Search Results (5,996)

Extremely low overlap leads to severely scarce local geometric correspondences across frame pairs. Pure geometric descriptors—encoding merely low-level shape signatures—inherently fail to impose sufficient constraints for reliable transformation estimation when matches become critically sparse, rendering registration fundamentally fragile. While recent red-green-blue-depth (RGB-D) attempts have explored visual augmentation, they predominantly rely on low-level chromatic statistics or shallow convolutional neural network (CNN) features, underutilizing the rich hierarchical semantics inherent in RGB imagery. We present V3Reg, a robust registration framework that pioneers the integration of large-scale vision foundation models (DINOv3) with adaptive cross-modal fusion. Specifically, we extract mid-to-deep semantic features (Layer 11) from DINOv3 to transcend low-level texture limitations, and propose a Task-Aware Channel-Wise Gated Adaptive Fusion (TACGAF) module that dynamically calibrates geometric-visual contributions via registration-error-guided channel-wise gating. To rigorously evaluate ultra-low-overlap robustness, we reconstruct RGBD-ZeroMatch, a benchmark with controllable overlap ratios ranging from 1% to 20%. Extensive experiments demonstrate that V3Reg achieves 99.6% Feature Matching Recall and 96.3% Registration Recall on standard benchmarks. Notably, it maintains 50.2% Registration Recall at merely 5% overlap, outperforming prior methods by over 18 percentage points. Full article

Fringe projection profilometry (FPP) is widely used for 3D reconstruction, but conventional single-view FPP systems suffer from inherent occlusions and shadow regions, leading to incomplete surface recovery. In this study, we propose CVA-Net, an end-to-end deep learning framework with cross-view attention (CVA) that directly reconstructs dense depth maps from multi-view fringe patterns. CVA-Net simultaneously processes four fringe images acquired from orthogonal projection directions and leverages a CVA module to explicitly model inter-view dependencies, enabling adaptive fusion of complementary information. A 3D U-Net backbone with attention gates, atrous spatial pyramid pooling (ASPP), and an auxiliary parameter estimation branch further enhances reconstruction accuracy and structural consistency via multitask learning. To support Sim2Real network training, we build a Blender-based digital twin of a multi-view FPP system and generate a large-scale synthetic dataset with perfect ground truth. Extensive experiments on both synthetic and real-world objects demonstrate that CVA-Net significantly outperforms state-of-the-art single-view methods. With a symmetric four-view configuration and fringe period of 8, CVA-Net achieves an MAE of 0.0359 mm, an MSE of 0.0379 mm² and an RMSE of 0.1947 mm, reducing the MAE, MSE, and RMSE by 32.8%, 54.1%, and 32.2%, respectively, compared to the best single-view competitor. Ablation studies validate the contribution of each architectural component, while real-system experiments demonstrate the feasibility of transferring a network trained purely on synthetic data to practical FPP measurements without domain adaptation. Although further improvements are required to enhance reconstruction accuracy under real imaging conditions, the proposed framework provides an effective initial step toward bridging the gap between digital-twin-based training and real-world multi-view FPP applications. CVA-Net provides a robust, occlusion-aware solution for multi-view FPP reconstruction. Full article

In the field of stereo depth sensing, modern research predominantly prioritizes accuracy, yet inference speed remains a critical bottleneck for practical, real-time applications on resource-constrained platforms. Existing acceleration approaches often rely on lighter network architectures or runtime-specific optimizations, which may require architectural redesign, platform-specific tuning, or accuracy trade-offs. However, a common inefficiency remains in many stereo pipelines: feature extraction is typically performed using two separate forward passes, one for the left image and one for the right, even though both passes use the same network weights. We address this redundancy by concatenating the left and right images into a single combined tensor, enabling feature extraction in one batched pass while preserving the original network architecture. By reducing feature extraction time by up to 48.4%, our results demonstrate that this method accelerates the overall inference rate by 10% to 39% on average on Nvidia V100 and up to 28.4% on edge device, depending on the model architecture. This speedup is achieved at the expense of only a moderate increase in runtime memory consumption, while retaining the original accuracy. Because the method does not alter the core stereo network, it can be applied as a plug-and-play enhancement to both existing and newly developed stereo matching models. Full article

An empirical model is developed to predict significant wave height and significant wave period using only wind speed at 10 m height, fetch, and water depth. The model distinguishes between fetch-limited and duration-limited sea states within a conditional empirical framework that incorporates modified empirical exponents and corrections into classical wave formulations. Validation was performed using wind and wave data from the Global Forecast System at 26 coastal and offshore stations distributed across eleven different pilot seas and oceans worldwide, encompassing a broad spectrum of marine environments and climatic conditions. The proposed model was benchmarked against established empirical approaches. Results indicate a mean prediction error of 6.6% for the significant wave height and 9.6% for the significant wave period, substantially outperforming conventional formulations whose errors exceed 50% under comparable conditions. Unlike existing empirical models that are restricted to specific regions or sea-state conditions, the proposed model demonstrated strong predictive performance across diverse seas, oceans, and climatic conditions, enabling more reliable wave predictions in data-scarce and dynamically complex marine environments. The developed model was further applied to the Red Sea, where it successfully reproduced the spatial variability of significant wave height and wave period. From the results, it has been found that the developed model provides a practical and transferable tool for wave forecasting, coastal engineering, and offshore renewable energy applications. Full article

Optical coherence tomography (OCT) is an optical imaging modality that provides high-resolution cross-sectional imaging of biological tissues noninvasively. In Fourier-domain OCT, axial resolution is governed by both the center wavelength and the spectral bandwidth of the light source; therefore, limited or discontinuous bandwidth degrades depth resolution and introduces sidelobes and artifacts in OCT images. To address these issues in OCT image reconstruction from gapped spectra, a sparse parameter estimation approach based on Sparse Iterative Covariance-based Estimation (SPICE) is proposed in this study. By utilizing a sparse parameter estimation framework to directly resolve depth-dependent components from discontinuous interferograms, SPICE enhances axial resolution while suppressing sidelobe artifacts inherent in standard interpolation. Experiments on multi-layered tape, oral epithelium, and finger skin show that SPICE visually suppresses gap-induced sidelobe artifacts and improves structural interpretability under representative gap conditions. Quantitative evaluations on multi-layer tape and biological tissues show that SPICE reduces axial FWHM by 30–45%, increases SSIM by 0.15–0.25, and achieves significantly lower computational cost than GAPES (p < 0.01). Full article

Background: Effective root canal disinfection is essential for successful nonsurgical root canal treatment (RCT). Although sodium hypochlorite (NaOCl) remains the standard irrigant, it carries a risk of chemical tissue injury if extruded beyond the root canal system and may have limited penetration into anatomically complex regions. Underwater discharge plasma (UDP) generates reactive oxygen and nitrogen species (RONS) through high-frequency, high-voltage electrical discharge in aqueous media, and preclinical and in vitro studies have reported broad-spectrum antimicrobial activity. This study evaluated the clinical and radiographic outcomes of nonsurgical RCT performed using physiological saline-based UDP irrigation without NaOCl in a heterogeneous real-world clinical cohort. Methods: This single-center retrospective cohort study included 186 teeth from 134 patients treated with the PLAZEN RCT^® UDP device and physiological saline irrigation, without NaOCl. The median follow-up period was 16 months. Radiographic outcomes were assessed using the Periapical Index (PAI) system, and treatment success was evaluated according to prespecified Strict and Loose criteria incorporating both radiographic and clinical findings. Stratified analysis was performed according to preoperative PAI score: Group A (PAI 1–2) and Group B (PAI 3–5). UDP-related adverse events, defined as thermal tissue injury caused by discharge heat, were ascertained through retrospective review of clinical records, operative notes, and serial periapical radiographs. Results: Among the 186 treated teeth, radiographic outcomes were classified as Healed (85.5%), Healing (3.8%), and Unhealed (10.8%). Overall Strict and Loose success rates were 79.6% and 82.3%, respectively. Initial treatment showed numerically higher success rates than retreatment. In the stratified analysis, Group A showed an 84.1% success rate with 100% tooth survival, whereas Group B demonstrated Strict and Loose success rates of 68.5% and 83.3%, respectively. Exploratory multivariable analysis showed that periodontal pocket depth > 3 mm was the most consistent factor associated with lower odds of treatment success, whereas associations involving canal obliteration and higher preoperative PAI score were less stable across sensitivity analyses and should be interpreted with caution. No UDP-related adverse events were recorded during follow-up. Attrition sensitivity analyses were performed, and the outcome estimates should be interpreted with caution, given the retrospective design and substantial loss to follow-up. Conclusions: In this preliminary observational cohort, physiological saline-based UDP irrigation without NaOCl was associated with favorable observed periapical healing outcomes and no recorded UDP-related adverse events over a median follow-up of 16 months. However, loss to follow-up was substantial; when all 116 teeth lost to follow-up were classified as treatment failures, the worst-case Strict success rate decreased to 49.0%. Therefore, these findings should be interpreted as preliminary descriptive evidence of clinical feasibility rather than as evidence of comparative efficacy or definitive clinical safety. Adequately powered randomized controlled trials with concurrent NaOCl control arms and long-term follow-up are warranted to evaluate the comparative effectiveness, safety, and reproducibility of physiological saline-based UDP irrigation protocols. Full article

The imminent threat of large-scale quantum computers motivates the deployment of post-quantum cryptography (PQC). CRYSTALS-Kyber, a leading lattice-based Key Encapsulation Mechanism, relies heavily on Number Theoretic Transform (NTT) operations, which remain a major performance and resource bottleneck. This paper presents a cross-platform NTT evaluation framework for CRYSTALS-Kyber, centered on an adaptive FPGA-based mixed-radix accelerator supporting radix-2, radix-4, and radix-8 configurations, together with comparative classical implementations and exploratory quantum-circuit prototypes. Classical evaluations show that an iterative Cooley–Tukey implementation outperforms a matrix-based baseline (≈3.6× faster for the forward NTT, ≈6.3× faster for the inverse NTT). Quantum prototypes implemented in Qiskit demonstrate proof-of-concept QFT-based NTT constructions under classical simulation environments, highlighting circuit-depth growth and noise sensitivity rather than practical hardware acceleration. The proposed FPGA design, based on a Xilinx Virtex UltraScale+ platform, employs an adaptive radix controller, LUT-based twiddle management, and Montgomery/Barrett modular arithmetic. Montgomery reduction provides superior timing and area trade-offs, with an estimated $F_{\max }$ of up to 231.48 MHz and only 5 DSPs for radix-2. At the same time, radix-2 offers the best resource/performance balance with a latency of approximately 32,804 cycles. The hybrid approach strikes a balance between near-term FPGA practicality and long-term quantum potential while preserving Kyber’s MLWE-based security. Experimental results and comparative analysis indicate that the adaptive design substantially reduces resource usage and timing overhead compared to recent HLS-based NTT accelerators. Full article

Buried steel pipelines are susceptible to external pitting corrosion whose spatially heterogeneous and time-dependent nature makes probabilistic remaining useful life (RUL) estimation both necessary and difficult. This study develops a three-stage framework comprising statistical pit depth characterization, a proxy corrosion-growth learner, and a mechanics-regularized limit-state RUL model anchored to the modified B31G burst criterion and an auxiliary conservative depth-screen rule, in which a corrosion-growth prior learned from sparse field measurements is embedded directly into the learning objective as a regularizer rather than merely used to construct training labels, and conformal calibration is applied at both the proxy and the limit-state stages to ensure honest empirical coverage, with the mechanics-regularized limit-state model consistently achieving coverage close to or above the nominal 90% target. Applied to the Velázquez excavation dataset and further examined on a 127-sample engineering database used as an auxiliary transferability check, the framework delivered stable point accuracy across all three wall thickness scenarios while maintaining empirical 90% coverage consistently above the nominal target. These results demonstrate that embedding mechanics-based growth constraints into the learning objective improves predictive consistency under sparse field data, while the resulting reliability outputs are best interpreted as scenario-based screening evidence for comparative integrity prioritization rather than as literal asset-life certification. Full article

A methodology for determining two parameters was proposed: the effective depth of heat penetration, and the thickness of the surface layer accumulating a given amount of heat. Explicit formulas allowing estimation of the values of these parameters for a semi-infinite body heated by heat flux with a variable time profile of intensity were obtained. Ten time profiles corresponding to different types of braking were analysed. The obtained results can be used at the design stage to determine the temperature mode and then to select materials for the friction elements of a disc braking system. Full article

Autonomous driving necessitates a robust 3D perception system that includes accurate object detection, tracking, and segmentation. While recent low-cost camera-based methods have demonstrated promising results, these systems are prone to performance degradation under poor lighting conditions or adverse weather, resulting in considerable localization errors. In this paper, we present a novel approach called Frequency-aware Depth Association Radar-Camera (FDARC) Fusion. This method aims to generate semantically rich and spatially accurate Bird’s-Eye-View (BEV) feature maps by integrating data from both camera and radar sensors. Initially, the image features are enhanced using frequency-aware techniques. Subsequently, these features are transformed into BEV representation with the assistance of depth information estimated from both sensor modalities and radar measurements. This process, known as Depth Association (DA), facilitates more precise BEV representations. Following this, a Temporal and Deformable Cross-Fusion (TDCF) layer is utilized to encode multi-modal feature maps into a unified space-time dimension representation. Extensive experiments conducted on the nuScenes dataset show that FDARC achieves state-of-the-art performance in 3D detection tasks, markedly outperforming baseline models on the nuScenes val set using a ResNet-50 backbone, which attains 53.5% nuScenes Detection Score (NDS) and 44.7% mean Average Precision (mAP). Full article

Computer vision (CV) has significantly advanced in object detection and multi-object tracking; however, its application to modeling safety-critical social behaviors for blind and low-vision (BLV) individuals remains limited. In particular, sustained behaviors such as stalking—characterized by persistent proximity and trajectory consistency—have not been systematically addressed within wearable assistive systems. To investigate this gap, we first conducted a formative user study combining semi-structured interviews and behavioral observations to identify safety concerns and wearable design requirements among BLV participants. The findings reveal recurring concerns regarding prolonged following behaviors and highlight the importance of privacy-preserving, socially unobtrusive device configurations. Guided by these insights, we develop a shoulder-slung wearable system integrating dual-camera sensing with an edge-based vision processing pipeline. We reformulate stalking detection as a temporal behavioral persistence problem built upon multi-object tracking (MOT). Leveraging FairMOT for identity-preserving tracking and monocular depth estimation for spatial modeling, we introduce an online temporal persistence-based risk scoring mechanism that accumulates proximity and directional consistency over time. The complete pipeline operates in real time on an embedded platform without cloud dependency. By bridging user-centered design and behavior-oriented visual inference, this work demonstrates how MOT outputs can be extended beyond identity preservation to support temporally coherent safety assessment in wearable assistive contexts. Full article

Against the backdrop of in-depth integration between the digital economy and green low-carbon development, exploring how digital technologies facilitate the systematic low-carbon transition of economy and society bears profound theoretical and practical implications for accomplishing China’s “Dual Carbon” goals. Based on provincial-level panel data covering 31 Chinese provinces over the period from 2015 to 2024, this paper adopts two-way fixed-effect specification, instrumental variable approach and Bootstrap-based mediation test to empirically identify the causal impact, underlying mechanisms and heterogeneous boundary conditions of digital technology adoption on low-carbon economic transition. The empirical results demonstrate three core findings. First, digital technology applications exert a statistically significant positive effect on low-carbon economic transition, and this benchmark result remains robust after a battery of robustness tests and endogenous bias corrections. Second, the mechanism estimation uncovers a sophisticated transmission pathway: digital technologies directly accelerate low-carbon transition, yet generate an adverse indirect impact via the green investment channel, which jointly forms a suppressing effect in the mediation framework. Third, the enabling effect of digital technologies on decarbonization presents striking regional imbalance, with significant promotional effects concentrated exclusively in eastern provinces and regions featuring well-developed marketization, which highlights the indispensable moderating role of regional endowments and institutional environments. This study contributes novel empirical evidence to unpack the intricate nexus between digital advancement and green transition, and delivers actionable policy references for designing differentiated and coordinated strategies to integrate digital upgrading with low-carbon development. Full article

Nanocolloid research has undergone a complete transformation, renouncing the empirical estimation of properties and relying on real case scenarios. The main objective of this paper is to compare a large number of samples that were experimentally studied in terms of thermophysical properties in order to be able to draw a conclusion in terms of the heat transfer efficiency of a certain surfactant addition to a 2 wt.% TiO₂ nanoparticle-enhanced fluid. The analysis discusses both the advantages and drawbacks in terms of surfactant type and concentration influence over the Prandtl number, thermal diffusivity, and Nusselt number, as well as the heat transfer coefficient for different Reynolds numbers in laminar flow. The investigation also includes a different figure of merits and performance evaluation criteria that are extensively employed in the literature in order to have a complete overview of the efficiency of surfactants in improving nanocolloids. In conclusion, even if surfactants are considered for improving nanocolloid stability, their drawbacks have not been debated in depth in the open literature. The main conclusion that arises from this study outlines that among all tested samples, F127 at a concentration of 0.25 wt.% consistently demonstrates the best overall performance, achieving an optimal balance between enhanced thermal properties and acceptable pumping requirements. Full article

Accurate delineation of buried slip surfaces remains a major uncertainty in landslide hazard assessment, especially where subsurface data are limited. This study evaluates a displacement-based approach to estimate quasi-three-dimensional (quasi-3D) slip surfaces using ground-surface displacement vector gradients derived from multi-temporal UAV-based LiDAR data. Two landslides in Japan (Jimba and Kamitokitozawa), representing contrasting scales, were analyzed to assess the method’s applicability and limitations. Two-dimensional (2D) slip-surface profiles were derived through group-wise median grouping of displacement gradients and weighted non-uniform rational B-spline fitting along longitudinal sections. Transverse profiles were constrained using side-scarp gradients and depths estimated from longitudinal profiles. These profiles were integrated into quasi-3D surfaces and validated against borehole-derived slip surfaces. At the Jimba landslide, characterized by relatively coherent movement, the estimated surfaces closely match borehole data in both depth and geometry. At the larger Kamitokitozawa landslide, the method reproduces first-order geometry and extent but shows larger local deviations, particularly in a graben-like subsidence zone. Nevertheless, the estimated displaced volume reaches 96% of that derived from borehole data. These results demonstrate that the method provides useful first-order constraints on slip-surface geometry for preliminary hazard assessment, borehole planning, and 3D stability analysis. Full article

This study presents the development and field testing of a Pressurized Driving-Down Air Multilevel Sampler (PDA-MLS), an integrated groundwater sampling device designed for depth-discrete sampling in boreholes affected by floating non-aqueous phase liquids (NAPLs). Conventional sampling methods—such as low-flow pumps, bailers, and packer-isolated systems—often fail under these conditions due to limited accessibility, cross-contamination, or disturbance of the water column. The proposed system addresses these limitations through a controlled pressurized-gas actuation mechanism that transfers groundwater from multiple PTFE-membrane chambers installed at discrete depths. This configuration enables low-disturbance sampling below floating contaminant layers. The use of chemically inert materials (stainless steel and PTFE) minimizes sampling artifacts and ensures compatibility with volatile organic compound (VOC) analyses. A simplified hydraulic conceptual framework describing inflow, outflow, and pressure-driven displacement was developed to support purge-duration estimation and operational parameter definition. The device was tested in a 90 m deep fractured limestone aquifer contaminated by tetrachloroethylene (PCE), where floating hydrocarbons limited the applicability of conventional sampling techniques. Field testing showed stable discharge conditions (~145–160 mL/min), repeatable sampling cycles, and successful collection of depth-discrete groundwater samples under the investigated site conditions. No evidence of sampler-related hydrocarbon entrainment was observed in the collected samples within the analytical detection limits of the adopted laboratory methods. To the authors’ knowledge, the PDA-MLS represents one of the few groundwater sampling systems specifically designed to combine low-disturbance multilevel sampling with operation in wells affected by floating NAPL. These features make it a promising tool for environmental monitoring, high-resolution characterization of fractured aquifers, and long-term assessment of contaminated sites. Full article