Search Results (1,095)

Sweeping jet (SWJ) actuators are widely used in active flow control, but explicitly resolving actuator-scale unsteadiness in full-configuration computational fluid dynamics (CFD) remains prohibitively expensive because of the small geometric scales and high-frequency oscillations involved. Existing reduced-order boundary-condition models constructed from exit-plane data alone can reproduce the observed switching waveform, but they treat the actuator as an input–output black box and provide limited insight into the internal dynamics that generate the response. This work develops a causal state-space reduced-order modeling framework that links internal mixing-chamber dynamics to time-resolved exit-plane boundary conditions. Proper orthogonal decomposition (POD) is used to obtain a low-dimensional representation of the internal flow, and a data-driven linear evolution operator is identified in the reduced space by least-squares regression of successive snapshot pairs. A POD truncation rank of $r=60$ is selected from cumulative-energy and validation-error sensitivity analyses, capturing well above 99% of the fluctuation energy while lying within the converged performance regime. A corresponding reduced operator is identified for the exit plane, and spectral comparison reveals near-neutrally stable oscillatory modes in both regions. Using a $\pm 1\%$ relative frequency-matching tolerance, the dominant reduced-operator modes exhibit a 28.3% frequency overlap, providing operator-level evidence that exit-plane oscillations are dynamically linked to internal coherent structures. This correspondence is further supported by cross-spectral coherence analysis between representative internal and exit-plane probe signals, which shows strong coherence at dynamically relevant frequencies. A delayed causal output mapping is then formulated in which the internal reduced state drives the exit-plane response after an identified lag of 149 time steps, corresponding to $2.98\times {10}^{-3}$ s. This delay provides a physically interpretable convective transport timescale from the mixing chamber to the actuator exit. Over the validation interval, the model maintains a mean relative $L_2$ error below 0.02, with maximum normalized errors below 0.04 for most of the prediction horizon, and localized increases are confined to rapid jet-switching events. Field-level reconstructions of streamwise velocity and total pressure show that the model captures both phases of the jet-switching cycle, with errors concentrated primarily in high-gradient shear-layer regions. Compared with exit-only reduced-order models, the proposed internal-driven formulation improves amplitude and phase fidelity over extended prediction horizons. The resulting framework provides a compact, interpretable, operator-based representation of SWJ actuator dynamics suitable for use as a CFD-embeddable dynamic boundary condition. Full article

Terahertz (THz) fingerprint detection is central to identifying characteristic absorption fingerprints of biomolecules derived from their intrinsic rotational and vibrational modes. The development of guided-mode resonance (GMR) technology together with pixelated design offers a new approach to enhance the recognition capability of such fingerprint spectra. Here, a novel secondary grating metasurface based on cycloolefin polymer (COP) is proposed, which adopts an ultra-minimalist dual-pixel complementary architecture to excite high-quality (Q)-factor GMR. Its spectral resolution does not exceed 50 GHz, enabling precise capture of target molecular characteristic information and meeting the requirements of broadband fingerprint sensing. More importantly, the design regulates the dual-pixel grating units through parameter gradient optimization and incorporates a dual regulation mode of static pixel-targeted coverage and dynamic angle fine tuning. By adjusting geometric parameters and incident angles, broadband coverage from 1.15 THz to 2.20 THz is achieved, which can accurately match the multi-fingerprint detection requirements of glutamic acid (Glu) and glutamine (Gln). This metasurface sensor, integrating the advantages of pixelation and high-Q-factor GMR characteristics, provides an effective strategy for enhanced broadband THz fingerprint sensing and shows broad application potential in the field of biochemical trace detection. Full article

Medium-resolution cryo-electron microscopy (cryo-EM) density maps preserve substantial information about protein secondary-structure organization; however, accurately recovering the topology and connectivity of α-helices and β-strands remains challenging due to noise, structural heterogeneity, and the intrinsic resolution limitations that obscure residue-level detail. Topology determination is a key intermediate step toward building atomic protein models from medium-resolution cryo-EM density maps. It requires identifying the correct correspondence and orientation between secondary-structure elements (SSEs), i.e., α-helices and β-strands, predicted from the amino-acid sequence and those detected in the three dimensional (3D) density map. Despite significant advances in cryo-EM reconstruction and molecular modelling, this correspondence problem remains a challenging task, particularly in the presence of noisy density maps and in large, topologically complex α/β proteins. To address this issue, we propose a fully automated, classification-based framework that infers protein secondary-structure topology directly from medium-resolution cryo-EM density maps. Specifically, we cast topology determination as a supervised classification problem in three-dimensional space, leveraging geometric learning on model-derived Cα coordinate representations to establish SSE correspondences, and a Dynamic Time Warping (DTW)-based procedure to resolve density-stick directionality. Validation on a benchmark of 38 proteins spanning both simulated and experimental cryo-EM maps and covering diverse fold classes (α, β, and α/β) demonstrates strong and consistent performance. Among the evaluated predictors, the Voronoi (1-NN) classifier achieves the highest average correspondence quality, with a mean F1-score of 96.82% across the full benchmark. The framework also scales to large, topologically dense targets containing up to 65 secondary-structure elements while preserving very fast correspondence inference (<3 ms), offering a substantial improvement over prior baselines in both accuracy and computational cost. Overall, the classification-driven strategy provides reliable SSE-to-density matching and, when coupled with DTW-based direction selection, yields stronger topology constraints that directly support model building and refinement from medium-resolution cryo-EM reconstructions, while remaining easy to integrate into existing structural interpretation pipelines. Full article

Astronomical positioning of space targets is an important task in space situational awareness. In both ground-based and space-based optical observation scenarios, accurate positioning relies on the reliable matching of numerous stars in observational images. However, dense star scenes increase the ambiguity of local patterns and the computational burden of candidate retrieval. Building on established geometric voting and catalog-indexing strategies, this paper develops a two-stage star map matching method that specifically combines adaptive magnitude stratification with seed-guided residual-star diffusion for large-field dense star scenes. In the first stage, an adaptive magnitude-stratified bright-star subset is selected according to field density, and angular-distance voting is used to obtain reliable seed correspondences. In the second stage, residual-star candidates are retrieved from seed-centered dual-feature sub-libraries indexed by angular distance and magnitude difference, and are then refined through single-seed local diffusion and multi-seed global fusion. Experimental results from both simulated and real observational data demonstrate that the proposed method achieves a high matching success rate with low computational cost and performs effectively in large-field, dense star scenes. The proposed method provides a practical matching solution for astronomical positioning in dense star scenes. Full article

The localization performance of visual–inertial simultaneous localization and mapping (VI-SLAM) strongly depends on front-end feature matching. In degraded scenes with low illumination, repetitive textures, and weak textures, traditional geometric front ends often suffer from sparse features and mismatches, resulting in unstable state estimation. To address this issue, this paper proposes Area-to-Point Matching Visual–Inertial SLAM (A2PM-VINS), a visual–inertial SLAM method based on Area-to-Point matching. The method introduces Area-to-Point hierarchical matching and a kinematic temporal inheritance mechanism to improve matching reliability and track continuity, and further designs an Anchor–Explorer feature selection strategy to retain features with higher geometric value for back-end optimization. In addition, a Sub-Window Consistency (SWC) weighting strategy is incorporated into the back end to suppress geometrically deceptive observations with poor temporal continuity and geometric consistency. Experiments on the European Robotics Challenge Micro Aerial Vehicle (EuRoC MAV) dataset show that A2PM-VINS achieves superior or competitive localization accuracy on multiple challenging sequences. The absolute trajectory errors on MH_04 and MH_05 are 0.0983 m and 0.1191 m, respectively, and stable tracking is maintained on V2_02, where VINS-Fusion fails. These results show that the proposed method effectively improves the robustness of visual–inertial state estimation in complex degraded environments. Full article

In maritime search and rescue and underwater surveillance missions employing forward-looking sonar, strong reverberation and complex underwater environments often substantially degrade the target signal-to-clutter ratio (SCR), presenting significant challenges for target detection. Existing algorithms typically simplify the point spread function (PSF) into an ideal isotropic model, thereby overlooking the inherent anisotropy induced by its sidelobe structures. This physical model mismatch leads to target energy leakage and severely limits detection performance in complex backgrounds. To overcome the limitations of current target models and detection algorithms, this paper introduces a Gaussian 5 Superposition (G5S) model to accurately characterize the physical features of the PSF and proposes a Laplacian-of-G5S-based Local Adaptive Detection (LoG5S-LAD) method through the construction of a LoG5S filtering operator. Initially, a high-SCR target likelihood map is generated using Hessian-matrix-based geometric gating and LoG5S matched filtering techniques. Subsequently, robust background suppression and the effective preservation of faint targets are achieved through morphological artifact suppression, connected component screening, and a high-energy exemption mechanism. The effectiveness of the proposed framework is validated through model fitting experiments, as well as comprehensive simulations and detection tests across various sonar configurations. Experimental results indicate that the G5S model demonstrates precise fitting capabilities and strong physical adaptability. Furthermore, the proposed LoG5S-LAD algorithm significantly enhances the SCR while maintaining robust detection performance for faint and small-scale targets. Full article

High-quality correspondences are critical to the accuracy and robustness of point cloud registration. Existing Transformer-based methods are fundamentally constrained by the quadratic computational complexity of self-attention, resulting in limited scalability. Moreover, conventional outlier removal paradigms operate by pruning initial correspondences, and thus fail catastrophically in low-overlap scenarios where initial inliers are inherently scarce. To address these challenges, we propose MaLCA, a point cloud registration method based on Mamba-enhanced features and local correspondence augmentation. We first adopt KPFCN as the backbone to extract multi-scale geometric features from raw point clouds. A Mamba selective state space model then replaces self-attention for global context modeling with linear complexity, while cross-attention is retained to facilitate inter-point-cloud feature interaction. Rather than following the conventional subtraction-based outlier removal paradigm, we introduce a prior-guided local rematching strategy combined with a fused neighbor matching mechanism that iteratively constructs dense, high-quality correspondences from sparse initial inliers, fundamentally overcoming the bottleneck of inlier scarcity in challenging scenes. Extensive experiments on the 3DMatch/3DLoMatch and 4DMatch/4DLoMatch benchmarks demonstrate that MaLCA achieves competitive registration performance across both rigid and deformable scenarios, with particular advantages in low-overlap cases. Full article

HyperCoreg is an automated, end-to-end pipeline for geometric co-registration of spaceborne hyperspectral imagery (PRISMA L2D and EnMAP L2A) to Sentinel-2 Level-2A reference data. The workflow addresses scene-dependent geolocation errors that hinder reliable data fusion and multi-temporal analyses, particularly in cloud-affected acquisitions. HyperCoreg builds on the AROSICS framework without replacing its image-matching engine and extends it at the workflow level through four operational functions: automated Sentinel-2 candidate selection, hyperspectral-to-multispectral band pairing, sequential alignment logic, and quality-controlled acceptance. The main output is a co-registered hyperspectral cube along with comprehensive metrics, per-scene reports, and optional diagnostic products that support accessible quality control. Performance is evaluated on a long time series of PRISMA images collected from 2019 to 2025 and an EnMAP test set acquired in 2025, over the Metropolitan City of Rome (Italy). The multi-sensor dataset encompasses heterogeneous acquisition conditions, including variable cloud cover, illumination, and seasonal variability. The results show systematic reductions in mean residual error compared with a controlled basic AROSICS-based pipeline configuration. The largest gains are achieved in challenging conditions where tie points are sparse or unevenly distributed. By improving geometric consistency, this pipeline facilitates spatial layering and integration of hyperspectral data with higher-resolution urban layers and supports a range of downstream applications where data integration and spatiotemporal consistency are cornerstones of further analysis. Full article

Accurate segmentation of shoe upper processing boundaries is crucial for automated trajectory generation and high-precision robotic control. However, developing a robust method is challenging due to the frequent style changes in High-Mix Low-Volume production. The reliance on large-scale annotated datasets renders traditional supervised methods impractical due to the prohibitive cost of annotation and retraining. To address these issues, a multimodal-based point cloud segmentation strategy is proposed for shoe upper processing boundaries. First, an unsupervised adaptive local spectral contrast filtering algorithm is designed to remove large amounts of background noise and isolate potential target regions by exploiting boundary color characteristics. Then, an unsupervised dynamic ellipsoidal neighborhood color-spatial region growing algorithm is developed based on geometric features of slender and closed boundary shapes to suppress interferences flanking the boundaries. Finally, a Siamese network is designed to perform few-shot matching against boundary templates exported from Shoemaster, effectively decoupling intrinsic boundary signals from complex extrinsic interferences to achieve precise segmentation. Experimental results demonstrate that the proposed method achieves a stable mean Intersection over Union (mIoU) of approximately 0.80. Compared to existing supervised and unsupervised baselines, this strategy exhibits superior generalization across diverse styles and effectively resolves the data dependency bottleneck. Full article

Stereo vision leverages binocular imagery to emulate the human visual system in perceiving three-dimensional (3D) structures by estimating disparity from rectified image pairs and converting it to depth via geometric triangulation. In recent years, deep learning-based stereo matching has significantly advanced in accuracy, efficiency, and generalization, surpassing traditional methods and demonstrating great potential in remote sensing applications. However, stereo matching in remote sensing faces unique challenges not commonly seen in terrestrial datasets. These include limited access to satellite imagery, seasonal differences between image pairs, difficulty in identifying small objects, and widespread regions with repetitive textures, such as lakes and forests. Unlike prior surveys that primarily address ground-level scenes, this paper presents a comprehensive review of stereo matching techniques tailored for remote sensing. It synthesizes the progress and limitations of representative models, analyzes the characteristics and domain-specific constraints of remote sensing stereo datasets, and outlines future research directions and application prospects in this field. Full article

This paper proposes the Branch-Priority Exploration (BPE) framework for autonomous coverage in confined industrial corridor environments. BPE integrates three components: (1) a symmetry-aware LiDAR branch detector; (2) a hierarchical BFS/DFS mode-switching policy; and (3) a barrier-based branch memory. Frontier-based methods often struggle in industrial corridors where branches split off from the main corridor. The symmetric layout of such environments, featuring T-shaped junctions and L-shaped turns, creates recurring geometric patterns that conventional frontier scoring fails to exploit. When the robot reaches a junction, nearby frontier candidates often receive similar scores, causing repeated target switching as the local map changes. Meanwhile, frontier cells inside a branch tend to score lower than those along the main corridor; so, the robot often bypasses the branch and continues forward, which leads to additional backtracking later. Even when the robot eventually returns, residual frontier cells near the entrance may attract the planner repeatedly, causing redundant re-entry into already-covered branches. To address these issues, a branch-priority exploration framework is developed. A symmetry-aware branch detection module uses LiDAR range measurements from multiple directions to identify T-shaped junctions and lateral openings, applying identical geometric criteria to lateral openings on either side of the robot. This allows branch entry to be triggered by explicit geometric evidence, rather than frontier score comparisons that tend to be unreliable near intersections. When a branch is detected, the robot transitions from BFS mode to DFS mode for systematic branch coverage. Entry and post-return locks prevent mode reversal before the robot commits to the new heading. Once a branch is completed, a permanent virtual barrier is placed at its entrance; so, the planner no longer routes the robot back into that branch. The framework is formalized as a constrained coverage problem on occupancy grids, and monotonic coverage progress and finite branch completion under barrier memory are established theoretically. A fully reproducible ROS implementation on a wheeled platform with differential drive is validated. Experiments span several corridor environments of increasing topological complexity. Compared to a nearest-frontier baseline, the proposed method substantially reduces both exploration time and goal cancellations while achieving complete coverage across all trials. The cancellation count matches the number of T-branches per environment, with near-zero variance across repeated runs. Full article

An ill-posed permittivity inversion problem is encountered in borehole radar (BHR) applications within stratified media due to a highly nonlinear forward relation, insufficient statistical coverage under data-limited conditions, strong noise contamination, and limited borehole observation geometry, which together cause instability and blurred boundaries. To address these challenges, a stratified media oriented conditional generative adversarial network for permittivity inversion, termed StratGAN, is proposed. BHR waveform data are used as the conditional input, and the complex mapping from time domain waveforms to depth domain permittivity distributions is learned end to end through conditional adversarial training between a generator and a discriminator, jointly constrained by a composite loss. During training, statistical characteristics of layered structures are learned from real samples by the discriminator, and adaptive feedback is provided as a data-driven loss to suppress spurious structures and boundary ambiguity. WGAN-GP is adopted and combined with a patch-based local discrimination mechanism to reinforce high-frequency details and geometric boundary consistency, thereby reducing the over-smoothing tendency of conventional CNNs. In addition, geometric consistency of inversion results is improved in an end-to-end manner without relying on complicated velocity analysis. Quantitative evaluations on simulated and measured datasets indicate that, compared with an architecture-matched convolutional neural network (CNN) and the baseline model GPRNet, StratGAN achieves overall better performance in terms of mean absolute error, coefficient of determination, and structural similarity metrics, and layered interfaces and anomaly boundaries are more effectively recovered. For the controlled measured data, the coefficient of determination ($R^2$) is improved to 0.9533 by StratGAN, whereas a value of 0.5598 is obtained by GPRNet. These results indicate the potential of StratGAN to enhance the reliability and structural fidelity of BHR permittivity inversion under limited-sample conditions, and preliminary evidence is provided for its practical applicability under controlled measured conditions. Full article

The orbital drift of the Fengyun-4B (FY-4B) satellite from 133°E to 105°E in early 2024 significantly altered its viewing geometry over China, providing a unique opportunity to evaluate the impact of satellite positioning on the accuracy of downward surface shortwave radiation (DSSR) retrievals. In this study, FY-4B DSSR products before and after the drift were systematically evaluated using a strictly matched common set of 141 first-order radiation stations from the China Meteorological Administration during the summer seasons of 2023 and 2024. The results show that the post-drift product achieved markedly improved satellite–ground consistency, with the correlation coefficient increasing from 0.93 to 0.95 and the RMSE decreasing by 11.8% from 111.5 to 99.58 W/m², while the mean bias remained close to zero. Spatially, the historical east–west disparity in retrieval accuracy was substantially mitigated, mainly because the westward orbital shift reduced the viewing zenith angle over western China and thereby weakened geometric distortions and atmospheric path-length errors. Further analyses across longitude, latitude, land cover, elevation, cloud regime, and diurnal cycle consistently indicate that the optimized viewing geometry was the dominant driver of the post-drift improvement, although residual errors remain in complex terrain and heterogeneous cloud conditions. These results demonstrate that the orbital shift to 105°E fundamentally enhanced the reliability of FY-4B DSSR products over China and provide useful guidance for future geostationary satellite deployment and radiation product application in solar energy assessment and numerical weather prediction. Full article

While the integration of large language models (LLMs) drives intelligent automation, their endogenous vulnerability to complex adversarial prompts engenders critical security threats. To address the limitations of existing defenses relying on prohibitive fine-tuning or lagging text matching, this paper proposes Contrastive Distribution Discriminator Guard (CDD-Guard), a training-free endogenous monitoring framework. CDD-Guard pioneers mapping a latent bipolar semantic axis to geometrically separate benign and malicious representations. To mitigate structural noise, it introduces an adaptive layer filtering mechanism driven by statistical effect size, while a cross-layer projection normalization mechanism utilizes Z-Scores to isolate statistical anomalies from thematic variances. Evaluations across heterogeneous LLMs demonstrate superior detection efficacy. Crucially, by directly reusing hidden states generated during a single forward pass, CDD-Guard circumvents the autoregressive generation overhead of external monitors, reducing computational monitoring latency by over 99%. Operating strictly under the constraint of preserving original weights, this framework achieves substantial improvements in attack interception rates and cross-model generalization, providing a lightweight yet robust contribution to LLM security. Full article

This study presents the design and evaluation of a markerless 3D scanning system based on the Helios2 time-of-flight 3D camera, with automatic alignment of multiple scans acquired from different viewpoints. The proposed system integrates data acquisition, raw coordinate conversion, point cloud preprocessing, including voxel downsampling, statistical outlier removal, and surface normal estimation using Open3D and a coarse-to-fine automatic registration strategy combining FPFH descriptor matching with RANSAC initialization and ICP refinement, followed by voxel-based fusion of aligned views. The system was experimentally evaluated on three cardboard filament-box objects representing a realistic robotic handling scenario. The reconstructed models were compared against nominal reference data obtained from a commercial Revopoint Miraco markerless scanner using surface deviation analysis. The evaluation yielded an overall mean surface deviation of 0.04 mm and a standard deviation of 1.34 mm, demonstrating that the proposed innovative Helios2 workflow can produce markerless multi-view reconstructions quantitatively comparable to those of an existing commercial scanning device. The results confirm the practical feasibility of automatic markerless alignment for ToF 3D scanning and indicate the suitability of the developed system for robotic perception applications where moderate geometric accuracy and reduced scene preparation are prioritized over ultra-high metrological precision. Full article