Search Results (437)

In the field of electronic support and radar warning, it is necessary to effectively detect and recognize the modulation types of non-cooperative radar signals, especially for radars with Low Probability of Intercept (LPI) waveforms. Multiple intelligent detection and recognition algorithms based on the Transformer architecture have been proposed, achieving good performance even under low signal-to-noise ratio (SNR). However, Transformer-based radar intelligent detection and recognition algorithms have a huge number of parameters coupled with complex structures, which will result in significant power consumption and computational latency when deployed on general computing platforms. To address the above issues, this paper proposes a lightweight design for Transformer-based radar signal intelligent detection and recognition networks. A Lightweight Joint Detection and Modulation Recognition Networks (JDMR-LNet) is designed. To enhance the feature extraction ability of lightweight networks, this paper designed a hybrid model distillation method. The experimental results demonstrate that, compared with the directly trained JDMR-LNet, the accuracy of automatic modulation type recognition of the JDMR-LNet after distillation is increased by 2.37% at −12 dB, and the signal detection is increased by 2.07% at −10 dB. The number of parameters of the JDMR-LNet has also decreased significantly. Compared with the original model, the JDMR-LNet is compressed by 11.18 times. Furthermore, this paper completed FPGA deployment of the JDMR-LNet model, with simulation verifying its functional correctness. Full article

Assistive devices in recent years have transitioned from a passive mode of operation to the integration of smart solutions that enable humans to interact with active and robotic platforms. The main problems in the evolution of this kind of device are accessibility in terms of price and the functional limitations of the smart integrated solutions. This project proposes an armrest prototype for integration into smart walkers or wheelchairs that can detect the user’s intentions at a low development cost. The smart principle of operation is based on Hall-effect sensors, strategically positioned to measure the Center of Pressure (CoP) of the user’s forearm and to classify motor intention using machine learning algorithms such as Random Forest and Leave-One-Subject-Out (LOSO). The detection and correct classification of the user’s intention is a tool that can be integrated as a control system for both motorized and passive assistive devices. Full article

This work proposes a multiple unmanned aerial vehicles (UAVs) cooperative trajectory planning scheme constructed by multi-agent reinforcement learning with hybrid critics, improving the searching and tracking efficiency and fairness when the dynamic unmanned surface vehicle (USV) swarm exceeds the number of UAVs. A confidence map of targets’ existence probability with spatio-temporal decay is first established through a local information fusion mechanism based on Bayesian update theory. It leads to a reformulation of the problem model into a communication-enhanced partially observable Markov decision process. To suppress policy variance and credibility imbalance of the multi-UAVs, a center-sub-critics deep deterministic policy gradient algorithm is then proposed, combining multiple centralized critics with decentralized critics. Meanwhile, a segmented reward function is designed to incentivize the UAV to revisit detected targets. Finally, the simulation results compared with diverse baseline algorithms demonstrate the efficacy and scalability of the proposed scheme in this paper. Full article

This study developed an integrated vision–actuation system for precision weeding in indoor soil bin environments, with cabbage as a case example. The system integrates lightweight object detection, 3D co-ordinate mapping, path planning, and a three-axis synchronized conveyor-type actuator to enable precise weed identification and automated removal. By integrating ECA and CBAM attention mechanisms into YOLO11, we developed the YOLO11-WeedNet model. This integration significantly enhanced the detection performance for small-scale weeds under complex lighting and cluttered backgrounds. Based on the optimal model performance achieved during experimental evaluation, the model achieved 96.25% precision, 86.49% recall, 91.10% F1-score, and a mean Average Precision (mAP@0.5) of 91.50% calculated across two categories (crop and weed). An RGB-D fusion localization method combined with a protected-area constraint enabled accurate mapping of weed spatial positions. Furthermore, an enhanced Artificial Hummingbird Algorithm (AHA⁺) was proposed to optimize the execution path and reduce the operating trajectory while maintaining real-time performance. Indoor soil bin tests showed positioning errors of less than 8 mm on the X/Y axes, depth control within ±1 mm on the Z-axis, and an average weeding rate of 88.14%. The system achieved zero contact with cabbage seedlings, with a processing time of 6.88 s per weed. These results demonstrate the feasibility of the proposed system for precise and automated weeding at the cabbage seedling stage. Full article

The Intelligent Road Side Unit (RSU) is a crucial component of Intelligent Transportation Systems (ITSs), where roadside LiDAR are widely utilized for their high precision and resolution. However, water droplets and atmospheric particles in fog significantly attenuate and scatter LiDAR beams, posing a challenge to multi-target tracking and ITS safety. To enhance the accuracy and reliability of RSU-based tracking, a collaborative RSU method that integrates denoising and tracking for multi-target tracking is proposed. The proposed approach first dynamically adjusts the filtering kernel scale based on local noise levels to effectively remove noisy point clouds using a modified bilateral filter. Subsequently, a multi-RSU cooperative tracking framework is designed, which employs a particle Probability Hypothesis Density (PHD) filter to estimate target states via measurement fusion. A multi-target tracking system for intelligent RSUs in Foggy scenarios was designed and implemented. Extensive experiments were conducted using an intelligent roadside platform in real-world fog-affected traffic environments to validate the accuracy and real-time performance of the proposed algorithm. Experimental results demonstrate that the proposed method improves the target detection accuracy by 8% and 29%, respectively, compared to statistical filtering methods after removing fog noise under thin and thick fog conditions. At the same time, this method performs well in tracking multi-class targets, surpassing existing state-of-the-art methods, especially in high-order evaluation indicators such as HOTA, MOTA, and IDs. Full article

With the increasing deployment of grid-forming static var generators (GFM-SVG) in modern power systems, the reliability of the valve hall that houses the core power modules has become a critical concern. To overcome the limitations of conventional wired monitoring systems—complex cabling, poor scalability, and incomplete state perception—this paper proposes and implements a multi-source fusion wireless data acquisition system specifically designed for GFM-SVG valve halls. The system integrates acoustic, visual, and infrared sensing nodes into a wireless sensor network (WSN) to cooperatively capture thermoacoustic visual multi-physics information of key components. A dual-mode communication scheme, using Wireless Fidelity (Wi-Fi) as the primary link and Fourth-Generation Mobile Communication Network (4G) as a backup channel, is adopted together with data encryption, automatic reconnection, and retransmission-checking mechanisms to ensure reliable operation in strong electromagnetic interference environments. The main innovation lies in a multi-source information fusion algorithm based on an improved Dempster–Shafer (D–S) evidence theory, which is combined with the object detection capability of the You Only Look Once, Version 8 (YOLOv8) model to effectively handle the uncertainty and conflict of heterogeneous data sources. This enables accurate identification and early warning of multiple types of faults, including local overheating, abnormal acoustic signatures, and coolant leakage. Experimental results demonstrate that the proposed system achieves a fault-diagnosis accuracy of 98.5%, significantly outperforming single-sensor approaches, and thus provides an efficient and intelligent operation-and-maintenance solution for ensuring the safe and stable operation of GFM-SVG equipment. Full article

The article examines the application of artificial intelligence (AI) in the fight against disinformation through a comparative analysis of different European initiatives collected by the SmartVote project. It analyzes their characteristics and contributions to identify common trends in technological development and collaboration models. The methodology combines a systematic documentary analysis of institutional and technical sources—public project records, reports, and official repositories—with a structured questionnaire addressed to the coordinators of the selected initiatives. This mixed approach made it possible to triangulate quantitative and qualitative information on the types of technology employed, areas of impact, stakeholders involved, and levels of funding. The results show a predominance of multimodal AI-based tools aimed at automated content detection and verification. Most of the projects rely on cooperation networks among universities, technology companies, media outlets, and social organizations, structured under the principle of human oversight. The main challenges include algorithmic accuracy, bias prevention, and Europe’s technological dependence. Overall, the initiatives studied are committed to transparency, interdisciplinary collaboration, and the ethical use of AI in defense of informational integrity. Full article

To address the fault location problem in multi-terminal hybrid overhead–cable transmission lines with multiple sections, this paper proposes a novel method combining Modified Ensemble Empirical Mode Decomposition (MEEMD) and the Teager Energy Operator (TEO). First, the MEEMD algorithm—which mitigates mode mixing—is integrated with the TEO, which captures instantaneous energy variations, to achieve accurate detection of traveling wavefronts. Considering the topological complexity of multi-terminal hybrid transmission lines, a fault branch separation and iterative judgment method is proposed. Based on the arrival time characteristics of traveling waves, two topology decoupling strategies are designed to enable branch identification through network reconstruction and iterative computation. After determining the faulted branch, the fault section is precisely localized by comparing the time difference between the arrival of traveling waves at branch terminals and T-nodes with the propagation time differences at each connection point. Finally, the dual-ended traveling wave method is applied to calculate the fault distance. The proposed method is validated through co-simulation using PSCAD 4.6.2 and MATLAB R2023b. Comparative analysis of ranging accuracy demonstrates that this approach ensures reliable fault location under varying fault positions and transition resistances. Full article

Accurate measurement of wall shear stress (WSS) is essential for both fundamental and applied fluid dynamics, where it governs boundary-layer behavior, drag generation, and the performance of flow-control systems. Yet, existing WSS sensing methods remain limited by low spatial resolution, complex instrumentation, or the need for user-dependent calibration. This work introduces a method based on artificial intelligence (AI) and Oil-Film Interferometry, referred to as AI-OFI, that transforms a classical optical technique into an automated and sensor-like platform for local WSS detection. The method combines the non-intrusive precision of Oil-Film Interferometry with modern deep-learning tools to achieve fast and fully autonomous data interpretation. Interference patterns generated by a thinning oil film are first segmented in real time using a YOLO-based object detection network and subsequently analyzed through a modified VGG16 regression model to estimate the local film thickness and the corresponding WSS. A smart interrogation-window selection algorithm, based on 2D Fourier analysis, ensures robust fringe detection under varying illumination and oil distribution conditions. The AI-OFI system was validated in the high-Reynolds-number Long Pipe Facility at the Centre for International Cooperation in Long Pipe Experiments (CICLoPE), showing excellent agreement with reference pressure-drop measurements and conventional OFI, with an average deviation below 5%. The proposed framework enables reliable, real-time, and operator-independent wall shear stress sensing, representing a significant step toward next-generation optical sensors for aerodynamic and industrial flow applications. Full article

Real-Time Precise Orbit Determination (RTPOD) of Low Earth Orbit (LEO) satellites relies primarily on onboard GNSS observations and may suffer from degraded performance when observation geometry weakens or tracking conditions deteriorate within satellite formations. To enhance the robustness and accuracy of RTPOD under such conditions, a cooperative Extended Kalman Filter (EKF) framework that fuses onboard GNSS and inter-satellite link (ISL) range measurements is established, integrated with an iterative Detection, Identification, and Adaptation (DIA) quality control algorithm. By introducing high-precision ISL range measurements, the strategy increases observation redundancy, improves the effective observation geometry, and provides strong relative position constraints among LEO satellites. This constraint strengthens solution stability and convergence, while simultaneously enhancing the sensitivity of the DIA-based quality control to observation outliers. The proposed strategy is validated in a simulated real-time environment using Centre National d’Etudes Spatiales (CNES) real-time products and onboard observations of the GRACE-FO mission. The results demonstrate comprehensive performance enhancements for both satellites over the experimental period. For the GRACE-D satellite, which suffers from about 17% data loss and a cycle slip ratio several times higher than that of GRACE-C, the mean orbit accuracy improves by 39% (from 13.1 cm to 8.0 cm), and the average convergence time is shortened by 44.3%. In comparison, the GRACE-C satellite achieves a 4.2% mean accuracy refinement and a 1.3% reduction in convergence time. These findings reveal a cooperative stabilization mechanism, where the high-precision spatiotemporal reference is transferred from the robust node to the degraded node via inter-satellite range measurements. This study demonstrates the effectiveness of the proposed method in enhancing the robustness and stability of formation orbit determination and provides algorithmic validation for future RTPOD of LEO satellite formations or large-scale constellations. Full article

Shadow detection has become a key technology for ground-based moving target indication in video synthetic aperture radar (SAR). However, single-platform video SAR faces the issue of moving-target shadows being occluded. This paper proposes a new dynamic programming-based spatiotemporal track-before-detect (DP-ST-TBD) algorithm for moving-target shadow indication based on a distributed unmanned aerial vehicle (UAV)-borne video SAR system. First, this approach establishes a spatiotemporal cooperative shadow detection model, which extends the temporal accumulation of traditional DP-TBD to spatiotemporal accumulation by state temporal transition and spatial mapping. Second, an adaptive state transition method is proposed to address the challenge in which the fixed-state transition of traditional DP-TBD struggles with maneuvering target detection. It utilizes target’s Doppler features from heterogeneous-view range-Doppler (RD) spectra to assist in target’s shadow search within the image domain. Finally, a state shrinking–sparseness strategy is used to reduce the computational burden caused by dense states in spatiotemporal search; thus, multi-platform, multi-frame accumulation of moving-target shadows can be realized based on sparse states. The comparative experiments demonstrate that the proposed DP-ST-TBD improves shadow-detection performance through heterogeneous-view measurements while reducing the required number of frames for reliable detection compared to the conventional two-step detection method (single-platform shadow detection followed by multi-platform track fusion). Full article

With the continuous proliferation of radio frequency devices, electromagnetic environments in various regions are becoming increasingly complex. Effective monitoring of the electromagnetic environment and identification of interference sources have thus become critical tasks for maintaining order in the electromagnetic spectrum. In recent years, rapid advances in UAV technology have spurred exploration of UAV-based electromagnetic spectrum monitoring as a novel approach. However, the limited payload capacity and endurance of UAVs constrain their monitoring capabilities. To address these challenges, we propose HMUDRL, a distributed heterogeneous multi-agent deep reinforcement learning algorithm. By leveraging cooperative operation between cluster-head UAVs (CH) and cluster-monitoring UAVs (CM) within a heterogeneous UAV swarm, HMUDRL enables high-precision detection and wide-area localization of UHF radiation source. Furthermore, we integrate a minimum-gap localization algorithm that exploits the spatial distribution of multiple CM to accurately pinpoint anomalous radiation sources. Simulation results validate the effectiveness of HMUDRL: in the later stages of training, the success rate of localizing target radiation sources converges to 96.1%, representing an average improvement of 1.8% over baseline algorithms; localization accuracy, measured by root mean square error (RMSE), is enhanced by approximately 87.3% compared to baselines; and communication overhead is reduced by more than 80% relative to homogeneous architectures. These results demonstrate that HMUDRL effectively addresses the challenges of data transmission control and sensing-localization performance faced by UAVs in UHF spectrum monitoring. Full article

The development of cross-border hydrogen energy value chains involves complex interactions between technological, regulatory, and logistical subsystems. Static assessment models often fail to capture the dynamic response of these coupled systems to external perturbations. This study addresses this gap by proposing the Dual Carbon Cooperation Index (DCCI), a data-driven framework designed to quantify the synergy efficiency of the China–Korea hydrogen ecosystem. We construct a dynamic state estimation model integrating three coupled dimensions—Technology Synergy, Regulatory Alignment, and Supply Chain Resilience—utilizing an adaptive weighting algorithm (Triple Dynamic Response). Based on multi-source heterogeneous data (2020–2024), the model employs Natural Language Processing (NLP) for vectorizing unstructured regulatory texts and incorporates an exogenous signal detection mechanism (GPR). Empirical results reveal that the ecosystem’s composite synergy score recovered from 0.38 to 0.50, driven by robust supply chain resilience but constrained by high impedance in technological transfer protocols. Crucially, the novel dynamic weighting algorithm significantly reduces state estimation error during high-volatility periods compared to static linear models, as validated by bootstrapping analysis (1000 resamples). The study provides a quantitative engineering tool for monitoring ecosystem coupling stability and proposes a technical roadmap for reducing system constraints through secure IP data architectures and synchronized standard protocols. Full article

Accurate Unmanned Aerial Vehicle (UAV) positioning is vital for swarm cooperation. However, this remains challenging in situations where Global Navigation Satellite System (GNSS) and other external infrastructures are unavailable. To address this challenge, we propose to use only the onboard Microelectromechanical System Inertial Measurement Unit (MIMU), Magnetic sensor, Monocular camera and Ultra-Wideband (UWB) device to construct a distributed and anchor-free cooperative localization system by tightly fusing the measurements. As the onboard UWB measurements under dynamic motion conditions are noisy and discontinuous, we propose an adaptive adjustment method based on chi-squared detection to effectively filter out inconsistent and false ranging information. Moreover, we introduce the pose-only theory to model the visual measurement, which improves the efficiency and accuracy for visual-inertial processing. A sliding window Extended Kalman Filter (EKF) is constructed to tightly fuse all the measurements, which is capable of working under UWB or visual deprived conditions. Additionally, a novel Multidimensional Scaling-MAP (MDS-MAP) initialization method fuses ranging, MIMU, and geomagnetic data to solve the non-convex optimization problem in ranging-aided Simultaneous Localization and Mapping (SLAM), ensuring fast and accurate swarm absolute pose initialization. To overcome the state consistency challenge inherent in the distributed cooperative structure, we model not only the UWB noisy uncertainty but also the neighbor agent’s position uncertainty in the measurement model. Furthermore, we incorporate the Covariance Intersection (CI) method into our UWB measurement fusion process to address the challenge of unknown correlations between state estimates from different UAVs, ensuring consistent and robust state estimation. To validate the effectiveness of the proposed methods, we have established both simulation and hardware test platforms. The proposed method is compared with state-of-the-art (SOTA) UAV localization approaches designed for GNSS-challenged environments. Extensive experiments demonstrate that our algorithm achieves superior positioning accuracy, higher computing efficiency and better robustness. Moreover, even when vision loss causes other methods to fail, our proposed method continues to operate effectively. Full article

Dynamic task planning for heterogeneous platforms across land, sea, air, and space is essential for achieving integrated situational awareness, yet current systems suffer from limited spatiotemporal coverage and inefficient resource scheduling. To address these challenges, we propose a novel mission planning method that integrates spatiotemporal segmentation with Deep Reinforcement Learning (DRL). The approach establishes a multidimensional spatiotemporal decomposition model to break down complex observation scenarios into manageable subtasks, while incorporating a unified accessibility–visibility computation framework that accounts for Earth curvature, platform dynamics, and sensor constraints. Using a Spatio-Temporal Adaptive Scheduling Network (STAS-Net) algorithm optimized with a multi-objective reward function covering mission completion rate, temporal coordination, and residual detection capacity, the method enables intelligent coordination of heterogeneous platforms. Experimental results across small-, medium-, and large-scale scenarios demonstrate that the proposed framework consistently achieves high target coverage (up to 98.4% in small-scale and 89.7% in large-scale tasks), with a reduction in coverage loss that is only about half of that exhibited by greedy and genetic algorithms as task scale expands. Moreover, STAS-Net maintains low planning time (as low as 9.5 s in small-scale and only 18.3 s in large-scale scenarios) and high resource utilization (reaching 86.8% under large-scale settings), substantially outperforming both baseline methods in scalability and scheduling efficiency. The framework not only establishes a solid theoretical foundation but also provides a practical and feasible solution for enhancing the overall performance of multi-platform cooperative observation systems. Full article