Search Results (274)

Target detection is essential in intelligent transportation and autonomous control of unmanned aerial vehicles (UAVs), with single-stage detection algorithms used widely due to their speed. However, these algorithms face limitations in detecting small targets, especially in aerial photography from unmanned aerial vehicles (UAVs), where small targets are often occluded, multi-scale semantic information is easily lost, and there is a trade-off between real-time processing and computational resources. Existing algorithms struggle to effectively extract multi-dimensional features and deep semantic information from images and to balance detection accuracy with model complexity. To address these limitations, we developed FFG-YOLO, a lightweight small-target detection method for UAVs based on YOLOv8. FFG-YOLO incorporates three modules: a feature enhancement block (FEB), a feature concat block (FCB), and a global context awareness block (GCAB). These modules strengthen feature extraction from small targets, resolve semantic bias in multi-scale feature fusion, and help differentiate small targets from complex backgrounds. We also improved the positioning accuracy of small targets using the Wasserstein distance loss function. Experiments showed that FFG-YOLO outperformed other algorithms, including YOLOv8n, in small-target detection due to its lightweight nature, meeting the stringent real-time performance and deployment requirements of UAVs. Full article

Unmanned Aerial Vehicles (UAVs) face significant challenges in global path planning within complex terrains, as traditional algorithms (e.g., A*, PSO, APF) struggle to balance computational efficiency, path optimality, and safety. This study proposes a Voronoi–A* fusion algorithm, combining Voronoi-vertex-based rapid trajectory generation with A* supplementary expansion for enhanced performance. First, an adaptive DEM layering strategy divides the terrain into horizontal planes based on obstacle density, reducing computational complexity while preserving 3D flexibility. The Voronoi vertices within each layer serve as a sparse waypoint network, with greedy heuristic prioritizing vertices that ensure safety margins, directional coherence, and goal proximity. For unresolved segments, A* performs localized searches to ensure complete connectivity. Finally, a line-segment interpolation search further optimizes the path to minimize both length and turning maneuvers. Simulations in mountainous environments demonstrate superior performance over traditional methods in terms of path planning success rates, path optimality, and computation. Our framework excels in real-time scenarios, such as disaster rescue and logistics, although it assumes static environments and trades slight path elongation for robustness. Future research should integrate dynamic obstacle avoidance and weather impact analysis to enhance adaptability in real-world conditions. Full article

This paper proposes a novel Twin Delayed Deep Deterministic Policy Gradient (TDDPG)-based joint optimization algorithm for hybrid reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) systems in Internet of Vehicles (IoV) scenarios. The proposed system model achieves deep integration of sensing and communication by superimposing the communication and sensing signals within the same waveform. To decouple the complex joint design problem, a dual-DDPG architecture is introduced, in which one agent optimizes the transmit beamforming vector and the other adjusts the RIS phase shift matrix. Both agents share a unified reward function that comprehensively considers multi-user interference (MUI), total transmit power, RIS noise power, and sensing accuracy via the CRLB constraint. Simulation results demonstrate that the proposed TDDPG algorithm significantly outperforms conventional DDPG in terms of sum rate and interference suppression. Moreover, the adoption of a hybrid RIS enables an effective trade-off between communication performance and system energy efficiency, highlighting its practical deployment potential in dynamic IoV environments. Full article

As global climate change intensifies and the transition to clean energy accelerates, lithium-ion batteries—critical components of electric vehicles—are becoming increasingly vital in international trade networks. This study investigates the structural evolution and determinants of the electric vehicle lithium-ion battery trade network among European Union (EU) member states from 2012 to 2023, employing social network analysis and the multiple regression quadratic assignment procedure method. The findings demonstrate the transformation of the network from a centralized and loosely connected structure, with Germany as the dominant hub, to a more interconnected and decentralized system in which Poland and Hungary emerge as the leading players. Key network metrics, such as the density, clustering coefficients, and average path lengths, reveal increased regional trade connectivity and enhanced supply chain efficiency. The analysis identifies geographic and economic proximity, logistics performance, labor cost differentials, energy resource availability, and venture capital investment as significant drivers of trade flows, highlighting the interaction among spatial, economic, and infrastructural factors in shaping the network. Based on these findings, this study underscores the need for targeted policy measures to support Central and Eastern European countries, including investment in logistics infrastructure, technological innovation, and regional cooperation initiatives, to strengthen their integration into the supply chain and bolster their export capacity. Furthermore, fostering balanced inter-regional collaborations is essential in building a resilient trade network. Continued investment in transportation infrastructure and innovation is recommended to sustain the EU’s competitive advantage in the global electric vehicle lithium-ion battery supply chain. Full article

The Internet of Vehicles (IoV) presents a vast opportunity for optimised traffic flow, road safety, and enhanced usage experience with the influence of Federated Learning (FL). However, the distributed nature of IoV networks creates certain inherent problems regarding data privacy, security from adversarial attacks, and the handling of available resources. This paper introduces Fed-DTB, a new dynamic trust-based framework for FL that aims to overcome these challenges in the context of IoV. Fed-DTB integrates the adaptive trust evaluation that is capable of quickly identifying and excluding malicious clients to maintain the authenticity of the learning process. A performance comparison with previous approaches is shown, where the Fed-DTB method improves accuracy in the first two training rounds and decreases the per-round training time. The Fed-DTB is robust to non-IID data distributions and outperforms all other state-of-the-art approaches regarding the final accuracy (87–88%), convergence rate, and adversary detection (99.86% accuracy). The key contributions include (1) a multi-factor trust evaluation mechanism with seven contextual factors, (2) correlation-based adaptive weighting that dynamically prioritises trust factors based on vehicular conditions, and (3) an optimisation-based client selection strategy that maximises collaborative reliability. This work opens up opportunities for more accurate, secure, and private collaborative learning in future intelligent transportation systems with the help of federated learning while overcoming the conventional trade-off of security vs. efficiency. Full article

Autonomous Parking Systems (APSs) are rapidly evolving, promising enhanced convenience, safety, and efficiency. This review critically examines the current strategies in perception, path planning, and vehicle control, alongside system-level aspects like integration, validation, and security. While significant progress has been made, particularly with the advent of deep learning and sophisticated sensor fusion, formidable challenges persist. This paper delves into the inherent trade-offs, such as balancing computational cost with real-time performance demands; unresolved foundational issues, including the verification of non-deterministic AI components; and the profound difficulty of ensuring robust real-world deployment across diverse and unpredictable conditions, ranging from cluttered urban canyons to poorly lit, ambiguously marked parking structures. We also explore the limitations of current technologies, the complexities of safety assurance in dynamic environments, the pervasive impact of cost considerations on system capabilities, and the critical, often underestimated, need for genuine user trust. Future research must address not only these technological gaps with innovative solutions but also the intricate socio-technical dimensions to realize the full potential of APS. Full article

The integration of reconfigurable intelligent surfaces (RISs) with non-terrestrial networks (NTNs), particularly those enabled by unmanned aerial vehicles (UAVs) or drone-based platforms, has emerged as a transformative approach to enhance 6G connectivity in high-mobility scenarios. UAV-assisted NTNs offer flexible deployment, dynamic altitude control, and rapid network reconfiguration, making them ideal candidates for RIS-based signal optimization. However, the high mobility of UAVs and their three-dimensional trajectory dynamics introduce unique challenges in maintaining robust, low-latency links and seamless handovers. This paper presents a comprehensive performance analysis of RIS-assisted UAV-based NTNs, focusing on optimizing RIS phase shifts to maximize the signal-to-interference-plus-noise ratio (SINR), throughput, energy efficiency, and reliability under UAV mobility constraints. A joint optimization framework is proposed that accounts for UAV path loss, aerial shadowing, interference, and user mobility patterns, tailored specifically for aerial communication networks. Extensive simulations are conducted across various UAV operation scenarios, including urban air corridors, rural surveillance routes, drone swarms, emergency response, and aerial delivery systems. The results reveal that RIS deployment significantly enhances the SINR and throughput while navigating energy and latency trade-offs in real time. These findings offer vital insights for deploying RIS-enhanced aerial networks in 6G, supporting mission-critical drone applications and next-generation autonomous systems. Full article

Unmanned Aerial Vehicles (UAVs) face a significant challenge in balancing high accuracy and high efficiency when performing real-time object detection tasks, especially amidst intricate backgrounds, diverse target scales, and stringent onboard computational resource constraints. To tackle these difficulties, this study introduces YOLO-SRMX, a lightweight real-time object detection framework specifically designed for infrared imagery captured by UAVs. Firstly, the model utilizes ShuffleNetV2 as an efficient lightweight backbone and integrates the novel Multi-Scale Dilated Attention (MSDA) module. This strategy not only facilitates a substantial 46.4% reduction in parameter volume but also, through the flexible adaptation of receptive fields, boosts the model’s robustness and precision in multi-scale object recognition tasks. Secondly, within the neck network, multi-scale feature extraction is facilitated through the design of novel composite convolutions, ConvX and MConv, based on a “split–differentiate–concatenate” paradigm. Furthermore, the lightweight GhostConv is incorporated to reduce model complexity. By synthesizing these principles, a novel composite receptive field lightweight convolution, DRFAConvP, is proposed to further optimize multi-scale feature fusion efficiency and promote model lightweighting. Finally, the Wise-IoU loss function is adopted to replace the traditional bounding box loss. This is coupled with a dynamic non-monotonic focusing mechanism formulated using the concept of outlier degrees. This mechanism intelligently assigns elevated gradient weights to anchor boxes of moderate quality by assessing their relative outlier degree, while concurrently diminishing the gradient contributions from both high-quality and low-quality anchor boxes. Consequently, this approach enhances the model’s localization accuracy for small targets in complex scenes. Experimental evaluations on the HIT-UAV dataset corroborate that YOLO-SRMX achieves an ${\mathrm{mAP}}_{50}$ of 82.8%, representing a 7.81% improvement over the baseline YOLOv8s model; an F1 score of 80%, marking a 3.9% increase; and a substantial 65.3% reduction in computational cost (GFLOPs). YOLO-SRMX demonstrates an exceptional trade-off between detection accuracy and operational efficiency, thereby underscoring its considerable potential for efficient and precise object detection on resource-constrained UAV platforms. Full article

This paper presents a three-dimensional robust spatiotemporal cooperative guidance law for unmanned aerial vehicles (UAVs) to track a dynamic target under jointly strongly connected topologies, even when some UAVs malfunction. To resolve the infinite gain challenge in existing prescribed-time cooperative guidance laws, a novel bounded-gain prescribed-time stability criterion was formulated. This criterion allows the convergence time of the guidance law to be prescribed arbitrarily without any convergence performance trade-off. Firstly, new prescribed-time disturbance observers are designed to achieve accurate estimations of the target acceleration within a prescribed time regardless of initial conditions. Then, by leveraging a distributed convex hull observer, a tangential acceleration command is proposed to drive arrival times toward a common convex combination within a prescribed time under jointly strongly connected topologies, remaining effective even when partial UAVs fail. Moreover, by utilizing a prescribed-time nonsingular sliding mode control method, normal acceleration commands are developed to guarantee that the line-of-sight angles constraints can be satisfied within a prescribed time. Finally, numerical simulations validate the effectiveness of the proposed guidance law. Full article

As a fundamental technology in environmental perception, sound source localization (SSL) plays a critical role in public safety, marine exploration, and smart home systems. However, traditional methods such as beamforming and time-delay estimation rely on manually designed physical models and idealized assumptions, which struggle to meet practical demands in dynamic and complex scenarios. Recent advancements in deep learning have revolutionized SSL by leveraging its end-to-end feature adaptability, cross-scenario generalization capabilities, and data-driven modeling, significantly enhancing localization robustness and accuracy in challenging environments. This review systematically examines the progress of deep learning-based SSL across three critical domains: marine environments, indoor reverberant spaces, and unmanned aerial vehicle (UAV) monitoring. In marine scenarios, complex-valued convolutional networks combined with adversarial transfer learning mitigate environmental mismatch and multipath interference through phase information fusion and domain adaptation strategies. For indoor high-reverberation conditions, attention mechanisms and multimodal fusion architectures achieve precise localization under low signal-to-noise ratios by adaptively weighting critical acoustic features. In UAV surveillance, lightweight models integrated with spatiotemporal Transformers address dynamic modeling of non-stationary noise spectra and edge computing efficiency constraints. Despite these advancements, current approaches face three core challenges: the insufficient integration of physical principles, prohibitive data annotation costs, and the trade-off between real-time performance and accuracy. Future research should prioritize physics-informed modeling to embed acoustic propagation mechanisms, unsupervised domain adaptation to reduce reliance on labeled data, and sensor-algorithm co-design to optimize hardware-software synergy. These directions aim to propel SSL toward intelligent systems characterized by high precision, strong robustness, and low power consumption. This work provides both theoretical foundations and technical references for algorithm selection and practical implementation in complex real-world scenarios. Full article

The increasing integration of renewable energy, electric vehicles, and industrial applications demands efficient power converter control strategies that reduce switching losses while maintaining high waveform quality. This paper presents a Finite-Control-Set Model Predictive Control (FCS-MPC) strategy for three-phase, two-level voltage source inverters (VSIs), incorporating a secondary objective for switching frequency minimization. Unlike conventional MPC approaches, the proposed method optimally balances control performance and efficiency trade-offs by adjusting the weighting factor (${\lambda }_{min}$). Real-time implementation using the OPAL-RT platform validates the effectiveness of the approach under both linear and non-linear load conditions. Results demonstrate a significant reduction in switching losses, accompanied by improved waveform tracking; however, trade-offs in distortion are observed under non-linear load scenarios. These findings provide insights into the practical implementation of real-time predictive control strategies for high-performance power converters. Full article

Recent advances in unmanned aerial vehicle (UAV) technology combined with deep learning techniques have greatly improved agricultural monitoring. However, accurately processing images at low resolutions remains challenging for precision cultivation of succulents. To address this issue, this study proposes a novel method that combines cutting-edge super-resolution reconstruction (SRR) techniques with object detection and then applies the above model in a unified drone framework to achieve large-scale, reliable monitoring of succulent plants. Specifically, we introduce MambaIR, an innovative SRR method leveraging selective state-space models, significantly improving the quality of UAV-captured low-resolution imagery (achieving a PSNR of 23.83 dB and an SSIM of 79.60%) and surpassing current state-of-the-art approaches. Additionally, we develop Succulent-YOLO, a customized target detection model optimized for succulent image classification, achieving a mean average precision (mAP@50) of 87.8% on high-resolution images. The integrated use of MambaIR and Succulent-YOLO achieves an mAP@50 of 85.1% when tested on enhanced super-resolution images, closely approaching the performance on original high-resolution images. Through extensive experimentation supported by Grad-CAM visualization, our method effectively captures critical features of succulents, identifying the best trade-off between resolution enhancement and computational demands. By overcoming the limitations associated with low-resolution UAV imagery in agricultural monitoring, this solution provides an effective, scalable approach for evaluating succulent plant growth. Addressing image-quality issues further facilitates informed decision-making, reducing technical challenges. Ultimately, this study provides a robust foundation for expanding the practical use of UAVs and artificial intelligence in precision agriculture, promoting sustainable farming practices through advanced remote sensing technologies. Full article

Semantic mapping plays a critical role in enabling autonomous vehicles to understand and navigate complex environments. Instead of computationally demanding 3D segmentation of point clouds, we propose efficient segmentation on RGB images and projection of the corresponding LIDAR measurements on the semantic OctoMap. This study presents a comparative evaluation of different semantic segmentation methods and examines the impact of input image resolution on the accuracy of 3D semantic environment reconstruction, inference time, and computational resource usage. The experiments were conducted using an ROS 2-based pipeline that combines RGB images and LiDAR point clouds. Semantic segmentation is performed using ONNX-exported deep neural networks, with class predictions projected onto corresponding 3D LiDAR data using calibrated extrinsic. The resulting semantically annotated point clouds are fused into a probabilistic 3D representation using an OctoMap, where each voxel stores both occupancy and semantic class information. Multiple encoder–decoder architectures with various backbone configurations are evaluated in terms of segmentation quality, latency, memory footprint, and GPU utilization. Furthermore, a comparison between high and low image resolutions is conducted to assess trade-offs between model accuracy and real-time applicability. Full article

This study aims to address the critical lack of standardized guidelines for unmanned aerial vehicle (UAV) image acquisition strategies utilizing structure-from-motion (SfM) by focusing on 3D building exterior modeling. A comprehensive experimental analysis was conducted to systematically investigate and quantitatively evaluate the effects of various shooting patterns and parameters on SfM reconstruction quality and processing efficiency. This study implemented a systematic experimental framework to test various UAV flight patterns, including circular, surface, and aerial configurations. Under controlled environmental conditions on representative building structures, key variables were manipulated, and all collected data were processed through a consistent SfM pipeline based on the SIFT algorithm. Quantitative evaluation results using various analytical methodologies (multiple regression analysis, Kruskal–Wallis test, random forest feature importance, principal component analysis including K-means clustering, response surface methodology (RSM), preference ranking technique based on similarity to the ideal solution (TOPSIS), and Pareto optimization) revealed that the basic shooting pattern ‘type’ has a significant and statistically significant influence on all major SfM performance metrics (reprojection error, final point count, computation time, reconstruction completeness; Kruskal–Wallis p < 0.001). Additionally, within the patterns, clear parameter sensitivity and complex nonlinear relationships were identified (e.g., overlapping variables play a decisive role in determining the point count and completeness of surface patterns, with an adjusted R² ≈ 0.70; the results of circular patterns are strongly influenced by the interaction between radius and tilt angle on reprojection error and point count, with an adjusted R² ≈ 0.80). Furthermore, composite pattern analysis using TOPSIS identified excellent combinations that balanced multiple criteria, and Pareto optimization explicitly quantified the inherent trade-offs between conflicting objectives (e.g., time vs. accuracy, number of points vs. completeness). In conclusion, this study clearly demonstrates that hierarchical strategic approaches are essential for optimizing UAV-SfM data collection. Additionally, it provides important empirical data, a validated methodological framework, and specific quantitative guidelines for standardizing UAV data collection workflows, thereby improving existing empirical or case-specific approaches. Full article

Institutions are increasingly being challenged to reduce the environmental impacts of daily commuting, while balancing complex and often conflicting sustainability goals. This study addressed the limitations of carbon-centric assessments by proposing a framework that integrated life cycle assessment (LCA) with multi-criteria decision analysis (MCDA) to evaluate seven prospective commuting alternatives for 2030, using a Portuguese university as a case study. Utilizing the PROMETHEE method across 16 environmental criteria, the analysis revealed that active mobility offered the most balanced and sustainable outcomes, consistently performing the best across all impact categories. In contrast, the electrification of private vehicles, although it reduced greenhouse gas emissions, was identified as the least favorable option, due to significant trade-offs in areas such as resource depletion and water use, as well as other environmental burdens. Public transport scenarios, particularly those involving electric bus systems, showed intermediate performance. In this context, the proposed LCA–MCDA framework provides policymakers and institutions with a comprehensive decision-support tool to navigate environmental trade-offs, promote low-impact mobility strategies, and meet evolving sustainability reporting requirements. Full article