full article

Cybersecurity in radio frequency (RF) technologies has become a critical concern, driven by the expansion of connected systems in urban and industrial environments. Although research on wireless networks and the Internet of Things (IoT) has advanced, comprehensive studies that provide a global and integrated view of cybersecurity development in this field remain limited. This work presents a scientometric and systematic review of international publications from 2009 to 2025, integrating the PRISMA protocol with semantic screening supported by a Large Language Model to enhance classification accuracy and reproducibility. The analysis identified two interdependent axes: one focusing on signal integrity and authentication in GNSS systems and cellular networks; the other addressing the resilience of IoT networks, both strongly associated with spoofing and jamming, as well as replay, relay, eavesdropping, and man-in-the-middle (MitM) attacks. The results highlight the relevance of RF cybersecurity in securing communication infrastructures and expose gaps in widely adopted technologies such as RFID, NFC, BLE, ZigBee, LoRa, Wi-Fi, and unlicensed ISM bands, as well as in emerging areas like terahertz and 6G. These gaps directly affect the reliability and availability of IoT and wireless communication systems, increasing security risks in large-scale deployments such as smart cities and cyber–physical infrastructures. Full article

In this paper, a flower-shaped spoof surface plasmon polaritons (SSPPs) unit with strong slow-wave effect is proposed to construct bandpass filters (BPFs). Benefiting from extended current path induced by addition of rotated stubs around rectangular unit, the proposed SSPPs unit exhibits reduced asymptotic frequency. Following this, a single-band filter boasting multiple transmission zeros (TZs) in its upper stopband is developed by embedding the unit into half-mode substrate integrated waveguide (HMSIW). To improve suppression of the lower stopband, a pair of open circuited stubs are loaded to produce TZs and enhance its frequency selectivity. Consequently, the single-band BPF realizes an impressive roll-off rate of 0.116 dB/MHz. Subsequently, geometric dimensions of the open-circuited stubs are modified to dispose the TZs into passband and acquire dual-band operation. In addition, defected ground structures (DGSs) are loaded to broaden the bandwidth of notch between two passbands. Finally, a dual-band filter with a wide suppression band of 0.50 GHz is developed. With roll-off rates of 0.096 and 0.119 dB/MHz, the filter demonstrates good selectivity as well. Full article

With the development of the Internet of Things (IoT) and smart industry, the demand for high-precision indoor positioning is becoming increasingly urgent. Ultra-ideband (UWB) technology has become a research hotspot due to its centimeter-level ranging accuracy, good penetration, and high multipath resolution. However, in complex environments, it still faces problems such as high cost of anchor node layout, gross errors in observation data, and difficulty in eliminating systematic errors such as electronic time delay. To address the aforementioned problems, this paper proposes a comprehensive UWB indoor positioning scheme. By constructing virtual reference stations to enhance the observation network, the geometric structure is optimized and the dependence on physical anchors is reduced. Combined with a gross error elimination method under short-baseline constraints and a double-difference positioning model including virtual observations, it systematically suppresses systematic errors such as electronic delay. Additionally, a quality control strategy with velocity constraints is introduced to improve trajectory smoothness and reliability. Static experimental results show that the proposed double-difference model can effectively eliminate systematic errors. For example, the positioning deviation in the Xdirection is reduced from approximately 2.88 cm to 0.84 cm, while the positioning accuracy in the Ydirection slightly decreases. Overall, the positioning accuracy is improved. The gross error elimination method achieves an identification efficiency of over 85% and an accuracy of higher than 99%, providing high-quality observation data for subsequent calculations. Dynamic experimental results show that the positioning trajectory after geometric enhancement of virtual reference stations and velocity-constrained quality control is highly consistent with the reference trajectory, with significantly improved trajectory smoothness and reliability. In summary, this study constructs a complete technical chain from data preprocessing to result quality control, effectively improving the accuracy and robustness of UWB positioning in complex indoor environments, and exhibits promising engineering application potential. Full article

Accurate quantification of forest aboveground biomass (AGB) is essential for monitoring terrestrial carbon stocks. While total AGB estimation is widely practiced, resolving component biomass such as canopy, branches, leaves, and trunks enhances the precision of carbon sink assessments and provides critical structural parameters for ecosystem modeling. Most studies rely on a single SAR sensor or a limited range of SAR features, which restricts their ability to represent vegetation structural complexity and reduces biomass estimation accuracy. Here, we propose a phased fusion strategy that integrates backscatter intensity, interferometric coherence, texture measures, and polarimetric decomposition parameters derived from dual-frequency ALOS-2, GF-3, and Sentinel-1A SAR data. These complementary multi-dimensional SAR features are incorporated into a Random Forest model optimized using an Adaptive Genetic Algorithm (RF-AGA) to estimate forest total and component estimation. The results show that the progressive incorporation of coherence and texture features markedly improved model performance, increasing the accuracy of total AGB to R² = 0.88 and canopy biomass to R² = 0.78 under leave-one-out cross-validation. Feature contribution analysis indicates strong complementarity among SAR parameters. Polarimetric decomposition yielded the largest overall contribution, while L-band volume scattering was the primary driver of trunk and canopy estimation. Coherence-enhanced trunk prediction increased R² by 13 percent, and texture improved canopy representation by capturing structural heterogeneity and reducing saturation effects. This study confirms that integrating coherence and texture information within the RF-AGA framework enhances AGB estimation, and that the differential contributions of multi-dimensional SAR parameters across total and component biomass estimation originate from their distinct structural characteristics. The proposed framework provides a robust foundation for regional carbon monitoring and highlights the value of integrating complementary SAR features with ensemble learning to achieve high-precision forest carbon assessment. Full article

This study presents a compact bulb-shaped ultra-wideband microstrip patch antenna designed for microwave imaging applications, more specifically, breast tumor detection. Traditional antenna design methods for medical applications are time-consuming. The proposed antenna, designed in CST Microwave Studio 2019 on a Rogers RT 5880 substrate with a slotted ground plane, achieves a bandwidth of 11.1 GHz, a gain of 6.2 dBi, and an efficiency above 80%. In response to the limitations of conventional antenna design approaches, this study introduces a novel machine learning-based approach to accelerate the design process, where a custom CatBoost model predicts key dimensions—feedline width, large circle radius, and small circle radius, based on the performance metrics such as resonant frequency, minimum reflection coefficient, bandwidth, real and imaginary part of impedance. The model achieves a cross-validation score of 95.13% with a mean absolute error of 0.0166 mm, outperforming conventional machine learning approaches. Shapley Additive exPlanations analysis is applied to interpret feature contributions. A prototype is fabricated using the prediction of a machine learning model. The bulb-shaped antenna structure, wide operational bandwidth, consistent gain, and strong sensitivity to tissue dielectric variations enhance its effectiveness for breast tumor detection compared with conventional antennas. Furthermore, experiments with a breast phantom confirmed the prototype’s suitability for detecting dielectric contrasts in tissue, establishing a foundation for machine learning-assisted antenna design in medical imaging. Full article

In the Internet of Things (IoT), ultra-wideband (UWB) plays an essential role in localization and navigation. However, in indoor environments, UWB signals are often blocked by obstacles, leading to non-line-of-sight (NLOS) propagation. Thus, reliable line-of-sight (LOS)/NLOS identification is essential for reducing errors and enhancing the robustness of localization. This paper focuses on a single-anchor UWB configuration and proposes a temporal deep learning framework that jointly exploits two-way ranging (TWR) and angle-of-arrival (AOA) measurements for LOS/NLOS identification. At the core of the model is a temporal convolutional network (TCN) augmented with a self-attentive pooling mechanism, which enables the extraction of dynamic propagation patterns and temporal contextual information. Experimental evaluations on real-world measurement data show that the proposed method achieves an accuracy of 96.65% on the collected dataset and yields accuracies ranging from 88.72% to 93.56% across the three scenes, outperforming representative deep learning baselines. These results indicate that jointly exploiting geometric and temporal information in a single-anchor configuration is an effective approach for robust UWB indoor positioning. Full article

There are observed unprecedented dynamics in transportation electrification—especially in electric vehicles (even being tested as autonomous units in some regions). The expected improvements in charging and driving distances strive toward higher power levels of passenger cars, public transportation, and trucks, thus leading to elevations of on-board voltage levels. It is expected that the kilovolt level will be crossed soon, thus implying testing at a few kV. To achieve efficient power conversion while maintaining high-power density, new classes of wide-band semiconductors are being implemented; however, fast-switching and ultra-short rise times may result in faster electrical insulation deterioration. The challenges and trends in the development of the high-voltage insulation of various EV components are analyzed. Insulation performance evaluation criteria are discussed, including partial discharges and monitoring approaches. In this context, the development of the transportation segment’s electrification is closely connected with high-voltage insulation problems. Full article