Search Results (1,598)

Multi-satellite sequential detection is crucial for maritime target identification and tracking. However, inherent satellite revisit patterns and maritime target motion often result in fragmented track segments, necessitating effective multi-satellite track association to ensure continuity. Existing methods predominantly rely on track information and statistical signal parameters, rendering them susceptible to localization errors and ineffective in scenarios characterized by dense targets and overlapping radar parameters. To overcome these limitations, this paper proposes an attention-guided track-pulse-sequence target association network (AG-TPS-TAN). First, the asymmetric dual-branch network operates by incorporating both track data and electromagnetic signal data, processing the latter in the form of raw pulse sequences instead of the conventional statistical parameters. Second, within the track branch, we enhance the feature representation by incorporating a novel track-point-aware attention mechanism which can autonomously identify and weight critical points indicative of motion continuity, such as interruption boundaries and maneuvering points. Third, we introduce a dual-feature fusion module optimized with a combined loss function, which pulls feature representations of the same target closer together while pushing apart those from different targets, thereby enhancing both feature consistency and discriminability. Experiments were conducted on a public AIS trajectory dataset, constructing a dataset containing both motion trajectories and electromagnetic signals. Evaluations under varying target numbers showed that the proposed AG-TPS-TAN achieved average association accuracies of 93.91% for 5 targets and 63.83% for 50 targets. Against this, the track-only method TSADCNN scored 76.08% and 25.64%, and the signal-statistics-based method scored 77.12% and 29.56%, for 5 and 50 targets, respectively, thus exhibiting a clear advantage for the proposed approach. Full article

Cooperative task allocation is one of the critical enablers for multi-Autonomous Underwater Vehicle (AUV) missions, but existing approaches often assume reliable communication that rarely holds in real underwater acoustic environments. We study here the performance and robustness of the Consensus-Based Bundle Algorithm (CBBA) for multi-AUV task allocation under realistically degraded underwater communication conditions with dynamically appearing tasks. An integrated simulation framework that incorporates a Dubins-based kinematic model with minimum turning radius constraints, a configurable underwater acoustic communication model (range, delay, packet loss, and bandwidth), and a full implementation of improved CBBA with new features, complemented by 3D trajectory and network-topology visualization. We define five communication regimes, from ideal fully connected networks to severe conditions with short range and high packet loss. Within these regimes, we assess CBBA based on task allocation quality (total bundle value and task completion rate), convergence behavior (iterations and convergence rate), and communication efficiency (message delivery rate, average delay, and network connectivity), with additional metrics on the number of conflicts during dynamic task reallocation. Our simulation results indicate that CBBA maintains performance close to the optimum when the conditions are good and moderate but degrades significantly when connectivity becomes intermittent. We then introduce a local-communication-based conflict resolution strategy in the face of frequent task conflicts under very poor conditions: neighborhood-limited information exchange, negotiation within task areas, and decentralized local decisions. The proposed conflict resolution strategy significantly reduces the occurrence of conflicts and improves task completion under stringent communication constraints. This provides practical design insights for deploying multi-AUV systems under weak underwater acoustic networks. Full article

Background/Objectives: Cerebral palsy (CP) is the most common cause of physical disability in childhood. While gait improvements are often observed during childhood, it remains unclear whether these gains are sustained into adulthood. This study aimed to evaluate the long-term evolution of gait outcomes from childhood to adulthood in individuals with CP who received orthopedic care early in life. Methods: This retrospective study included 83 adults with cerebral palsy (44 unilateral/uCP, 39 bilateral/bCP; GMFCS I–III) who underwent clinical gait analysis in childhood and again as adults (minimum 4 years between visits, n = 249 CGA). Gait was assessed using the modified Gait Profile Score (mGPS) and normalized walking speed (NWS). The effects of life stage (childhood, adolescence, early adulthood, and adulthood) were analyzed using Kruskal–Wallis tests with post hoc comparisons. Individual clinical transitions were quantified from early adulthood to adulthood, with a minimal clinically important difference (MCID) change in mGPS (1.6°) and NWS (0.20 s⁻¹) for improvement or decline. Results: Longitudinal analysis revealed that while group-average mGPS improved from childhood to adulthood, NWS declined significantly for all patients (p < 0.01). However, individual trajectories from early adulthood to adulthood diverged by CP type. Those with bCP GMFCS II and III had a more frequent clinical decline in mGPS (4/14, 29%), with minimal potential for improvement (1/14, 17%). In contrast, individuals with uCP had less frequent decline (1/17, 6%) and a greater improvement (3/17, 18%). Conclusions: While significant improvements in gait quality are achieved by early adulthood, substantial clinical decline occurs during adulthood in bCP (GMFCS II–III) patients. These findings highlight the need for lifelong monitoring, with re-evaluation regarding the need for surgical interventions from early adulthood to adulthood in bCP patients with greater motor impairments. Full article

This paper presents a controller-hardware-in-the-loop simulation (C-HILS) framework for validating models, evaluating control performance, and assessing the thermal lifetime of a tens-of-kilowatt inverter. The real inverter and the C-HILS platform were operated in parallel, and accuracy was quantified using phase-current root mean square error, voltage spectral analysis, and total harmonic distortion (THD). Across a wide range of SVPWM and DPWM cases, deviations remained within 2–5%, confirming close agreement between experiment and simulation. Using the validated C-HILS system, sampling frequency and output power were swept while comparing current tracking, THD, average switching frequency, semiconductor losses, and efficiency. SVPWM achieved lower THD, whereas DPWM reduced average switching frequency and switching losses, improving efficiency. C-HILS waveforms were then applied to a Foster thermal network to reconstruct the junction–temperature trajectory; $T_{\mathrm{j}}\left(t\right)$, and $\mathsf{\Delta }{T}_{\mathrm{j}}$ and $T_{\mathrm{j},\mathrm{m}\mathrm{i}\mathrm{n}}$ were mapped to lifetime using the Bayerer model. For a representative cyclic mission, $\mathsf{\Delta }{T}_{\mathrm{j}}$ decreased from approximately $25.6 °\mathrm{C}$ with SVPWM to about $17.5 °\mathrm{C}$ with DPWM, increasing the estimated lifetime from approximately 1.36 years to 9.14 years. These results demonstrate that the proposed C-HILS framework provides a unified pre-prototype tool for model verification, control strategy comparison, and quantitative thermal reliability assessment of shipboard propulsion inverters. Full article

Effectively fusing the rich semantic information from camera images with the high-precision geometric measurements provided by LiDAR point clouds is a key challenge in mobile robot environmental perception. To address this problem, this paper proposes a highly extensible instance-aware fusion framework designed to achieve efficient alignment and unified modeling of heterogeneous sensory data. The proposed approach adopts a modular processing pipeline. First, semantic instance masks are extracted from RGB images using an instance segmentation network, and a projection mechanism is employed to establish spatial correspondences between image pixels and LiDAR point cloud measurements. Subsequently, three-dimensional bounding boxes are reconstructed through point cloud clustering and geometric fitting, and a reprojection-based validation mechanism is introduced to ensure consistency across modalities. Building upon this representation, the system integrates a data association module with a Kalman filter-based state estimator to form a closed-loop multi-object tracking framework. Experimental results on the KITTI dataset demonstrate that the proposed system achieves strong 2D and 3D detection performance across different difficulty levels. In multi-object tracking evaluation, the method attains a MOTA score of 47.8 and an IDF1 score of 71.93, validating the stability of the association strategy and the continuity of object trajectories in complex scenes. Furthermore, real-world experiments on a mobile computing platform show an average end-to-end latency of only 173.9 ms, while ablation studies further confirm the effectiveness of individual system components. Overall, the proposed framework exhibits strong performance in terms of geometric reconstruction accuracy and tracking robustness, and its lightweight design and low latency satisfy the stringent requirements of practical robotic deployment. Full article

Designing high-speed aircraft for wide-speed-range operation remains a major aerodynamic challenge. This study investigates the unsteady aerodynamics of a continuously morphing airfoil from transonic to hypersonic regimes. A smooth morphing trajectory is constructed among transonic, supersonic, and hypersonic baseline shapes, and analyzed via high-fidelity unsteady Reynolds-averaged Navier–Stokes (URANS) simulations with a radial basis function (RBF) dynamic mesh. Two processes are examined: pure geometric morphing at fixed Mach numbers (Ma), and morphing coupled with flight acceleration. Key findings reveal two distinct adaptation features: (1) Transonic flow is highly sensitive to morphing (28.8% drop in lift-to-drag ratio), while supersonic flow is robust (<5% variation). (2) During coupled acceleration, the flow transitions smoothly—the shock evolves from a detached bow wave to an attached oblique structure, and the adaptive airfoil maintains a lift-to-drag ratio above 4 across Ma = 0.8–6. Additionally, wake vorticity transitions from organized shear layers to multi-scale clusters. These results elucidate the flow physics mechanism of continuous morphing and provide a framework for designing adaptive wide-speed-range aircraft. Full article

Crushing is a critical step in the efficient utilization of quasi-brittle materials such as ores and solid wastes. During this process, materials undergo fracture, and the product particles are ejected, carrying significant kinetic energy. This study investigates typical quasi-brittle materials—concrete and quartz glass—by conducting impact crushing tests using a drop-weight apparatus under varying contact modes and input energy levels. High-speed camera was employed to capture the fracture patterns of the materials and the trajectories of the ejected particles, enabling the calculation of kinetic energy during crushing. The results indicate that under point contact loading, both kinetic energy and its proportion increase significantly with rising input energy. In contrast, under surface contact loading, the kinetic energy and its proportion exhibit minimal change as input energy increases. The average ejection velocity of particles from quartz glass specimens during crushing was 6.28 m/s, which is 2.21 times that of concrete specimens. Moreover, the average proportion of kinetic energy in quartz glass crushing was 5.049%, approximately 14.43 times greater than that in concrete. Enhancing material toughness and adopting surface contact loading help reduce both the kinetic energy and its proportion during crushing. This research contributes to minimizing kinetic energy loss and improving the efficiency of energy utilization in crushing processes. Full article

In the process of ore drawing using a caving method under interburden conditions, the key to controlling ore dilution lies in the accurate prediction of boundary particle migration trajectories. To address the challenges of high computational costs and complex modeling in traditional numerical simulations, this study designs a dataset construction method. After calibrating parameters using the angle of repose, ore-drawing numerical simulation datasets with interburden (post-defined and pre-defined models) are established. Building upon this foundation, an improved Transformer model is proposed. The model enhances spatiotemporal representation through multi-layer feature fusion embedding, strengthens long-range dependency capture via a reinforced spatiotemporal attention backbone, improves local dynamic modeling capability through optimized decoding at the output stage, and integrates transfer learning to achieve continuous prediction of particle migration. Validation results demonstrate that the model accurately predicts the spatial distribution patterns and collective motion trends of particles, with prediction errors at critical nodes confined to within a single stage and an average estimation error of approximately 4% in interburden regions. The proposed approach effectively overcomes the timeliness bottleneck of traditional interburden ore-drawing simulations, enabling rapid and accurate prediction of boundary particle migration under interburden conditions. Full article

Population aging is driving the need for unobtrusive, continuous monitoring solutions in residential care environments. Radio-frequency (RF)-based technologies such as Ultra-Wideband (UWB) and millimeter-wave (mmWave) radar are particularly attractive for providing detailed information on presence and movement while preserving privacy. Building on a UWB–mmWave localization system deployed in a senior living residence, this paper focuses on the data-processing methodology for extracting quantitative mobility indicators from long-term indoor monitoring data. The system combines a device-free mmWave radar setup in bedrooms and bathrooms with a tag-based UWB positioning system in common areas. For mmWave data, an adaptive short-term average/long-term average (STA/LTA) detector operating on an aggregated, normalized radar energy signal is used to classify micro- and macromovements into bedroom occupancy and non-sedentary activity episodes. For UWB data, a partially constrained Kalman filter with a nearly constant velocity dynamics model and floor-plan information yields smoothed trajectories, from which daily gait- and mobility-related metrics are derived. The approach is illustrated using one-day samples from three users as a proof of concept. The proposed methodology provides individualized indicators of bedroom occupancy, sedentary behavior, and mobility in shared spaces, supporting the feasibility of combined UWB and mmWave radar sensing for longitudinal routine analysis in real-world elderly care environments. Full article

Automatic parking in constrained environments, such as dead-end roads and narrow parallel spaces, remains a challenge due to the low success rate and poor real-time performance of conventional planning algorithms. The paper proposes an entry-point guided path planning method that integrates heuristic search with hybrid A* and reeds-shepp curve to address the above limitations. By rapidly identifying the optimal initial parking pose, the proposed method ensures the kinematic feasibility and smoothness of the resulting trajectories. To further improve efficiency and safety in tight spaces, a hybrid collision detection mechanism is developed by combining a rectangular envelope with multi-circle fitting. The hierarchical geometric modeling approach significantly reduces computational cost while maintaining high detection accuracy. The method is validated through both simulations and real-vehicle experiments in vertical and parallel parking scenarios. Results demonstrate that in typical constrained scenarios, the average planning time is only 0.543 s, and the number of direction changes is maintained between 1 and 6, demonstrating superior computational efficiency and improved trajectory smoothness. These attributes make the algorithm highly suitable for practical deployment in advanced driver assistance systems and autonomous vehicles. Full article

To address challenges in weld recognition during vacuum electron beam welding caused by dark environments and metal reflections, this study proposes an improved hybrid algorithm combining YOLOv11-seg with adaptive Canny edge detection. By incorporating the UFO-ViT attention mechanism and optimizing the network architecture with the EIoU loss function, along with adaptive threshold setting for the Canny operator using the Otsu method, the recognition performance under complex conditions is significantly enhanced. Experimental results demonstrate that the optimized model achieves an average precision (mAP) of 77.4%, representing a 9-percentage-point improvement over the baseline YOLOv11-seg. The system operates at 20 frames per second (FPS), meeting real-time requirements, with the generated welding trajectories showing an average length deviation of less than 3 mm from actual welds. This approach provides an effective pre-weld visual guidance solution, which is a critical step towards the automation of electron beam welding. Full article

Schistosomiasis remains a major neglected tropical disease globally and presents particular challenges for countries transitioning from control to elimination. Saudi Arabia represents a unique epidemiological setting, having shifted from historical endemic transmission to very low reported incidence, yet long-term national analyses remain limited. A retrospective longitudinal analysis of national schistosomiasis surveillance data from 2002 to 2024 was conducted to evaluate temporal trends, clinical subtypes, regional distribution, and demographic characteristics. Joinpoint regression was used to identify significant changes in temporal trends, and autoregressive integrated moving average (ARIMA) models were applied to forecast national and regional trajectories. National incidence declined markedly from 5.5 per 100,000 in 2002 to 0.12 per 100,000 in 2024, with a notable change around 2010, followed by sustained low-level incidence. Intestinal schistosomiasis accounted for most cases, with increasing concentration among adult non-Saudi males and near-elimination among children. Regionally, cases were confined to a limited number of western and southwestern regions, particularly Ta’if, Al Baha, Jazan, and Madinah. Forecasting analyses indicated continued low-level detection without evidence of national resurgence. These findings demonstrate a transition to an elimination-maintenance phase and highlight the need for sustained surveillance in historically endemic regions and mobile populations. Full article

Covert communication assisted by unmanned aerial vehicles (UAVs) can achieve a low detection probability in complex environments through auxiliary strategies, including dynamic trajectory planning and power management, etc. This paper proposes a dual-UAV scheme, where one UAV transmits covert information while the other one generates stochastic jamming to disrupt the eavesdropper and reduce the probability of detection. We propose a dual-mode jamming scheme which can efficiently enhance the average covert rate (ACR). A joint optimization of the dual UAVs’ flight speeds, accelerations, transmit power, and trajectories is conducted to achieve the maximum ACR. Given the high complexity and non-convexity, we develop a dedicated algorithm to solve it. To be specific, the optimization is decomposed into three sub-problems, and we transform them into tractable convex forms using successive convex approximation (SCA). Numerical results verify the efficacy of dual-mode jamming in boosting ACR and confirm the effectiveness of this algorithm in enhancing CC performance. Full article

The integration of Mobile Edge Computing and container virtualization technologies provides crucial support for low-latency and highly resilient service deployment in Internet of Vehicles (IoV) applications. However, the high mobility of vehicles poses challenges to service continuity, necessitating dynamic adjustment of service deployment locations through container migration. Existing research predominantly focuses on independent service migration while overlooking the complex interdependencies among multiple subtasks in practical applications. In this paper, we investigate the container migration problem for dependency-aware services in IoV environments. We first formulate the problem as a dual-objective optimization problem centered on minimizing both the average service delay and system load imbalance. To address the complex dependencies among containers and the highly dynamic nature of IoV environments, we propose an intelligent migration algorithm named GADM that integrates Graph Attention Networks with Deep Reinforcement Learning. The GADM algorithm leverages Graph Attention Networks to capture critical paths in task dependencies, and combines this with an actor–critic-based Deep Reinforcement Learning framework to achieve adaptive decision-making in dynamic environments. Validation using real-world vehicle trajectory datasets and Alibaba cluster trace datasets demonstrates the effectiveness of the proposed algorithm. Experimental results indicate that compared to other methods, GADM significantly improves system load balancing while reducing average service latency. Full article

With the deployment of mega-constellations, the proliferation of on-orbit Resident Space Objects (RSOs) poses a severe challenge to Space Situational Awareness (SSA). RSOs produce elongated and stripe-like signatures in long-exposure imagery as a result of their relative orbital motion. The accurate detection of these signatures is essential for critical applications like satellite navigation and space debris monitoring. However, on-orbit detection faces two challenges: the obscuration of dim RSOs by complex stray light interference, and their dense overlapping trajectories. To address these challenges, we propose the Shape-Aware Attention Network (SAANet), establishing a unified Shape-Aware Paradigm. The network features a streamlined Shape-Aware Feature Pyramid Network (SA-FPN) with structurally integrated Two-way Orthogonal Attention (TTOA) to explicitly model linear topologies, preserving dim signals under intense stray light conditions. Concurrently, we propose an Adaptive Linear Oriented Bounding Box (AL-OBB) detection head that leverages a Joint Geometric Constraint Mechanism to resolve the ambiguity of regressing targets amid dense, overlapping trajectories. Experiments on the AstroStripeSet and StarTrails datasets demonstrate that SAANet achieves state-of-the-art (SOTA) performance, achieving Recalls of 0.930 and 0.850, and Average Precisions (APs) of 0.864 and 0.815, respectively. Full article