Search Results (190)

The Gunung Sewu UNESCO Global Geopark (GSUGGp) is one of Indonesia’s 12 UNESCO-designated geoparks. Its presence is expected to enhance rural development by boosting the local economy through tourism. However, there is a lack of statistical evidence quantifying the economic benefits of geopark development, mainly due to the complex, non-linear nature of these impacts and limited village-level economic data available in Indonesia. To address this gap, this study aims to measure how socio-economic and environmental factors contribute to the Village Development Index (VDI) within the GSUGGp area, which includes the districts of Gunung Kidul, Wonogiri, and Pacitan. A machine learning–deep learning approach was employed, utilizing four algorithms grouped into eight models, with hyperparameter tuning and cross-validation, tested on a sample of 92 villages. The analysis revealed insights into how 17 independent variables influence the VDI. The Artificial Neural Network (ANN) algorithm outperformed others, achieving an R-squared of 0.76 and an RMSE of 0.040, surpassing random forest, CART, SVM, and linear models. Economically related factors—considered the foundation of rural development—had the strongest impact on village progress within GSUGGp. Additionally, features related to tourism, especially beach tourism linked to geological landscapes, contributed significantly. These findings are valuable for guiding geopark management and policy decisions, emphasizing the importance of integrated strategies and strong cooperation among local governments at the regency and provincial levels. Full article

The increasing presence of non-cooperative targets poses significant challenges to the space environment and threatens the sustainability of aerospace operations. Accurate on-orbit perception of such targets, particularly those without cooperative markers, requires advanced algorithms and efficient system architectures. This study presents a hardware–software co-design framework for the pose estimation of non-cooperative targets. Firstly, a two-stage architecture is proposed, comprising object detection and pose estimation. YOLOv5s is modified with a Focus module to enhance feature extraction, and URSONet adopts global average pooling to reduce the computational burden. Optimization techniques, including batch normalization fusion, ReLU integration, and linear quantization, are applied to improve inference efficiency. Secondly, a customized FPGA-based accelerator is developed with an instruction scheduler, memory slicing mechanism, and computation array. Instruction-level control supports model generalization, while a weight concatenation strategy improves resource utilization during convolution. Finally, a heterogeneous DSP–FPGA system is implemented, where the DSP manages data pre-processing and result integration, and the FPGA performs core inference. The system is deployed on a Xilinx X7K325T FPGA operating at 200 MHz. Experimental results show that the optimized model achieves a peak throughput of 399.16 GOP/s with less than 1% accuracy loss. The proposed design reaches 0.461 and 0.447 GOP/s/DSP48E1 for two model variants, achieving a 2× to 3× improvement over comparable designs. Full article

To overcome the limitations in the perception performance of individual robots and homogeneous robot teams, this paper presents a distributed multi-robot collaborative SLAM method based on air–ground cross-domain cooperation. By integrating environmental perception data from UAV and UGV teams across air and ground domains, this method enables more efficient, robust, and globally consistent autonomous positioning and mapping. First, to address the challenge of significant differences in the field of view between UAVs and UGVs, which complicates achieving a unified environmental understanding, this paper proposes an iterative registration method based on semantic and geometric features assistance. This method calculates the correspondence probability of the air–ground loop closure keyframes using these features and iteratively computes the rotation angle and translation vector to determine the coordinate transformation matrix. The resulting matrix provides strong initialization for back-end optimization, which helps to significantly reduce global pose estimation errors. Next, to overcome the convergence difficulties and high computational complexity of large-scale distributed back-end nonlinear pose graph optimization, this paper introduces a multi-level partitioning majorization–minimization DPGO method incorporating loss kernel optimization. This method constructs a multi-level, balanced pose subgraph based on the coupling degree of robot nodes. Then, it uses the minimization substitution function of non-trivial loss kernel optimization to gradually converge the distributed pose graph optimization problem to a first-order critical point, thereby significantly improving global pose estimation accuracy. Finally, experimental results on benchmark SLAM datasets and the GRACO dataset demonstrate that the proposed method effectively integrates environmental feature information from air–ground cross-domain UAV and UGV teams, achieving high-precision global pose estimation and map construction. Full article

Specific emitter identification (SEI) is a core technology for wireless device security that plays a crucial role in protecting wireless communication systems from various security threats. However, current deep learning-based SEI methods heavily rely on large amounts of labeled data for supervised training, facing challenges in non-cooperative communication scenarios. To address these issues, this paper proposes a novel contrastive asymmetric masked learning-based SEI (CAML-SEI) method, effectively solving the problem of SEI under scarce labeled samples. The proposed method constructs an asymmetric auto-encoder architecture, comprising an encoder network based on channel squeeze-and-excitation residual blocks to capture radio frequency fingerprint (RFF) features embedded in signals, while employing a lightweight single-layer convolutional decoder for masked signal reconstruction. This design promotes the learning of fine-grained local feature representations. To further enhance feature discriminability, a learnable non-linear mapping is introduced to compress high-dimensional encoded features into a compact low-dimensional space, accompanied by a contrastive loss function that simultaneously achieves feature aggregation of positive samples and feature separation of negative samples. Finally, the network is jointly optimized by combining signal reconstruction and feature contrast tasks. Experiments conducted on real-world ADS-B and Wi-Fi datasets demonstrate that the proposed method effectively learns generalized RFF features, and the results show superior performance compared with other SEI methods. Full article

Arginine plays a critical role in biomolecular interactions due to its guanidinium side chain, which enables multivalent electrostatic and hydrogen bonding contacts. In this study, atomistic molecular dynamics simulations were conducted across a broad concentration range (26–605 mM) to investigate the thermodynamic and structural features of arginine self-assembly in aqueous solution. Key observables—including hydrogen bond count, radius of gyration, contact number, and isobaric heat capacity—were analyzed to characterize emergent behavior. A three-regime aggregation pattern (dilute, cooperative, and saturated) was identified and quantitatively modeled using the Hill equation, revealing a non-linear transition in clustering behavior. Spatial analyses were supplemented with trajectory-based clustering and radial distribution functions. The heat capacity peak observed near 360 mM was interpreted as a thermodynamic signature of hydration rearrangement. Trajectory analyses utilized both GROMACS tools and the MDAnalysis library. While force field limitations and single-replica sampling are acknowledged, the results offer mechanistic insight into how arginine concentration modulates molecular organization—informing the understanding of biomolecular condensates, protein–nucleic acid complexes, and the design of functional supramolecular systems. The findings are in strong agreement with experimental observations from small-angle X-ray scattering and differential scanning calorimetry. Overall, this work establishes a cohesive framework for understanding amino acid condensation and reveals arginine’s concentration-dependent behavior as a model for weak, reversible molecular association. Full article

Gait recognition, as a non-contact biometric technology, offers unique advantages in scenarios requiring long-distance identification without active cooperation from subjects. However, existing gait recognition methods predominantly rely on single-modal data, which demonstrates insufficient feature expression capabilities when confronted with complex factors in real-world environments, including viewpoint variations, clothing differences, occlusion problems, and illumination changes. This paper addresses these challenges by introducing a multi-modal gait recognition network based on channel shuffle regulation and spatial-frequency joint learning, which integrates two complementary modalities (silhouette data and heatmap data) to construct a more comprehensive gait representation. The channel shuffle-based feature selective regulation module achieves cross-channel information interaction and feature enhancement through channel grouping and feature shuffling strategies. This module divides input features along the channel dimension into multiple subspaces, which undergo channel-aware and spatial-aware processing to capture dependency relationships across different dimensions. Subsequently, channel shuffling operations facilitate information exchange between different semantic groups, achieving adaptive enhancement and optimization of features with relatively low parameter overhead. The spatial-frequency joint learning module maps spatiotemporal features to the spectral domain through fast Fourier transform, effectively capturing inherent periodic patterns and long-range dependencies in gait sequences. The global receptive field advantage of frequency domain processing enables the model to transcend local spatiotemporal constraints and capture global motion patterns. Concurrently, the spatial domain processing branch balances the contributions of frequency and spatial domain information through an adaptive weighting mechanism, maintaining computational efficiency while enhancing features. Experimental results demonstrate that the proposed GaitCSF model achieves significant performance improvements on mainstream datasets including GREW, Gait3D, and SUSTech1k, breaking through the performance bottlenecks of traditional methods. The implications of this research are significant for improving the performance and robustness of gait recognition systems when implemented in practical application scenarios. Full article

To address the issue of misclassification and diminished accuracy that is prevalent in existing intent recognition models for non-cooperative spacecraft due to the omission of environmental influences, this paper presents a novel recognition framework leveraging a hybrid neural network subject to multiple constraints. The relative orbital motion of the targets is characterized and categorized through the use of Clohessy–Wiltshire equations, forming the foundation of a constrained intention dataset employed for training and evaluation. Furthermore, the method incorporates a composite architecture combining a convolutional neural network (CNN), long short-term memory (LSTM) unit, and self-attention (SA) mechanism to enhance recognition performance. The experimental results demonstrate that the integrated CNN-LSTM-SA model attains a recognition accuracy of 98.6%, significantly surpassing traditional methods and neural network models. Additionally, it demonstrates high efficiency, indicating significant promise for practical applications in avoiding spacecraft collisions and performing orbital maneuvers. Full article

Reinforcement learning has achieved significant success in sequential decision-making problems but exhibits poor adaptability in non-stationary environments with unknown dynamics, a challenge particularly pronounced in multi-agent scenarios. This study aims to enhance the adaptive capability of multi-agent systems in such volatile environments. We propose a novel cooperative Multi-Agent Reinforcement Learning (MARL) algorithm based on MADDPG, termed MACPH, which innovatively incorporates three mechanisms: a Composite Experience Replay Buffer (CERB) mechanism that balances recent and important historical experiences through a dual-buffer structure and mixed sampling; an Adaptive Parameter Space Noise (APSN) mechanism that perturbs actor network parameters and dynamically adjusts the perturbation intensity to achieve coherent and state-dependent exploration; and a Huber loss function mechanism to mitigate the impact of outliers in Temporal Difference errors and enhance training stability. The study was conducted in standard and non-stationary navigation and communication task scenarios. Ablation studies confirmed the positive contributions of each component and their synergistic effects. In non-stationary scenarios featuring abrupt environmental changes, experiments demonstrate that MACPH outperforms baseline algorithms such as DDPG, MADDPG, and MATD3 in terms of reward performance, adaptation speed, learning stability, and robustness. The proposed MACPH algorithm offers an effective solution for multi-agent reinforcement learning applications in complex non-stationary environments. Full article

With an increasing number of illegal radio stations, connected cars, and IoT devices, high-accuracy radio source localization techniques are in demand. Traditional methods such as GPS positioning and triangulation suffer from accuracy degradation in NLOS (non-line-of-sight) environments due to obstructions. In contrast, the fingerprinting method builds a database of pre-collected radio information and estimates the source location via pattern matching, maintaining relatively high accuracy in NLOS environments. This study aims to improve the accuracy of fingerprinting-based localization by optimizing UAV flight paths. Previous research mainly relied on RSSI-based localization, but we introduce an AOA model considering AOA (angle of arrival) and EOA (elevation of arrival), as well as a HYBRID model that integrates multiple radio features with weighting. Using Wireless Insite, we conducted ray-tracing simulations based on the Institute of Science Tokyo’s Ookayama campus and optimized UAV flight paths with PSO (Particle Swarm Optimization). Results show that the HYBRID model achieved the highest accuracy, limiting the maximum error to 20 m. Sequential estimation improved accuracy for high-error sources, particularly when RSSI was used first, followed by AOA or HYBRID. Future work includes estimating unknown frequency sources, refining sequential estimation, and implementing cooperative localization. Full article

Pose measurement of non-cooperative targets in space is one of the key technologies in space missions. However, most existing methods simulate well-lit environments and do not consider the degradation of algorithms in low-light conditions. Additionally, due to the limited computing capabilities of space platforms, there is a higher demand for real-time processing of algorithms. This paper proposes a real-time pose measurement method based on binocular vision that is suitable for low-light environments. Firstly, the traditional point feature extraction algorithm is adaptively improved based on lighting conditions, greatly reducing the impact of lighting on the effectiveness of feature point extraction. By combining point feature matching with epipolar constraints, the matching range of feature points is narrowed down to the epipolar line, significantly improving the matching speed and accuracy. Secondly, utilizing the structural information of the spacecraft, line features are introduced and processed in parallel with point features, greatly enhancing the accuracy of pose measurement results. Finally, an adaptive weighted multi-feature pose fusion method based on lighting conditions is introduced to obtain the optimal pose estimation results. Simulation and physical experiment results demonstrate that this method can obtain high-precision target pose information in a real-time and stable manner, both in well-lit and low-light environments. Full article

This study addresses the challenging issues in 3D point cloud feature matching within the field of computer vision, where high data quality requirements and vulnerability to disturbances significantly impact performance. Existing methods are prone to outliers when generating feature correspondences due to noise, sampling deviations, symmetric structure, and other factors. To improve the robustness of point cloud feature matching, this paper proposes a novel 3D spatial encoding (3DSE) method that incorporates compact geometric constraints. Our method encodes the spatial layout of matching feature points by quantifying the order of appearance of matching points, and combines rigidity constraints to iteratively eliminate the least consistent matching pairs with the remaining point pairs, thereby sorting the initial matching set. The 3DSE algorithm was evaluated on multiple datasets, including simulated data, self-collected data, and public datasets, which cover data from both LiDAR and Kinect sensors. The comparison results with existing techniques demonstrate that 3DSE exhibits superior performance and robustness in handling noise, sparse point clouds, and changes in data modalities. The application of the proposed method significantly enhances the point cloud registration process, showing promising potential for 3D reconstruction, model-driven 3D object recognition, and pose estimation of non-cooperative targets. Full article

This study systematically investigated the influence of aging temperature variations on the evolution of Cu-rich precipitates and dislocation distribution characteristics in 17-4PH stainless steel through comprehensive electrochemical testing and microstructural characterization. The mechanism by which microstructural features govern electrochemical corrosion behavior was elucidated. Experimental results demonstrated that within the aging temperature range of 480–620 °C, matrix dislocations consistently maintained non-uniform distribution characteristics, though their regional heterogeneity exhibited a decreasing trend with increasing temperature. The precipitation behavior of copper followed an evolutionary sequence: transitioning from dispersed copper precipitates to finely distributed Cu-rich precipitates with high numerical density, ultimately progressing to coarsening and agglomeration. The corrosion resistance of the material initially improved before subsequent degradation, accompanied by a morphological transition of surface corrosion features from characteristic elongated striations to elliptical patterns. Samples aged at 580 °C for 4 h exhibited optimal corrosion resistance. Mechanistic analysis revealed that reduced dislocation density heterogeneity effectively minimized electrochemical potential differences between micro-regions, while elemental segregation induced by Cu-rich precipitates coarsening intensified local electrochemical inhomogeneity. These two mechanisms cooperatively regulated the overall corrosion resistance evolution of the material. Full article

Gait recognition, a long-range biometric technique based on walking posture, the fact that they do not require the cooperation of the subject and are non-invasive has made them highly sought after in recent years.Although existing methods have achieved impressive results in laboratory environments, the recognition performance is still deficient in real-world applications, especially when confronted with complex and dynamic scenarios. The major challenges in gait recognition include changes in viewing angle, occlusion, clothing changes, and significant differences in gait characteristics under different walking conditions. To slove these issues, we propose a gait recognition method based on relational-aware global attention. Specifically, we introduce a Relational-aware Global Attention (RGA) module, which captures global structural information within gait sequences to enable more precise attention learning. Unlike traditional gait recognition methods that rely solely on local convolutions, we stack pairwise associations between each feature position in the gait silhouette and all other feature positions, along with the features themselves, using a shallow convolutional model to learn attention. This approach is particularly effective in gait recognition due to the physical constraints on human walking postures, allowing the structural information embedded in the global relationships to aid in inferring the semantics and focus areas of various body parts, thereby improving the differentiation of gait features across individuals. Our experimental results on multiple datasets (Grew, Gait3D, SUSTech1k) demonstrate that GaitRGA achieves significant performance improvements, especially in real-world scenarios. Full article

Pose estimation plays a crucial role in on-orbit servicing technologies. Currently, point cloud registration-based pose estimation methods for noncooperative spacecraft still face the issue of misalignment due to similar point cloud structural features. This paper proposes a pose estimation approach for noncooperative spacecraft based on the point cloud and optical image feature collaboration, inspired by methods such as Oriented FAST and Rotated BRIEF (ORB) and Robust Point Matching (RPM). The method integrates ORB feature descriptors with point cloud feature descriptors, aiming to reduce point cloud mismatches under the guidance of a transformer mechanism, thereby improving pose estimation accuracy. We conducted simulation experiments using the constructed dataset. Comparison with existing methods shows that the proposed approach improves pose estimation accuracy, achieving a rotation error of 0.84° and a translation error of 0.022 m on the validation set. Robustness analysis reveals the method’s stability boundaries within a 30-frame interval. Ablation studies validate the effectiveness of both ORB features and the transformer layer. Finally, we established a ground test platform, and the experimental data results validated the proposed method’s practical value. Full article

Future generations of wireless networks at high-frequency spectrum suffer from limited coverage and Non-Line- of-Sight signal blockage, challenging emerging applications, such as smart industries and intelligent automation systems. Collaborative and cooperative communications with smart relays via Non-Orthogonal Multiple Access (NOMA) could be a breakthrough solution to this challenge. This paper presents a blockchain-integrated framework for NOMA wireless communication systems that incentivizes cooperation among users serving as relays. By leveraging Ethereum-based smart contracts, we introduce a Service Verification Contract featuring a Proof of Quality of Experience (PQoE) mechanism. The contract uses trust scores, weighted verifications, and dynamic validation thresholds to ensure honest behavior and deter malicious activities. The simulation results show that honest participants gradually increase their trust scores and require fewer verifications, while malicious verifiers lose influence over repeated rounds. Our findings indicate that combining trust-based incentives with a decentralized ledger can effectively promote reliable data-relaying services and streamline payment processes in collaborative and smart wireless networking systems. Full article