Search Results (1,833)

Accurate feature matching between optical and synthetic aperture radar (SAR) images remains a significant challenge in remote sensing due to substantial modality discrepancies in texture, intensity, and geometric structure. In this study, we proposed an attention-context-aware deep learning framework (ACAMatch) for robust and efficient optical–SAR image registration. The proposed method integrates a structure-enhanced feature extractor, RS2FNet, which combines dual-stage Res2Net modules with a bi-level routing attention mechanism to capture multi-scale local textures and global structural semantics. A context-aware matching module refines correspondences through self- and cross-attention, coupled with a confidence-driven early-exit pruning strategy to reduce computational cost while maintaining accuracy. Additionally, a match-aware multi-task loss function jointly enforces spatial consistency, affine invariance, and structural coherence for end-to-end optimization. Experiments on public datasets (SEN1-2 and WHU-OPT-SAR) and a self-collected Gaofen (GF) dataset demonstrated that ACAMatch significantly outperformed existing state-of-the-art methods in terms of the number of correct matches, matching accuracy, and inference speed, especially under challenging conditions such as resolution differences and severe structural distortions. These results indicate the effectiveness and generalizability of the proposed approach for multimodal image registration, making ACAMatch a promising solution for remote sensing applications such as change detection and multi-sensor data fusion. Full article

Multi-modal remote sensing image (MRSI) matching suffers from severe nonlinear radiometric distortions and geometric deformations, and conventional feature-based techniques are generally ineffective. This study proposes a novel and robust MRSI matching method using the side window filter (MSWF). First, a novel side window scale space is constructed based on the side window filter (SWF), which can preserve shared image contours and facilitate the extraction of feature points within this newly defined scale space. Second, noise thresholds in phase congruency (PC) computation are adaptively refined with the Weibull distribution; weighted phase features are then exploited to determine the principal orientation of each point, from which a maximum index map (MIM) descriptor is constructed. Third, coarse position, orientation, and scale information obtained through global matching are employed to estimate image-pair geometry, after which descriptors are recalculated for precise correspondence search. MSWF is benchmarked against eight state-of-the-art multi-modal methods—six hand-crafted (PSO-SIFT, LGHD, RIFT, RIFT2, HAPCG, COFSM) and two learning-based (CMM-Net, RedFeat) methods—on three public datasets. Experiments demonstrate that MSWF consistently achieves the highest number of correct matches (NCM) and the highest rate of correct matches (RCM) while delivering the lowest root mean square error (RMSE), confirming its superiority for challenging MRSI registration tasks. Full article

Whether pattern or amount of daily activity determines neuromuscular properties is the focus of this review. The fast-to-slow conversion of many properties of fast-twitch muscles, by stimulating their nerves electrically with the continuous low-frequency pattern typical of slow motoneurons, argued that muscle properties are determined by their pattern of activity. However, the composition of the motor units (MUs) in almost all muscles is heterogeneous, with the MUs grouped into slow, fast-fatigue-resistant and fast-fatigable types that match corresponding histochemical fiber types. Nonetheless, their contractile forces lie on a continuum, with MUs recruited into activity in order of their size. This ‘size principle’ of MU organization and function applies in normally innervated and reinnervated muscles and, importantly, begs the question of whether it is the amount rather than the pattern of the MU activation that determines their properties. Experimental evidence that uniform daily amounts of ~<0.5, 5%, and 50% ES, converted motoneuron, nerve, and muscle properties to one physiological and histochemical type, argued in favor of the amount of activity determining MU properties. Yet, that the properties were not confined to the expected narrow range argued that factors other than the pattern and/or amount of neuromuscular activity must be considered. These include the progressive increase in the synaptic inputs onto motoneurons. The range of the effects of endurance and intermittent exercise programs on healthy subjects and those suffering nerve injuries and disease is also consistent with the argument that factors other than pattern or amount of neuromuscular activity should be investigated. Full article

Identifying correspondences of road segments in different road networks, namely road-network matching, is an essential task for road network-centric data processing such as data integration of road networks and data quality assessment of crowd-sourced road networks. Traditional road-network matching usually relies on feature engineering and parameter selection of the geometry and topology of road networks for similarity measurement, resulting in poor performance when dealing with dense and irregular road network structures. Recent development of graph neural networks (GNNs) has demonstrated unsupervised modeling power on road network data, which learn the embedded vector representation of road networks through spatial feature induction and topology-based neighbor aggregation. However, weighting spatial information on the node feature alone fails to give full play to the expressive power of GNNs. To this end, this paper proposes a Spatial Pattern-aware Graph EMbedding learning method for road-network matching, named SP-GEM, which explores the idea of spatially-explicit modeling by identifying spatial patterns in neighbor aggregation. Firstly, a road graph is constructed from the road network data, and geometric, topological features are extracted as node features of the road graph. Then, four spatial patterns, including grid, high branching degree, irregular grid, and circuitous, are modelled in a sector-based road neighborhood for road embedding. Finally, the similarity of road embedding is used to find data correspondences between road networks. We conduct an algorithmic accuracy test to verify the effectiveness of SP-GEM on OSM and Tele Atlas data. The algorithmic accuracy experiments show that SP-GEM improves the matching accuracy and recall by at least 6.7% and 10.2% among the baselines, with high matching success rate (>70%), and improves the matching accuracy and recall by at least 17.7% and 17.0%, compared to the baseline GNNs, without spatially-explicit modeling. Further embedding analysis also verifies the effectiveness of the induction of spatial patterns. This study not only provides an effective and practical algorithm for road-network matching, but also serves as a test bed in exploring the role of spatially-explicit modeling in GNN-based road network modeling. The experimental performances of SP-GEM illuminate the path to develop GeoEmbedding services for geospatial applications. Full article

The innovation demand and supply matching requires an accurate and time-consuming analysis of patents and the identification of their technological domains; since these tasks can be particularly challenging, this is why recent studies have evaluated the possibility of adopting Artificial Intelligence based on NLP techniques. Here, we present an automated workflow for patent analysis and classification devoted to the Italian patent scenario. High-quality data from the online platform KnowledgeShare (KS) were investigated: KS is the first patent management platform on the Italian innovation scene. A not secondary aspect consisted in determining which words mostly influenced patent classification, thus characterizing the corresponding research areas. Several models were compared to ensure the workflow’s robustness; Logistic Regression (LR) resulted in the best-performing model, and its performance compared well with the State of the Art. For each technological domain in the KS database, we evaluated and discussed its characteristic words; furthermore, a further analysis was focused on explaining why some domains, such as “Packaging” and “Environment,” were particularly confounding. This last aspect is of paramount importance to identify cross-contamination effects among research areas. Full article

Light Detection and Ranging(LiDAR)-inertial simultaneous localization and mapping (SLAM) is a critical component in multi-sensor autonomous navigation systems, providing both accurate pose estimation and detailed environmental understanding. Despite its importance, existing optimization-based LiDAR-inertial SLAM methods often face key limitations: unreliable feature extraction, sensitivity to noise and sparsity, and the inclusion of redundant or low-quality feature correspondences. These weaknesses hinder their performance in complex or dynamic environments and fail to meet the reliability requirements of autonomous systems. To overcome these challenges, we propose a novel and accurate LiDAR-inertial SLAM framework with three major contributions. First, we employ a robust multi-category feature extraction method based on principal component analysis (PCA), which effectively filters out noisy and weakly structured points, ensuring stable feature representation. Second, to suppress outlier correspondences and enhance pose estimation reliability, we introduce a coarse-to-fine two-stage feature correspondence selection strategy that evaluates geometric consistency and structural contribution. Third, we develop an adaptive weighted pose estimation scheme that considers both distance and directional consistency, improving the robustness of feature matching under varying scene conditions. These components are jointly optimized within a sliding-window-based factor graph, integrating LiDAR feature factors, IMU pre-integration, and loop closure constraints. Extensive experiments on public datasets (KITTI, M2DGR) and a custom-collected dataset validate the proposed method’s effectiveness. Results show that our system consistently outperforms state-of-the-art approaches in accuracy and robustness, particularly in scenes with sparse structure, motion distortion, and dynamic interference, demonstrating its suitability for reliable real-world deployment. Full article

Remote sensing object detection (RSOD) models are widely deployed on edge devices for critical applications. Their security and integrity have become urgent concerns. This work proposes a fragile model watermarking method that enables black-box integrity verification for RSOD models. Specifically, for a given RSOD model, we construct class-specific sensitive object triggers and corresponding fragile watermark samples for each target category. During the trigger generation process, a trained surrogate model is first employed to construct the initial sensitive object trigger, where real objects are utilized to guide the trigger to acquire weak semantic features of the target class. This trigger is then jointly optimized using both the original model and a tampered version. The original model ensures that the trigger remains recognizable, while the tampered model encourages sensitivity to parameter changes. During integrity verification, the model is queried with all the fragile watermark samples. The model is considered intact only if all predictions match the expected results. Extensive experiments demonstrate that the proposed method is effective across multiple RSOD models. It exhibits high sensitivity to various model modifications, including backdoor injection, fine-tuning, pruning, random parameter perturbation, and model compression. Full article

In-band full-duplex (IBFD) technology has attracted significant attention for its potential to double the spectral efficiency by enabling a simultaneous transmission and reception over the same frequency channel. However, achieving high isolation between closely spaced transmit and receive paths remains a critical challenge. In this paper, a novel compact co-polarized monopole antenna with self-decoupling capability is proposed based on common-mode/differential-mode (CM/DM) theory. By innovatively folding the ends of the monopole elements, the antenna exploits the distinct behaviors under CM and DM excitations at a close spacing to achieve simultaneous impedance matching in both modes. This effectively enhances the isolation between antenna elements. The design enables self-interference suppression without requiring any additional decoupling structures, even under compact antenna and port spacing. Measurement results confirm that the proposed antenna achieves over 20 dB isolation within the 3.4–3.6 GHz operating band, with a compact spacing of 0.008 λ₀ (λ₀ corresponds to the wavelength at the center frequency). Full article

The main objective of proton beam therapy is to precisely irradiate diseased tissue while minimizing damage to healthy cells. For effective treatment, the linear energy transfer (LET) is a key parameter in ensuring the destruction of diseased cells, and both the dose and LET are typically represented as functions of depth. The distribution of dose and LET in the target depends on the beam properties, including beam energy, energy spread, beam size, and beam emittance. The aim of this work is to present the method used to characterize the proton beam properties obtained from the machine employed in the simulation and to determine the dose and dose-averaged LET (LET_d) values, including their peak positions in depth. These results are used to predict the dose and LET_d at different depth positions under experimental conditions. We utilized GEANT4, a Monte Carlo (MC) simulation-based software, to examine the integral depth-dose position and the peak position of the LET_d. The proton source was obtained from a cyclotron accelerator, specifically the Varian ProBeam Compact spot scanning system at King Chulalongkorn Memorial Hospital in Bangkok, Thailand. The system provides proton energies ranging from 70 MeV to 220 MeV. In this study, four proton energies—70 MeV, 100 MeV, 150 MeV, and 220 MeV—were chosen to characterize the beam properties. The 80%–20% distal fall-off obtained from the simulation was used to determine the energy spread for each selected energy by matching the depth-dose peak with the measurement data. The optimal energy spreads were found to be 1.5%, 1.25%, 1%, and 0.5% for proton energies of 70 MeV, 100 MeV, 150 MeV, and 220 MeV, respectively. These energy spreads ensure that the difference in the depth-dose profile is below 1% when comparing the simulated and measured depth-dose profiles. Furthermore, the peak LET_d was found to be approximately 1 mm away from the R₈₀ position, a depth that corresponds to 80% of maximum dose, for each energy. This information can be used to guide the desired LET_d position by utilizing the R₈₀ depth position. Full article

A previously pyrometallurgical process, developed to obtain a Pb-Ag alloy and a slag rich in sulfur from the recycling of a mixture of industrial wastes of jarosite and lead paste, was thermodynamically assessed at 1200 °C. The industrial jarosite sourced from a Mexican zinc hydrometallurgical plant corresponded to an ammonium jarosite with a measurable silver content. The specific heat capacity (Cp) of the ammonium jarosite was obtained from TGA and DSC measurements, as well as the thermodynamic functions of enthalpy, entropy, and Gibbs free energy. The Cp was successfully modeled using polynomial regression, with a second-degree polynomial employed to describe the low-temperature behavior. The thermodynamic data generated were input into the thermodynamic software FactSage 8.2 for modeling of the lead paste–ammonium jarosite-Na₂CO₃-SiC system and represented by stability phase diagrams. The thermodynamic assessment of the pyrometallurgical process predicted compounds formed at high temperatures, showing that a Pb-Ag alloy and a slag rich in Na, S, and Fe (NaFeS₂ and NaFeO₂) were obtained. The compounds formed evidence of the effective sulfur retention in the slag, which is crucial for mitigating SO₂ emissions during high-temperature treatments. The experimental compounds, after solidification, were determined by X-ray diffraction measurements to be Na₂Fe(SO₄)₂ and Na₂(SO₄), which reasonably match the thermodynamic assessment. The heat capacity of the ammonium jarosite provides essential thermodynamic insights into the compositional complexities of industrial waste, which are particularly relevant for thermodynamic modeling and process optimization in pyrometallurgical systems aimed at metal recovery and residue valorization. Full article

This paper establishes some links between Sturm–Liouville problems and the well-known controllability property in linear dynamic systems, together with a control law design that allows any prefixed arbitrary final state finite value to be reached via feedback from any given finite initial conditions. The scheduled second-order dynamic systems are equivalent to the stated second-order differential equations, and they are used for analysis purposes. In the first study, a control law is synthesized for a forced time-invariant nominal version of the current time-varying one so that their respective two-point boundary values are coincident. Afterward, the parameter that fixes the set of eigenvalues of the Sturm–Liouville system is replaced by a time-varying parameter that is a control function to be synthesized without performing, in this case, any comparison with a nominal time-invariant version of the system. Such a control law is designed in such a way that, for given arbitrary and finite initial conditions of the differential system, prescribed final conditions along a time interval of finite length are matched by the state trajectory solution. As a result, the solution of the dynamic system, and thus that of its differential equation counterpart, is subject to prefixed two-point boundary values at the initial and at the final time instants of the time interval of finite length under study. Also, some algebraic constraints between the eigenvalues of the Sturm–Liouville system and their evolution operators are formulated later on. Those constraints are based on the fact that the solutions corresponding to each of the eigenvalues match the same two-point boundary values. Full article

We combine corrected Gaia DR3 astrometry with non-conservative MESA modelling to retrace the evolution of the FS-CMa binary MWC 728. The revised parallax sets the distance at $d=1.2\pm 0.1$ kpc, leading—after Monte-Carlo error propagation—to luminosities of $log{(L/L_{\odot })}_{\mathrm{acc}}=2.6\pm 0.1$ and $log{(L/L_{\odot })}_{\mathrm{don}}=1.5\pm 0.1$, corresponding to the accretor and donor, respectively. A fiducial binary track that starts with $M_{\mathrm{don}}=3.6\pm 0.1M_{\odot }$, $M_{\mathrm{acc}}=1.8\pm 0.1M_{\odot }$, and $P_0=21.0\pm 0.2$ d reproduces the observations provided the Roche-lobe overflow, which is moderately non-conservative: only $39\%$ of the transferred mass is retained by the accretor, while the remainder leaves the system via (i) a fast isotropic wind from the donor ($\alpha =0.01$), (ii) isotropic re-emission near the accretor ($\beta =0.45$), and (iii) outflow into a circumbinary torus ($\delta =0.15$, lever arm $\gamma =1.3$). These channels remove sufficient angular momentum to expand the orbit to the observed $P_{\mathrm{obs}}=27.5\pm 0.1$ d while sustaining the dusty circumbinary outflow. At $t\simeq 223$ Myr, the model matches every current observable: $M_{\mathrm{don}}=1.30\pm 0.05M_{\odot }$, $M_{\mathrm{acc}}=2.67\pm 0.05M_{\odot }$, mass ratio $q=2.0\pm 0.1$, and an ongoing transfer rate of $\dot{M}\simeq (1\pm 0.3)\times {10}^{-6}{M}_{\odot }{\mathrm{yr}}^{-1}$. MWC 728 thus serves as a benchmark intermediate-mass binary for testing how non-conservative outflows regulate angular-momentum loss and orbital growth. Full article

Background/Objectives: Bisphosphonates may influence vascular calcification and atheroma formation via farnesyl pyrophosphate synthase inhibition in the mevalonate pathway regulating bone and lipid metabolism. However, the clinical impact of NCB use on cardiovascular outcomes remains uncertain, largely due to methodological heterogeneity in prior studies. We aimed to evaluate the association between nitrogen-containing bisphosphonate (NCB) therapy and coronary artery calcium (CAC) progression, as well as the incidence of cardiovascular disease (CVD) and coronary heart disease (CHD) events. Methods: From 6814 participants in MESA Exam 1, we excluded males (insufficient male NCB users in the MESA cohort), pre-menopausal women, baseline NCB users, and users of hormone replacement therapy, raloxifene, or calcitonin. Among 166 NCB initiators and 1571 non-users with available CAC measurements, propensity score matching was performed using the available components of FRAX, namely age, race, BMI, LDL cholesterol, alcohol, smoking, and steroid use, and baseline CAC yielded 165 NCB initiators matched to 473 non-users (1:3 ratio). Linear mixed-effects models evaluated CAC progression, and Cox models analyzed incident CVD and CHD events. Results: In the overall cohort, NCB use was not significantly associated with CAC progression (annual change: −0.01 log Agatston units; 95% CI: −0.05 to 0.01). However, among participants with a baseline estimated glomerular filtration rate (eGFR) < 65 mL/min/1.73 m², NCB use was associated with attenuated CAC progression compared with non-users (−0.06 log Agatston units/year; 95% CI: −0.12 to −0.007). No significant association was observed between NCB use and incident CVD events in the overall cohort (HR: 0.90; 95% CI: 0.60−1.36) or within kidney function subgroups. Conclusions: Incident NCB use among postmenopausal women with mild or no CAC at baseline was associated with reduced CAC progression only in women with impaired kidney function. However, this association did not correspond to a decreased risk of subsequent cardiovascular events, suggesting that the observed imaging benefit may not translate into meaningful clinical association. Full article

With the advancement of agricultural automation technologies, apple-harvesting robots have gradually become a focus of research. As their “perceptual core,” machine vision systems directly determine picking success rates and operational efficiency. However, existing vision systems still exhibit significant shortcomings in target detection and positioning accuracy in complex orchard environments (e.g., uneven illumination, foliage occlusion, and fruit overlap), which hinders practical applications. This study proposes a visual system for apple-harvesting robots based on improved Mask R-CNN and binocular vision to achieve more precise fruit positioning. The binocular camera (ZED2i) carried by the robot acquires dual-channel apple images. An improved Mask R-CNN is employed to implement instance segmentation of apple targets in binocular images, followed by a template-matching algorithm with parallel epipolar constraints for stereo matching. Four pairs of feature points from corresponding apples in binocular images are selected to calculate disparity and depth. Experimental results demonstrate average coefficients of variation and positioning accuracy of 5.09% and 99.61%, respectively, in binocular positioning. During harvesting operations with a self-designed apple-picking robot, the single-image processing time was 0.36 s, the average single harvesting cycle duration reached 7.7 s, and the comprehensive harvesting success rate achieved 94.3%. This work presents a novel high-precision visual positioning method for apple-harvesting robots. Full article

Feature matching is crucial in image recognition. However, blurring caused by illumination changes often leads to deviations in local appearance-based similarity, resulting in ambiguous or false matches—an enduring challenge in computer vision. To address this issue, this paper proposes a method named MultS-ORB (Multistage Oriented FAST and Rotated BRIEF). The proposed method preserves all the advantages of the traditional ORB algorithm while significantly improving feature matching accuracy under illumination-induced blurring. Specifically, it first generates initial feature matching pairs using KNN (K-Nearest Neighbors) based on descriptor similarity in the Hamming space. Then, by introducing a local motion smoothness constraint, GMS (Grid-Based Motion Statistics) is applied to filter and optimize the matches, effectively reducing the interference caused by blurring. Afterward, the PROSAC (Progressive Sampling Consensus) algorithm is employed to further eliminate false correspondences resulting from illumination changes. This multistage strategy yields more accurate and reliable feature matches. Experimental results demonstrate that for blurred images affected by illumination changes, the proposed method improves matching accuracy by an average of 75%, reduces average error by 33.06%, and decreases RMSE (Root Mean Square Error) by 35.86% compared to the traditional ORB algorithm. Full article