Search Results (458)

Vision-based pose estimation has been widely applied in unmanned aerial vehicle (UAV) navigation. However, existing visual pose estimation algorithms are highly sensitive to camera imaging distortion, which degrades estimation accuracy, and often suffer from noticeable jitter between frames in dynamic scenarios. To address these issues, this paper proposes an improved visual pose estimation algorithm built upon the Perspective Similar Triangle (PST) geometric model. Using a planar fiducial marker as the observation target, the single-frame pose estimation problem is reformulated as a hierarchical geometric inference framework, including image point distortion correction, depth recovery based on planar similar triangle constraint, and rigid transformation estimation between the camera and world coordinate systems. This formulation improves pose estimation accuracy under distorted imaging conditions. To accommodate distortion variations in practical scenarios, a radial distortion coefficient update method is further designed to adaptively adjust the radial distortion parameters under single-frame observations, ensuring that the distortion model remains consistent with the actual imaging distortion and providing reliable model inputs for distortion correction in pose estimation. In addition, to enhance pose stability in dynamic scenarios, a multi-frame optical center consistency constraint (MOCCC) method is introduced to optimize the pose estimation for more stability. By constraining pose estimation across adjacent frames using the mean optical center over multiple frames as the optimization objective, the proposed method effectively suppresses pose jitter caused by single-frame observation noise. Finally, a three-degree-of-freedom (3-DOF) attitude motion platform is established, and both static and dynamic experimental scenarios are designed to validate the accuracy and stability of the proposed algorithm. Experimental results demonstrate that the proposed algorithm achieves high accuracy and high stability pose estimation under imaging distortion and small perturbations, exhibiting good robustness and suitability for practical UAV visual navigation applications. Full article

Maintaining high-precision line-of-sight pointing in airborne electro-optical gimbals remains a significant challenge due to the simultaneous presence of heterogeneous disturbances and strict mechanical structural constraints within complex dynamic conditions. Traditional anti-disturbance methods often struggle to provide fine-grained compensation for multi-source uncertainties where low-frequency lumped disturbances (e.g., friction and unbalanced torques) and high-frequency harmonic vibrations (e.g., engine-induced vibrations and aerodynamic gusts) are intricately coupled. To address these challenges, this paper proposes a refined disturbance separation-based composite control scheme. First, a deep-coupled aircraft–gimbal dynamics model is constructed to reveal the spectral separation characteristics of multi-source disturbances under the “moving base” effect. Second, a Refined Disturbance Observer architecture is developed by coupling an Extended State Observer with a Harmonic Disturbance Observer, enabling the decoupled separation and precise estimation of heterogeneous disturbances based on their spectral characteristics. Furthermore, a finite-time composite controller incorporating a Barrier Lyapunov Function is designed to guarantee that the system output strictly adheres to inherent mechanical structural boundaries. Numerical simulations confirm high-precision tracking and strict constraint satisfaction of the scheme. Full article

Introduction: Venous leg ulcers are the most severe manifestation of chronic venous insufficiency, and their treatment is based on compression therapy, whose effectiveness depends on the magnitude of the pressure and the biomechanical properties of the system. Doubts persist about the actual correlation between interface pressure, bandage stiffness and clinical outcomes in real-world practice. Objective: To compare the clinical efficacy and biomechanical behavior of different multicomponent compression systems in venous leg ulcers, analyzing the relationship between interface pressure, static stiffness, edema reduction and variation in the wound area. Methodology: This is a prospective, observational and multicenter study in six districts/health areas of Andalusia, in adults with active venous ulcers attended by Advanced Practice Nurses in Complex Chronic Wounds. Several multi-component compression systems were applied, and interface pressure was monitored using Tight Alright^® at three points on the leg for 96 h, recording final pressure, static stiffness, perimeters and ulcer area. Results: All systems achieved a reduction in leg circumference, more marked at the proximal points, evidencing an overall decongestant effect. The correlation between final pressure and edema reduction was weak, and relevant differences were observed in the reduction in ulcer area, with Urgo K2 and CPK Compress 2 standing out with decreases of more than 50% compared to medium or low yields of other systems with comparable pressures. The static stiffness analysis showed specific patterns according to system and leg size, as well as a heterogeneous longitudinal distribution of pressure. Conclusions: The efficacy of compression in venous ulcers depends on both the interface pressure and the design and biomechanical behavior of the system, with clinically relevant differences between multicomponent dressings. Multipoint pressure and stiffness monitoring provides useful information to optimize system selection and support decisions based on biomechanical parameters and standardized clinical outcomes. Full article

Inland water bodies are vital to the Earth’s ecosystem, global water cycles, and climate regulation. Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a powerful tool for water detection, particularly with the deployment of the Fengyun-3 (FY-3) E, F, and G satellites. This study proposes an inland water body detection method by integrating the Z-score algorithm with specular point land surface reflectivity (SR_sp) derived from FY-3 Level-1 GNSS-R data. Using 2024 observations, the method was validated in the Amazon and Congo basins against optical water body products. The results demonstrate high detection performance, achieving overall accuracies of 95.39% and 97.38% in the two regions, respectively. Analysis of reflectivity expressed in decibels (dB) reveals that while dB-units enhance the detection of small tributaries, they are more susceptible to noise-induced misclassification compared to linear units. Furthermore, a comparative assessment of GNSS constellations shows that multi-system combination significantly reduces noise compared to single-system approaches. Notably, the Galileo system exhibited limited sensitivity to small tributaries due to lower observational density. Sensitivity analyses further reveal that interpolation methods and Z-score threshold selection are important factors influencing detection accuracy. As the first systematic evaluation of FY-3 GNSS-R data for inland water detection, this research provides a critical benchmark for future multi-platform and multi-constellation land surface retrieval studies. Full article

Against the backdrop of the global transition toward clean and low-carbon energy systems, hydrogen has emerged as a promising alternative to fossil fuels owing to its carbon-free characteristics and broad cross-sector applicability. However, the high diffusivity and wide flammability range of hydrogen pose significant safety challenges for its large-scale deployment. Conventional detection methods are generally limited to point-based data acquisition and struggle to capture the transient flow-field characteristics associated with hydrogen diffusion as well as combustion or explosion processes. This review aims to systematically clarify the exclusive technical advantages of schlieren visualization technology for hydrogen research, summarize its application progress in hydrogen and hydrogen mixture diffusion distribution and combustion/explosion flow-field testing, and propose future optimization directions and application expansion paths. Schlieren visualization, based on optical refraction principles, has evolved from a traditional experimental technique into a comprehensive system adapted to diverse scenarios, including high-speed schlieren, Z-type schlieren, background-oriented schlieren (BOS), and color schlieren. Owing to its non-intrusive nature, high spatiotemporal resolution and full-field visualization capability, schlieren technology can directly observe the fundamental diffusion behavior of hydrogen jets and capture distinctive flow features throughout all stages of hydrogen mixture combustion and explosion. It effectively overcomes the limitations of conventional detection methods and has become an indispensable tool in hydrogen energy safety research. Future research should focus on improving technical performance, strengthening interdisciplinary integration with machine learning and digital twin technologies, and expanding application scenarios to multi-field coupling systems, so as to support the safe and efficient development of the hydrogen industry and contribute to global carbon peaking and carbon neutrality goals. Full article

In low-altitude complex environments, accurately reconstructing the three-dimensional (3D) flight trajectories of small unmanned aerial vehicles (UAV) without onboard positioning modules remains challenging. To address this issue, this paper proposes a multi-view ground camera-based UAV 3D trajectory detection method founded on an optical imaging geometric model. Multiple ground cameras are used to synchronously observe UAV flight, enabling stable 3D trajectory reconstruction without relying on onboard Global Navigation Satellite System (GNSS). At the two-dimensional (2D) observation level, a lightweight object detection model is employed for rapid UAV detection. Foreground segmentation is further introduced to extract accurate UAV contours, and geometric centroids are computed to obtain precise image plane coordinates. At the 3D reconstruction stage, camera extrinsic parameters are estimated using a back intersection method with ground control points, and the UAV spatial position in the world coordinate system is recovered via multi-view forward intersection. Field experiments demonstrate that the proposed method achieves stable 3D trajectory reconstruction in real urban environments, with a median error of 4.93 m and a mean error of 5.83 m. The mean errors along the X, Y, and Z axes are 2.28 m, 4.58 m, and 1.09 m, respectively, confirming its effectiveness for low-cost UAV trajectory monitoring. Full article

Electric vehicle(EV) diffusion exhibits nonlinear, path-dependent dynamics shaped by interacting economic, technological, and social constraints. This paper develops a unified hybrid systems framework that captures these complexities by integrating microfounded household choice, capacity-constrained firm behavior, local network spillovers, and multi-level policy intervention within a Filippov differential-inclusion structure. Households face heterogeneous preferences, liquidity limits, and network-mediated moral and informational influences; firms invest irreversibly under learning-by-doing and profitability thresholds; and national and local governments implement distinct financial and infrastructure policies subject to budget constraints. The resulting aggregate adoption dynamics feature endogenous switching, sliding modes at economic bottlenecks, network-amplified tipping, and hysteresis arising from irreversible investment. We establish conditions for the existence of Filippov solutions, derive network-dependent tipping thresholds, characterize sliding regimes at capacity and liquidity constraints, and show how network structure magnifies hysteresis and shapes the effectiveness of local versus national policy. Optimal-control analysis further demonstrates that national subsidies follow bang–bang patterns and that network-targeted local interventions minimize the fiscal cost of achieving regional tipping. Beyond theoretical characterization, the framework is structurally calibrated to match the order-of-magnitude effects reported in leading empirical and simulation-based studies, including network diffusion models, agent-based simulations, bass-type specifications, and fuel-price shock analyses. The hybrid formulation reproduces short-run percentage-point subsidy effects, long-run forecast dispersion under alternative network assumptions, and policy-induced equilibrium shifts observed in the applied literature while providing a unified geometric interpretation of these heterogeneous results through explicit basin boundaries and regime switching. The framework provides a complex systems perspective on sustainable mobility transitions and clarifies why identical national policies can generate asynchronous regional outcomes. These results offer theoretical foundations for designing coordinated, cost-effective, and network-aware EV transition strategies. Full article

Multiview video coding grows exponentially with the number of views, and VVC-based systems face particularly severe computational burdens from exhaustive inter-view prediction searches. We propose VVC-MV-CM, a complexity-managed multiview extension of VVC that combines rule-based pre-screening with CNN-based adaptive inter-view prediction bypassing within a two-stage decision engine. Performance trends are observed across 19 test sequences covering planar, arc, and spherical camera configurations under all-view and selected-view encoding modes. For planar all-view configurations, VVC-MV-CM-A achieves −52.7% BD-rate relative to MIV-A with 68% encoding time reduction. Arc arrangements yield competitive performance at −1.26% (all-view) and approximately −1% (selected-view) BD-rate. Spherical configurations demonstrate −19.8% (all-view) and −15.0% (selected-view) BD-rate gains, driven by multi-reference redundancy and temporal prediction prioritization. View density analysis reveals a 4.8 percentage-point compression difference between all-view and selected-view configurations, corresponding to approximately 2.4% efficiency gain per doubling of camera count. The proposed codec achieves 1.17–1.46× encoding time relative to MIV anchors with 18–36% decoding speedup, establishing configuration-adaptive prediction as an effective and deployable approach to multiview video coding across a wide range of geometric complexities and view-sampling densities. Full article

The integration of photovoltaic (PV) generation with bidirectional electric vehicle (EV) fast-charging systems offers a promising pathway toward sustainable transportation and grid support. However, the dynamic coupling between maximum power point tracking (MPPT) perturbations and grid-side power quality presents a fundamental challenge in such multi-converter architectures. This paper addresses this challenge through a coordinated design and optimization framework for a grid-connected, PV-assisted bidirectional off-board EV fast charger. The system integrates a 184.695 kW PV array via a DC-DC boost converter, a common DC link, a three-phase bidirectional active front-end rectifier with an LCL filter, and a four-phase interleaved bidirectional DC-DC converter for the EV battery interface. A comparative evaluation of three MPPT algorithms establishes the Fuzzy Logic Variable Step-Size Perturb & Observe (Fuzzy VSS-P&O) as the optimal strategy, achieving 99.7% tracking efficiency with 46 μs settling time. However, initial integration of this high-performance MPPT reveals system-level harmonic distortion, with grid current total harmonic distortion (THD) reaching 4.02% during charging. To resolve this coupling, an Artificial Bee Colony (ABC) metaheuristic algorithm performs coordinated optimization of all critical PI controller gains. The optimized system reduces grid current THD to 1.40% during charging, improves DC-link transient response by 43%, and enhances Phase-Locked Loop (PLL) synchronization accuracy. Comprehensive validation confirms robust bidirectional operation with seamless mode transitions and compliant power quality. The results demonstrate that system-wide intelligent optimization is essential for reconciling advanced energy harvesting with stringent grid requirements in next-generation EV fast-charging infrastructure. Full article

Artificial light at night (ALAN) data is widely used in urban function analysis and socio-economic activity monitoring, but its application at the micro-scale of cities still faces challenges. This study utilizes high spatial resolution SDGSAT-1 nighttime light data to explore the spatial heterogeneity of ALAN at the street scale in two representative Chinese cities—Beijing and Guangzhou. By integrating multi-source data (such as building vector data, road networks, and point of interest data), a multi-dimensional indicator system covering urban morphology, functional structure, and transportation accessibility is constructed. Based on this, the study employs a Geographically Weighted Random Forest (GWRF) model combined with the Shapley Additive Explanations (SHAP) method to deeply analyze the non-linear relationships between ALAN intensity and multiple driving factors, as well as their spatial variability. Results demonstrate the superiority of the GWRF model over global models in capturing spatial non-stationarity, with R² values of 0.67 for Beijing and 0.74 for Guangzhou, compared to 0.62 and 0.71 for the random forest models, respectively. Road density is the dominant factor influencing nighttime light intensity in both Beijing and Guangzhou. However, the relationship between ALAN and its driving factors varies across these cities. In Beijing, a balanced multi-factor model is observed, whereas in Guangzhou, ALAN intensity is primarily driven by road density, with secondary influences from other factors like sky view factor. This study validates SDGSAT-1 for micro-scale analysis, offering a scientific basis for differentiated urban lighting planning. Full article

Background: Altered gut microbiota and gut-derived inflammation impair glucose regulation and promote metabolic endotoxemia, yet evidence on probiotic effects across combined glycaemic, inflammatory and short-chain fatty acid (SCFA) outcomes remains limited. This study investigated the effects of a 12-week multi-species probiotic on glucose homeostasis, incretin hormones, inflammatory biomarkers and circulating SCFAs in adults with subthreshold depression. Methods: In a 12-week double-blind, randomised, placebo-controlled trial, 39 adults with subthreshold depression were allocated to either a probiotic supplement containing Limosilactobacillus fermentum LF16, Lacticaseibacillus rhamnosus LR06, Lactiplantibacillus plantarum LP01 and Bifidobacterium longum 04 (n = 19) or placebo (n = 20). Fasting glucose, insulin, HOMA-IR, glucose-dependent insulinotropic peptide (GIP), high-sensitivity C-reactive protein (hs-CRP), lipopolysaccharide-binding protein (LBP), soluble CD14 (sCD14) and SCFAs were evaluated at three time points: baseline, week 6 and week 12. Between-group and treatment × time effects were analysed using general linear models. Results: Probiotic supplementation significantly reduced fasting glucose at 12 weeks compared with placebo (−1.8 vs. 0.1 mmol/L; p = 0.036). In the probiotic group, greater reductions in GIP (p = 0.012; p = 0.037), LBP (p < 0.001), sCD14 (p = 0.002; p = 0.001) and hs-CRP (p = 0.047) were also observed compared with placebo. Plasma SCFA concentrations remained largely unchanged, with no significant treatment × time interactions, except for higher valerate levels at 12 weeks in the probiotic group (p = 0.019). Conclusions: Twelve weeks of multi-species probiotic supplementation improved fasting glucose, reduced incretin and inflammatory biomarkers and attenuated metabolic endotoxemia, without alterations in circulating SCFAs. These findings support beneficial modulation of metabolic–immune pathways and highlight the promising role of probiotics to enhance glucose regulation and systemic inflammatory tone in adults with subthreshold depression. Full article

The proliferation of Internet of Things (IoT) devices has intensified the need for efficient real-time anomaly and intrusion detection, making the selection of an appropriate Complex Event Processing (CEP) engine a critical architectural decision for security-aware data pipelines. Python-based CEP frameworks offer compelling advantages through the seamless integration with data science and machine learning ecosystems; however, rigorous comparative evaluations of such frameworks under realistic IoT security workloads remain absent from the literature. This study presents the first systematic comparative evaluation of Faust and Streamz—two Python-native CEP engines representing fundamentally different architectural philosophies—specifically in the context of IoT network intrusion detection. Faust was selected for its actor-based stateful processing model with native Kafka integration and distributed table support, while Streamz was selected for its reactive, lightweight pipeline design targeting high-throughput stateless processing, making them representative of the two dominant paradigms in Python stream processing. Although both engines target different application niches, their performance characteristics under realistic CEP workloads have never been rigorously compared, leaving practitioners without empirical guidance. The primary evaluation employs an IoT network intrusion dataset comprising 583,485 events from 83 heterogeneous devices. To assess whether the observed performance characteristics are specific to this single dataset or generalize across different workload profiles, a secondary IoT-adjacent benchmark is included: the PaySim financial transaction dataset (6.4 million records), selected because its event schema, fraud-pattern temporal structure, and volume differ substantially from the intrusion dataset, providing a stress test for cross-workload robustness rather than a claim of domain equivalence. We acknowledge the reviewer’s valid point that a second IoT-specific intrusion dataset (such as TON_IoT or Bot-IoT) would constitute a more directly comparable validation; this is identified as a priority for future work. The load levels used in scalability experiments (up to 5000 events per second) intentionally exceed the dataset’s natural rate to stress-test each engine’s architectural ceiling and identify saturation thresholds relevant to large-scale or multi-sensor IoT deployments. We conducted controlled experiments with comprehensive statistical analysis. Our results demonstrate that Streamz achieves superior throughput at 4450 events per second with 89% efficiency and minimal resource consumption (40 MB memory, 12 ms median latency), while Faust provides robust intrusion pattern detection with 93–98% accuracy and stable, predictable resource utilization (1.4% CPU standard deviation). A multi-framework comparison including Apache Kafka Streams and offline scikit-learn baselines confirms that Faust achieves detection quality competitive with JVM-based alternatives (Faust: 96.2%; Kafka Streams: 96.8%; absolute difference of 0.6 percentage points, not statistically significant at $p=0.318$) while retaining the Python ecosystem advantages. Statistical analysis confirms significant performance differences across all metrics ($p<0.001$, Cohen’s $d>0.8$). Critical scalability thresholds are identified: Streamz maintains efficiency above 95% up to 3500 events per second, while Faust degrades beyond 2500 events per second. These findings provide IoT security engineers and system architects with actionable, empirically grounded guidance for CEP engine selection, establish reproducible benchmarking methodology applicable to future Python-based stream processing evaluations, and advance theoretical understanding of the accuracy–throughput trade-off in stateful versus stateless Python CEP architectures. Full article

Segmented telescopes face significant challenges in achieving high segment positioning accuracy under complex disturbances, which directly impact observational sensitivity and resolution. Conventional rigid actuators with limited bandwidth (e.g., Keck ~20 Hz) struggle to maintain control stability. Novel dual-stage actuators combining coarse and fine adjustment (e.g., voice coil motors) now achieve <8 nm precision over millimeter-level strokes. Moreover, their higher closed-loop bandwidth (e.g., TMT ~60 Hz) can ensure rapid settling without overshoot and robust suppression of high-frequency disturbances (e.g., pulsating wind and mechanical vibration). In parallel, system-level control strategies have been updated accordingly. Ground-based systems focus on real-time multimodal decoupling, while space-based systems emphasize non-contact vibration isolation and nested multi-loop control to achieve sub-arcsecond pointing stability. This review surveys the design and control strategies of damping–positioning mechanisms for segmented telescopes and discusses the key trade-offs among critical performance metrics, including resolution, stroke, and load capacity. Particular attention is given to the disturbance-sensitivity analysis and active damping techniques (up to ~50% vibration reduction) implemented in the ELT “hard” actuator approach. Future directions include cross-scale collaborative control, smart material applications, and AI-based adaptive parameter optimization, which together provide a technical pathway toward high-precision imaging in next-generation highly segmented telescopes. Full article

Microwave wireless power transfer (MWPT) for space solar power stations (SSPS) requires high-precision beam pointing in order to maintain effective aperture coupling and transmission efficiency under platform motion and disturbances. This paper proposes a dual-link beam pointing estimation framework that integrates guidance-link interferometric angle-of-arrival (AoA) measurements with power-link energy-field reconstruction. The interferometric chain provides high-rate azimuth and elevation observations for dynamic tracking, while the energy-field reconstruction estimates the energy-centroid displacement from the received-aperture power distribution to correct steady-state pointing bias. A Kalman filter (KF) is developed to fuse the asynchronous multi-rate measurements, yielding continuous and robust pointing estimates for closed-loop beam control. Simulation results show that the proposed fusion method achieves azimuth and elevation RMSEs of ${0.0069}^{°}$ and ${0.006}^{°}$ with interferometric and energy-centroid error levels of approximately ${0.05}^{°}$ and ${0.02}^{°}$, respectively, significantly reducing high-frequency fluctuations. In addition, a sensitivity model is established to quantify the impact of angular errors on capture efficiency. The expected efficiency improves from approximately 0.988 and 0.998 for the individual methods to nearly unity for the fusion output. Quantitative accuracy thresholds corresponding to different efficiency requirements are further derived, providing practical guidelines for SSPS MWPT system design. Full article

This paper presents an integrated multi-modal framework for detecting hyperthyroidism-associated abnormalities, namely exophthalmos and thyroid-related neck swelling, through the joint analysis of frontal facial and neck images using a deep learning-based approach. The objective of this research is to develop an integrated AI framework that improves hyperthyroid-related abnormality detection by simultaneously analyzing facial images of both the eye and neck based on pattern clinical knowledge. The multi-modal framework mimics a biological visual mechanism by using a dual-pathway architecture that concurrently processes foveal-like details of the eyes and neck. It integrates these high-resolution visual embeddings with quantitative morphological measurements to simulate a clinician’s ability to fuse observation with physical assessment. The proposed system employs a multi-faceted decision-making process derived from three distinct data components: two from frontal face analysis and one from neck region analysis. Specifically, eye regions extracted from facial images are preprocessed using the YOLOv11s model. The proposed system leverages a dual-pathway processing architecture to extract comprehensive diagnostic features. For the eye dataset, the framework utilizes a face mesh-based eye landmark (FMEL) to extract both eye regions and perform eyes unfold processing. These regions are subsequently analyzed by the proposed sclera map unwrapping engine (SMUE) to derive quantitative sclera metrics from both the left and right eyes. To optimize classification, a dual-branch architecture is employed by integrating CNN visual embeddings with SMUE-derived statistical features through a feature fusion layer. Simultaneously, the neck processing path executes the neck region of interest (ROI) prediction {upper, lower} to segment critical regions for goiter assessment via the proposed neck $\mu -\sigma$ ensemble thresholding (NSET) algorithm. The experimental results demonstrate that the proposed algorithm for eye analysis achieved a mean average precision (mAP50) of 96.4%, with a specific mAP50 of 98.6% for the hyperthyroid class. Regarding quantitative scleral measurement, the SMUE process revealed distinct morphological differences, with the experimental data group exhibiting consistently higher pixel distances across the reference points compared with the normal group. Furthermore, the proposed NSET algorithm yielded the highest performance for swollen neck classification with an mAP50 of 92.0%, significantly outperforming the baseline deep learning models while maintaining lower computational complexity. Full article