Search Results (229)

The rapid development of space exploration demands real-time backhaul of massive sensing payload data in space-ground integrated telemetry, tracking, and command (TT&C) networks. However, traditional narrow-band TT&C links suffer from severe congestion during massive data backhaul. Since most TT&C applications are inherently task-oriented and do not require pixel-perfect data reconstruction, we propose a task-oriented joint resource allocation framework based on semantic communications. Specifically, we introduce an adaptive semantic split computing mechanism that extracts and transmits only compact, decision-critical features instead of raw bitstreams, fundamentally mitigating the bandwidth bottleneck. The joint optimization of computation offloading, semantic splitting, and continuous on-board computing allocation is formulated as a stochastic mixed-integer nonlinear programming (MINLP) problem. We propose a decoupled algorithm based on Hierarchical Multi-Agent Proximal Policy Optimization (HMAPPO) to solve it. An outer layer employs multi-agent reinforcement learning (MARL) for distributed discrete decision-making, while an inner layer utilizes a Karush–Kuhn–Tucker (KKT)-based solver for continuous space-based computing allocation. This bi-level architecture overcomes the curse of dimensionality and mathematically guarantees zero-violation of physical capacity constraints. Simulations demonstrate that HMAPPO rapidly converges and sustains a high weighted success rate under heavy traffic congestion, significantly improving system utility compared to state-of-the-art baselines. Full article

Cloud computing is one of the essential computing platforms for modern enterprises. A total of 84 percent of large businesses use cloud computing services in 2025 to enable remote working and higher flexibility of operation with reduction in the cost of operation. Cloud environments are dynamic and multitenant, often demanding high computational resources for real-time processing. However, the cloud system’s behavior is subjected to various kinds of anomalies in which patterns of data deviate from the normal traffic. The varieties of anomalies that exist are performance anomalies, security anomalies, resource anomalies, and network anomalies. These anomalies disrupt the normal operation of cloud systems by increasing the latency, reducing throughput, frequently violating service level agreements (SLAs), and experiencing the failure of virtual machines. Among all anomalies, virtual machine failures are one of the potential anomalies in which the normal operation of the virtual machine is interrupted, resulting in the degradation of services. Virtual machine failure happens because of resource exhaustion, malware access, packet loss, Distributed Denial of Service attacks, etc. Hence, there is a need to detect the chances of virtual machine failures and prevent it through proactive measures. Traditional machine learning techniques often struggle with high-dimensional data and nonlinear correlations, ending up with poor real-time adaptation. Hence, quantum machine learning is found to be a promising solution which effectively deals with combinatorially complex and high-dimensional data. In this paper, a novel quantum-enhanced support vector machine (QSVM) is designed as an optimized binary classifier which combines the principles of both quantum computing and support vector machine. It encodes the classical data into quantum states. Feature mapping is performed to transform the data into the high-dimensional form of Hilbert space. Quantum kernel evaluation is performed to evaluate similarities. Through effective optimization, optimal hyperplanes are designed to detect the anomalous behavior of virtual machines. This results in the exponential speed-up of operation and prevents the local minima through entanglement and superposition operation. The performance of the proposed QSVM is analyzed using the QuCloudSim 1.0 simulator and further validated using expected value analysis methodology. Full article

Traffic flow forecasting provides essential support for the construction of smart transportation systems. Despite the superiority of the ASTGCN, which uses an attention mechanism to capture spatio-temporal correlations, it lacks an explicit physical interpretation and thus falls into a more general category known for its lack of such interpretation. As a result, in the presence of sparse or unstable congestion, these data-driven models often violate conservation laws and may generate “physical anomalies” or other logically impossible states. To close the gap of data-driven expressiveness and physical consistency, we propose the congestion-aware physics-guided STGCN (CAP-STGCN). This framework builds a synergistic model that achieves intrinsic coupling between the macroscopic traffic flow kinematics (fundamental diagram) and the spatio-temporal learning process. That is to say, under the model’s solution-space constraining effect, its motion space is bound on a feasible manifold. In terms of kinematics, it restricts consistency in the flow, density and speed. Concurrently, to address slow convergence under long-tailed distributions due to a lack of training samples, such as when there are fewer users or higher-quality items, a dynamic congestion-rectification mechanism is introduced. The aforementioned mechanism redefines the optimization landscape by prioritizing hard-to-predict saturation occurrences. Experiments show that, compared with other models, CAP-STGCN achieves higher prediction accuracy; more importantly, it is free of physical anomalies during inference and can be directly used in practice. Full article

Motor vehicle encroachment into non-motorized lanes is a common but hard-to-verify violation in urban intersections, especially when monitored from unmanned aerial vehicles (UAVs) or high-mounted overhead views. Existing rule-based solutions built on horizontal bounding boxes and center-point/line-crossing criteria are sensitive to perspective distortion, occlusion, and frame-to-frame jitter, resulting in unstable decisions and low evidential value. This paper presents a cascaded UAV-view system that closes the loop from perception to evidence output through detection–segmentation–recognition–decision. First, we adopt a two-stage detection cascade: a lightweight vehicle detector localizes vehicles using axis-aligned bounding boxes, and a dedicated YOLOv5n-based oriented bounding box (OBB) license plate detector, constructed via architecture grafting and weight transfer, is then applied within each vehicle region of interest (ROI) to localize rotated license plates under large pose variation and small-target conditions. Second, a U-Net lane region segmentation module provides pixel-level spatial constraints to define an enforceable lane occupancy region. Third, a perspective rectification step is integrated with the PP-OCRv4 optical character recognition (OCR) framework to improve license plate recognition reliability for tilted plates. Finally, an area ratio criterion and an N-frame temporal counter are used to suppress transient misdetections and stabilize alarms. On a representative 100-sample controlled encroachment benchmark, the proposed system improves detection accuracy from 67.0% to 92.0% and reduces the false positive rate from 32.35% to 5.88% compared with a baseline horizontal bounding box (HBB)-based rule. The system outputs both violation alarms and license plate evidence, supporting practical deployment for multi-view traffic governance. Full article

Traffic accidents at urban intersections represent a major road safety concern, particularly those caused by traffic signal violations. To analyze accident mechanisms and develop effective prevention strategies, this study employs a cellular automata model to investigate the relationship between accident probability $P_{ac}$ and traffic parameters at signalized intersections. Simulation results reveal a nonlinear relationship between Pac and traffic demand. The accident probability reaches a maximum under free-flow conditions and subsequently decreases as congestion increases, eventually stabilizing at a nearly constant level under highly congested traffic. Additionally, collision risk increases with lane-changing probability $P_{chg}$, especially upstream of the intersection. High traffic speeds significantly elevate both accident probability and severity. Finally, the results indicate that extending traffic signal cycle durations is not an effective strategy for reducing accident risk. Overall, the proposed model provides a useful framework for estimating accident risk under different traffic conditions and supporting traffic management, including control decisions aimed at improving road safety. Full article

Ensuring the safety of Autonomous Driving Systems (ADS) at urban intersections remains challenging due to complex interactions between vehicles and traffic management infrastructure. This study validates an ADS equipped with connected perception using Infrastructure-to-Vehicle (I2V) communication within a combined virtual and hybrid testing approach. The validation follows the overall structure and methodology of the SUNRISE Safety Assurance Framework (SAF), which is applied in detail where required by the scope of the study. Five representative urban intersection scenarios, covering both nominal driving conditions and safety-critical edge cases, are evaluated using virtual simulations in MATLAB/Simulink (2014b) and hybrid experiments integrating OMNeT++ (5.7.1)/Veins (5.2)/SUMO (1.12.0) with real-world components. Key Performance Indicators (KPIs) related to safety, decision-making, longitudinal control, passenger comfort, and V2X communication performance are analyzed. The results show strong consistency between virtual and hybrid testing, with ego vehicle speed deviations below 2 km/h and trigger distance differences under 3 m. V2X communication achieves a near-perfect Cooperative Awareness Message (CAM) delivery ratio, with an average latency of approximately 142 ms. While this latency remains within the tolerance of the deployed ADS, the overall end-to-end delay highlights opportunities for further optimization. The study demonstrates how the SUNRISE SAF can effectively structure ADS validation, identifies critical scenarios such as right-of-way violations by non-priority obstacles, and provides insights into improving connectivity handling and low-speed braking behavior for Cooperative, Connected, and Automated Mobility (CCAM) systems in urban environments. Full article

Traffic surveillance cameras, as core sensing devices in smart cities, are crucial for traffic management, violation detection, and autonomous driving. However, due to deployment constraints and hardware limitations, the videos they capture often suffer from low resolution and noise, leading to missed and false detections in traditional object detection algorithms trained on high-resolution data. To address this issue, this study proposes a cascaded collaborative framework that integrates video super-resolution (VSR) and object detection for robust perception in low-quality traffic surveillance scenarios. First, a transformer-based VSR model with masked intra- and inter-frame attention (MIA-VSR) is employed to reconstruct temporally coherent high-resolution video sequences from degraded inputs. A domain-specific super-resolved dataset is subsequently constructed to train a lightweight one-stage detector (You Only Look One-level Feature, YOLOF) for efficient vehicle localisation. Extensive experiments on public datasets (REDS, Vimeo90k, UA-DETRAC) demonstrate that the proposed framework achieved a 56.89 mAP@0.5 on low-resolution UA-DETRAC, outperforming both direct low-resolution inference (39.17 mAP@0.5) and conventional fine-tuning strategies (45.70 mAP@0.5) by 17.72 and 11.19 points, respectively. These findings indicate that super-resolution-driven data reconstruction provides an effective pathway for mitigating feature degradation in low-quality surveillance environments, offering both theoretical insight and practical value for intelligent transportation perception systems. Full article

A great deal of effort has been made to investigate and develop approaches to address driver behavior, fatigue, and sleepiness for different road users worldwide. However, very little research has been conducted to explore these issues in the context of Bus Rapid Transit (BRT) drivers in a low-income countries such as Mexico. The present study fills this gap. The aim of this study is to identify the human factors contributing to aberrant driver behavior (ADB) among BRT professional drivers in Mexico City. A total of 152 drivers participated in a self-reported survey. Exploratory factor analysis was performed on the BRT-ADBQ to identify the behavioral factors, and the Checklist Individual Strength (CIS–Fatigue) subscale was employed to assess the fatigue of drivers. The key findings were the following: (a) the created BRT-ABDQ identified two ADBs (violations and errors); (b) violations factors, but not errors, contributed to accident involvement; (c) ADB, fatigue, poor sleep and age (30–39) were predictors to accidents and (d) a linear trend has been revealed indicating that as the hours of sleep decreased, the experience of fatigue increased proportionally. The conclusion of the study is that ADB, sleepiness, and fatigue are real and existent among BRT drivers and should be a matter of concern for the case of the BRT organization that participated in the study. More generally, organizations running these systems should intervene by implementing sleep and fatigue reduction strategies to mitigate the adverse impact of these and thereby contribute to sustainable traffic safety and urban mobility. Full article

Primary Objective: To characterize high-resolution computed tomography (HRCT) fracture patterns, namely orientation and otic capsule status, among Sudanese patients with acute temporal bone trauma. Secondary Objectives: (i) To quantify the prevalence and pattern of concomitant craniofacial fractures, (ii) to describe early audiologic outcomes, and (iii) to document facial nerve dysfunction. Methods: Prospective cross-sectional study of 45 consecutive patients (≥5 years) with HRCT-confirmed TBF sustained within 7 days of injury, managed at two tertiary otolaryngology centers in Khartoum (October 2022–March 2023). All imaging, clinical, and audiologic variables were recorded once at the index presentation (≤7 days after trauma); the study did not include longitudinal follow-up. Two blinded experts independently classified fracture orientation (longitudinal, transverse, mixed/oblique), otic capsule status (sparing [OCS] vs. otic capsule-violating [OCV]), and ancillary HRCT signs (ossicular chain disruption, tympanic plate fracture, pneumolabyrinth/CSF leak); inter-observer reliability was assessed with Cohen’s κ. Concomitant craniofacial fractures, pure-tone audiometry, and House–Brackmann facial nerve grades were recorded. Predictor–outcome associations were examined with χ² statistics (p < 0.05). Results: Mean age 35.9 ± 17.4 years; 78% male. Road traffic accidents were associated with 58% of injuries. HRCT showed 60% longitudinal, 20% transverse, and 20% mixed/oblique fractures; 27% were OCV. Ossicular chain disruption, tympanic plate fracture, and ppneumolabyrinthCSF leak were present in 17.8%, 13.3%, and 8.9%, respectively. Concomitant craniofacial fractures occurred at 27%, chiefly Lefort III (15.6%) and Lefort II (8.9%). Transverse/mixed fractures were strongly associated with Lefort II–III injuries (χ² = 16.2, p = 0.001); age (p = 0.21) and sex (p = 0.08) were non-significant. Conductive, sensorineural, and mixed hearing loss affected 69%, 13%, and 18%; facial nerve palsy occurred in 58%. Inter-observer agreement was substantial to almost perfect for all imaging variables (κ = 0.77–0.92). Conclusions: Although longitudinal fractures predominated, over one-quarter breached the otic capsule and one-fifth followed transverse/mixed planes, configurations associated with higher odds of conductive deafness, facial nerve palsy, and complex mid-facial fractures. HRCT provides reliable characterization and should underpin comprehensive head-and-mid-face trauma protocols. Enhanced road safety policies and multidisciplinary trauma care are vital for reducing neuro-otologic morbidity in resource-limited settings. Full article

Intrusion detectors are often evaluated using average metrics at unconstrained thresholds, yet deployments require explicit control over false alarms. We investigate zero-day (out-of-distribution, OOD) intrusion detection under a target-FPR calibrated protocol, where a threshold is set on benign validation traffic to satisfy a target false positive rate α and transferred, unchanged, to a seen-test and OOD-test. Using CICIDS2017-derived host-session nodes aggregated in 1 min and 5 min windows, we compare tabular baselines, message-passing GNNs on a rule-based graph, and employ a method that builds a k-nearest-neighbor similarity graph with lightweight feature pre-smoothing. Robustness is measured using the OOD violation ratio, percentile tail risk, and feasibility under explicit false-alarm budgets. Base-graph GNNs exhibit heavy-tailed false-alarm amplification under OOD shifts: at α = 0.001, the p95 violation ratio reaches 68.50 (1 m) and 67.95 (5 m). In contrast, the proposed method reduces p95 to 3.41 (1 m) and 1.15 (5 m) and improves budget feasibility. We further verify robustness beyond a single held-out family by evaluating additional unseen-family splits (e.g., DDoS and DDoS+DoS) under the same calibrated operating point. We also quantify deployment-oriented cost via edge-list size and practical parsing/loading time. These findings suggest that similarity-based graphs with light pre-smoothing improve deployability under distribution shifts. Full article

The escalating global rates of traffic accidents in urban areas and the growing demands of smart cities underscore the urgent need for advanced real-time monitoring solutions. This paper presents an EdgeML-based system for vehicle tracking that performs real-time speed and distance analysis and traffic violation detection. This is achieved by deploying a YOLOv8 object detection model on a Raspberry Pi 5 with a Coral USB Edge TPU accelerator. The system integrates computer vision and IoT technologies to enable real-time processing. It utilizes the Message Queuing Telemetry Transport (MQTT) protocol to allow scalable communication between distributed edge devices and a central MongoDB database, facilitating real-time storage and analysis of traffic data. A synthetic dataset generated via the Blender 3D modeling tool validates the system’s accuracy, demonstrating average speed and distance measurement errors of ±2.11 km/h and ±0.58 m, respectively. These findings are further supported by preliminary practical experiments in a real-world environment, where speed estimation errors remained within 0–2 km/h and distance errors stayed below 0.11 m. Key innovations of this work include license plate recognition, speeding and collision detection, and context analysis using Google’s Gemini-2.5-Flash API. A Streamlit dashboard provides real-time visualization of traffic metrics, violations, and aggregated data. A comparative evaluation of YOLOv5n, YOLOv8n, YOLOv11n, and YOLOv12n identifies YOLOv8n as the most suitable model for embedded deployment, achieving 91.07 ± 0.61% mAP@0.5 without quantization, 88.77 ± 3.31% mAP@0.5 with quantization, while maintaining real-time performance of 30–43 frames per second (FPS) on the Edge TPU. The system’s modular architecture, low latency, and robust performance highlight its suitability for smart city applications, enhancing traffic safety and enabling data-driven urban mobility management. Full article

Traffic violations represent a significant public health concern, with professional drivers substantially impacting road safety. This pilot study compared self-report questionnaires (general personality versus domain-specific), performance-based tests, and driving simulator measures to determine which assessment method best predicts traffic offences among professional truck drivers. Participants (N = 27) completed the Impulsiveness–Venturesomeness–Empathy Questionnaire (IVE), the Road Traffic Behaviours Questionnaire (KZD), and the Vienna Risk-Taking Test Traffic (WRBTV) and performed standardised driving scenarios in a truck simulator. Performance was assessed using speed variations in five validated decision-making situations. Drivers were classified into two groups based on relatively higher and relatively lower numbers of self-reported traffic offences. The KZD demonstrated the strongest group differentiation (p = 0.034, d = 0.76). Simulator performance was significantly different between the groups (p = 0.033, d = −0.68), with offence-reporting drivers showing smaller speed reductions. The WRBTV and the IVE empathy subscale approached significance (p = 0.056 and p = 0.059, respectively). Higher empathy characterised offence-free drivers, suggesting social–emotional factors may contribute to traffic safety. General impulsiveness and venturesomeness showed no group differences. The results indicate that domain-specific questionnaires and behavioural assessments offer superior predictive validity compared to general personality measures for identifying potentially unsafe drivers. ROC analysis revealed moderate predictive validity across significant measures (AUC: 0.64–0.70), with differential patterns of sensitivity and specificity among predictors. The findings suggest implementing tiered screening approaches using domain-specific questionnaires as initial cost-effective tools, followed by simulator assessment for at-risk drivers, enabling transport companies and regulatory bodies to identify high-risk drivers proactively. Full article

The convergence of the Internet of Everything (IoE) and healthcare requires ultra-reliable, low-latency, and intelligent communication systems. Sixth-generation (6G) wireless networks, coupled with digital twin (DT) models and large AI models (LAMs), are envisioned to promise substantial and practically meaningful improvements in smart healthcare by enabling real-time monitoring, diagnosis, and personalized treatment. In this article, we propose an LAM-enhanced DT-driven network slicing framework for healthcare applications. The framework leverages large models to provide predictive insights and adaptive orchestration by creating virtual replicas of patients and medical devices that guide dynamic slice allocation. Reinforcement learning (RL) techniques are employed to optimize slice orchestration under uncertain traffic conditions, with LAMs augmenting decision-making through cognitive-level reasoning. Numerical results show that the proposed LAM–DT–RL framework reduces service-level agreement (SLA) violations by approximately 42–43% compared to a reinforcement-learning-only slicing strategy, while improving spectral efficiency and fairness among heterogeneous healthcare services. Finally, we outline open challenges and future research opportunities in integrating LAMs, DTs, and 6G for resilient healthcare IoE systems. Full article

Road transport across Asia is undergoing rapid motorisation and exemplifies growing road safety challenges, with rising accident rates closely linked to driver behaviour. Recent reports indicate that Vietnamese drivers often perceive risk as manageable and enforcement as inconsistent, contributing to habitual violations such as speeding, signal ignoring, and risky manoeuvres, particularly when traffic is light. Evidence shows that riders, especially young adults, feel confident controlling their vehicles and frequently disregard safety warnings. This study investigates traffic safety awareness among motorcyclists and car drivers in Hanoi, based on a questionnaire survey of 393 respondents. Principal Component Analysis (PCA) was used to group 11 attitudinal statements into key components influencing road safety perceptions, identifying five: non-compliance with traffic regulations (Component 1), aggressive driving behaviour (Component 2), traffic signal issues (Component 3), road quality and infrastructure (Component 4), and preventive measures (Component 5). Multiple Correspondence Analysis (MCA) and two-step cluster analysis (TCA) were then applied to determine user clusters by socio-demographic characteristics, producing three groups: young adults in employment riding motorcycles (Cluster 1), young adults in education riding motorcycles (Cluster 2), and mature adults in employment driving cars (Cluster 3). Finally, Multinomial Logistic Regression (MLR) was applied to assess variations in road safety perceptions across the different groups (clusters). Mature adults driving cars (Cluster 3) identified the first four components as significant, with Components 1 and 2 showing negative associations and Components 3 and 4 positive associations. Full article

Urban intersections are critical nodes for roadway safety, congestion management, and autonomous vehicle coordination. Traditional traffic control systems based on fixed-time signals and static sensors lack adaptability to real-time risks such as red-light violations, near-miss incidents, and multimodal conflicts. This study presents a grid-enabled framework integrating computer vision and machine learning to enhance real-time intersection intelligence and road safety. The system overlays a computational grid on the roadway, processes live video feeds, and extracts dynamic parameters including vehicle trajectories, deceleration patterns, and queue evolution. A novel active learning module improves detection accuracy under low visibility and occlusion, reducing false alarms in collision and violation detection. Designed for edge-computing environments, the framework interfaces with signal controllers to enable adaptive signal timing, proactive collision avoidance, and emergency vehicle prioritization. Case studies from multiple intersections typical of US cities show improved phase utilization, reduced intersection conflicts, and enhanced throughput. A grid-based heatmap visualization highlights spatial risk zones, supporting data-driven decision-making. The proposed framework bridges static infrastructure and intelligent mobility systems, advancing safer, smarter, and more connected roadway operations. Full article