Local Matching Research Articles

Nonuniform Temperature Compensation in Ultrasonic Guided Wave Pipeline Health Monitoring Using Local Phase Matching

Faced with a complex working environment, pipelines are prone to nonuniform temperature variations, which cause nonuniform phase changes in the guided wave signals during structural health monitoring, thereby increasing the difficulty of monitoring. To address this, a simulation model is established in this paper to analyze the effects of temperature on material parameters and the variation patterns of guided wave signals. A nonuniform temperature compensation method based on local phase matching is proposed. The algorithm first uses cosine similarity to find the locally best-matched signal segments between the monitoring signal and the baseline signal. Then, an indicator is introduced to quantify the differences between these best-matched signal segments, with the maximum difference considered to be the damage index. Three heating experiments on pipelines with nonuniform temperature fields ranging from 24 °C to 80 °C demonstrate that the proposed method can effectively overcome the resulting phase deviations while achieving high detection accuracy and a reduction false positives. Additionally, the method shows high resolution in detecting defects in both temperature-varying and non-temperature-varying regions.

Effect of match location on the playing style of teams coached by ‘Pep’ Guardiola

IntroductionAnalysis in football seeks to find the performance factors that bring teams closer to success.MethodsThis study aims to analyze the playing styles of two teams managed by Pep Guardiola (F.C. Barcelona and Manchester City) based on match location (home or away). Two methods of analysis were used: descriptive statistics through chi-square tests to evaluate game characteristics and the polar coordinates technique to analyze the relationships between the different lines of each team (goalkeeper, defenders, midfielders, and forwards).ResultsThe results showed that F.C. Barcelona maintained a consistent playing style regardless of location, exhibiting significant differences only in actions that involved shots or header (p = 0.035), with better performance at home. In contrast, Manchester City displayed significantly different performance in action success (p < 0.001), level of play elaboration (p = 0.004), density (p = 0.033), duration (p = 0.036), and actions that included a shot (p = 0.001) depending on the location. Additionally, qualitative analyses revealed differences in the relationships among the team lines according to match location, with Manchester City displaying more variability in these interactions than F.C. Barcelona.DiscussionThe study concludes that although Guardiola applies a consistent set of strategies, match location has a greater influence on Manchester City’s performance, suggesting that this team adjusts its playing style on the basis of contextual conditions. These findings highlight the importance of considering factors such as location when preparing tactics to increase the probability of success in elite football.

Metasurface-enabled 3D imaging via local bright spot gray scale matching using the structured light dot array.

Three-dimensional (3D) imaging is widely utilized in various applications, such as light detection, autonomous vehicles, and machine vision. However, conventional 3D imaging systems often rely on bulky optical components. Metasurfaces, as next-generation optical devices, possess flexible wavefront modulation capabilities and excellent combination with computer vision algorithms. Here, we propose a large field-of-view (FOV) structured light dot array projection device based on a metasurface, covering a 2π-FOV, for projecting coded point clouds in Fourier space. We explore a local bright spot gray scale matching algorithm for depth extraction, enabling 3D imaging. This algorithm simplifies the data processing flow and optimizes depth extraction and feature matching processes through a customized region gray scale comparison. As a result, it effectively reduces computational complexity and enhances tolerance to image quality fluctuations. The proposed approach provides new possibilities for developing compact and high-performance planar 3D optical imaging devices, which will drive the advancement of fields such as computer vision and artificial intelligence.

Naked-eye 3D visualization of cultural relics based on integrated imaging

AbstractDigital technology has become crucial for the protection of cultural relics, enabling their acquisition, preservation, exhibition, and dissemination in a non-contact manner.At present, the three-dimensional(3D) visualization technology of cultural relics focuses on the research of non-true 3D visualization, which cannot provide continuous viewing angle and takes a long time to render. This paper focuses on the acquisition and reproduction of digital information related to cultural relics, and proposes to achieve the true 3D display of cultural relics from a continuous perspective based on integrated imaging(InIm). Building up on traditional InIm, an Element image array (EIA) generation algorithm based on local depth template matching is designed by using spatial geometry relation. Experimental results show that the algorithm not only enables naked-eye 3D visualization of cultural relics, but also the generation speed is more than twice that of the compared 3D display data source generation algorithm.

A fingerprint dictionary processing approach in indoor localization system based on wi-fi

Indoor localization using Wi-Fi fingerprinting based on Received Signal Strength (RSS) has gained widespread attention due to its immunity to external factors and ability to penetrate obstacles. The localization process involves an offline phase for building a radio map and an online phase for matching location queries. Existing matching algorithms often prioritize enhancing online phase accuracy, overlooking the importance of offline data preprocessing, which can negatively impact overall performance. This study introduces a novel approach called Fingerprint Dictionary Preprocessing (FDP) that employs Convolutional Dictionary Learning (CDL) to process radio map data. CDL learns a set of kernels capturing site characteristics, representing RSS values from Access Points (APs) in a sparse manner. The proposed FDP system compresses data through feature learning, reducing storage requirements for data transmission. In the online phase, CDL is utilized for assisting matching fingerprints against the learned dictionary, accurately locating users. The contributions of the FDP system presenting a cost-effective and practical solution for indoor localization, addressing the challenges associated with large data collection and multi-dimensional data requirements, making it a promising approach for real-world applications. We conducted experiments in two real indoor environments, and the results indicated that the proposed FDP system, whether applied to the original radio map or the preprocessed fingerprint database, led to improved localization accuracy and reduced localization time.

Intelligent recognition and automatic localization of pipeline welds based on multi-vision system

Abstract Currently, the leakage detection of spacecraft pipeline welds relies on manual point-by-point inspection using a detection gun, which is inefficient and inadequate for the automation needs of spacecraft production. However, the accurate recognition and precise localization of widely distributed and small pipeline welds are crucial for automated detection. Therefore, this paper proposes a multi-vision detection and localization system that integrates global and local information, considering both comprehensive global 3D search and high-precision local 3D measurement. The improved YOLOv8 model is employed for pipeline weld recognition, which improves the recognition rate of welds. Based on the deep learning recognized and segmented welds, this paper proposes stereo matching and segmentation extraction methods for 3D localization and pipeline orientation determination. Additionally, the system integrates a robot to perform automated point-by-point inspection of welds within the area without collisions. The experimental results demonstrate the effectiveness of the improved YOLOv8 and the proposed methods for 3D weld localization and pipeline orientation determination. The maximum deviation of the spatial distance of fine weld positioning is 0.20 mm, and the repeatability of the 3D coordinates is around 0.1 mm. The system can perform precise localization and detection, meeting the requirements for automatic weld recognition and localization.

Design of a Location-Based Case-Control Study of Built Environment Risk Factors for Pedestrian Fatalities in the U.S.

The location-based case-control design is a useful approach for studies where the exposures of interest are aspects of the environment around the location of a health event such as a pedestrian fatality. In this design locations are the unit of analysis and an enumerated cohort of locations are followed through time for the health events of interest and a case-control study of locations is nested within the cohort. Locations where events occurred (case-locations) are compared to matched locations where these events did not occur (control-locations). We describe the application of this design to the issue of pedestrian fatalities using a cohort of 9,612,698 intersections, 17,737,728 road segments, and 222,318 entrance/exit ramp segments that existed in 2017 across all 384 U.S. Metropolitan Statistical Areas. This cohort of locations was followed up from Jan 1, 2017 to Dec 31, 2018 for pedestrian fatalities using the National Highway Traffic Safety Administration Fatality Analysis Reporting System. In total, 10,587 fatalities were identified as having occurred on cohort locations and 21,174 matched control locations were selected using incidence density sampling. Geographic information systems, spatially linked administrative data sets and virtual neighborhood audits via Google Street View are underway to characterize study locations.

Tactical analysis of direct attack from the English Premier League and Spanish La Liga.

The aim of this study was to evaluate the effect of tactical and contextual dimensions on the offensive performance, in terms of degree of penetration or creation of goal scoring opportunities (GSO), of direct attacks in the English Premier League (EPL) and Spanish La Liga. In total, 10,078 team possessions from 40 random La Liga and EPL matches were initially selected. From that, 2,384 possessions categorized as open play direct attacks were selected for analysis, including a first univariate binary regression analysis and a further construction of adjusted binary multivariate logistic regression models. Four independent tactical dimensions ("Initial zone," "Initial opponent position," "Initial opponent pressure," and "Width of the possession") and four independent contextual dimensions ("Match location," "Match status," "Quality of opponent," and "Quality of the observed team") were initially analyzed to predict penetration performance on one hand and scoring opportunity performance on the other hand. The results showed that the distribution of the type of attacks was different in La Liga and EPL (χ 2 = 11.015, p = 0.001), but direct attacks were not different in La Liga and EPL in terms of performance. Three of the four tactical dimensions ("Initial zone," "Initial opponent position," and "Width of the possession") showed association with "Penetration performance" (p < 0.01), while only "Width of the possession" showed association with "Scoring opportunity performance" (p < 0.01). Two of the four contextual dimensions ("Match status" and "Quality of observed team") showed association with "Penetration performance" (p < 0.05), while none of them showed association with "Scoring opportunity performance." In conclusion, direct attacks are frequent in EPL and La Liga but very ineffective offensively in terms of creation of goal scoring opportunities. The tactical dimensions that explain a higher effectiveness in terms of getting penetrative performance are vertical progression through the field, attacking against a defensive low-block, as well as starting the team possessions from the defensive zones instead of middle zones, while the only tactical dimension that explains higher performance in creating GSOs is vertical progression. Contextual dimensions, such as "Match status" and "Team level," also influence the offensive penetration performance during direct attacks but not GSO performance.

FederEI: Federated Library Matching Framework for Electron Ionization Mass Spectrum Based Compound Identification.

Mass spectrometry (MS) is a powerful tool for compound structure elucidation, crucial in various domains including pharmaceuticals, chemical synthesis, environmental analysis, and metabolomics. Compound identification via spectral matching relies heavily on reference libraries, yet existing universal libraries may lack comprehensiveness. Laboratories often create their own libraries, leading to fragmentation within the scientific community. Addressing this, we propose FederEI, a federated library matching solution for EI-MS-based compound identification. FederEI enables decentralized compound identification while preserving data privacy, achieved through a server-to-server connection framework. Each laboratory retains control over its spectral data, enhancing privacy and minimizing data transmission. FederEI integrates a fast and accurate library matching algorithm, offering comparable performance to local matching while maintaining data security. Evaluation against the classic approach demonstrates FederEI's effectiveness, ensuring robust compound identification.

Research on the railway multi-source homonymous geographical entity matching algorithm based on dynamic time warping

This study aims to explore an efficient technique for matching multisource homonymous geographical entities in railways to address the identification issues of homonymous geographical entities. Focusing on railway line vector spatial data, this research investigates the matching problem of multisource homonymous geographical entities. Building on statistical feature matching of attribute data, a curve similarity calculation method based on the DTW algorithm is designed to achieve better local elastic matching, overcoming the limitations of the Fréchet algorithm. The empirical study utilizes railway line layer data from two data sources within Beijing’s jurisdiction, fusing 6237 segment lines from source 2 with 105 long lines from source 1. The structural comparison between the two data sources is conducted through statistical methods, applying cosine similarity and the maximum similarity value of TF-IDF for text similarity calculation. Finally, Python is used to implement the DTW algorithm for curve similarity. The experimental results show an average DTW distance of 3.92, a standard deviation of 4.63, and a mode of 0.005. Similarity measurement results indicate that 95.53% of records are within the predetermined threshold, demonstrating the effectiveness and applicability of the method. The findings significantly enhance the accuracy of railway data matching, promoting the informatization of the railway industry, and hold substantial significance for improving railway operational efficiency and system performance.

Object and spatial discrimination makes weakly supervised local feature better

Local feature extraction plays a crucial role in numerous critical visual tasks. However, there remains room for improvement in both descriptors and keypoints, particularly regarding the discriminative power of descriptors and the localization precision of keypoints. To address these challenges, this study introduces a novel local feature extraction pipeline named OSDFeat (Object and Spatial Discrimination Feature). OSDFeat employs a decoupling strategy, training descriptor and detection networks independently. Inspired by semantic correspondence, we propose an Object and Spatial Discrimination ResUNet (OSD-ResUNet). OSD-ResUNet captures features from the feature map that differentiate object appearance and spatial context, thus enhancing descriptor performance. To further improve the discriminative capability of descriptors, we propose a Discrimination Information Retained Normalization module (DIRN). DIRN complementarily integrates spatial-wise normalization and channel-wise normalization, yielding descriptors that are more distinguishable and informative. In the detection network, we propose a Cross Saliency Pooling module (CSP). CSP employs a cross-shaped kernel to aggregate long-range context in both vertical and horizontal dimensions. By enhancing the saliency of keypoints, CSP enables the detection network to effectively utilize descriptor information and achieve more precise localization of keypoints. Compared to the previous best local feature extraction methods, OSDFeat achieves Mean Matching Accuracy of 79.4% in local feature matching task, improving by 1.9% and achieving state-of-the-art results. Additionally, OSDFeat achieves competitive results in Visual Localization and 3D Reconstruction. The results of this study indicate that object and spatial discrimination can improve the accuracy and robustness of local feature, even in challenging environments. The code is available at https://github.com/pandaandyy/OSDFeat.

Local labor match and corporate investments: Evidence from new flight routes

Local labor match and corporate investments: Evidence from new flight routes

Synthesis of fullerite (100) through epitaxy on a complex intermetallic surface

We report the first epitaxial growth of bulk-like C60(100) plane stabilized by a size-matching effect on the surface of a complex intermetallic compound. As characterized by low energy electron diffraction (LEED) and scanning tunneling microscopy (STM) techniques, the adsorption of C60 on Au–Si–Ho(100) surface is disordered at room temperature but ordered when the substrate is held at elevated temperature (669 K). In the submonolayer regime, different superstructures are observed depending on the fullerenes coverage. Upon completion of the first monolayer of C60, the thin film of c(1 × 1) superstructure is structurally equivalent to a (100) surface plane of the face-centered cubic structure of C60 bulk crystal. This structure is unusual for C60 monolayers on metal surfaces and appears to be favored by an almost perfect match between the most preferred C60 intermolecular distance and the distances separating specific adsorption sites of the large unit cell compound. The adsorption site selection is dictated by a local symmetry matching between the pentagonal fullerene facets and pseudo-five-fold symmetric atomic arrangements on the substrate. After the completion of the first monolayer, further deposition of C60 at 387 K results in the growth of a (100) oriented thicker epitaxial fullerite crystal.

Retinal Input to Macaque Superior Colliculus Derives from Branching Axons Projecting to the Lateral Geniculate Nucleus.

The superior colliculus receives a direct projection from retinal ganglion cells. In primates, it remains unknown if the same ganglion cells also supply the lateral geniculate nucleus. To address this issue, a double-label experiment was performed in 2 male macaques. The animals fixated a target while injection sites were scouted in the superior colliculus by recording and stimulating with a tetrode. Once suitable sites were identified, cholera toxin - subunit B Alexa Fluor 488 was injected via an adjacent micropipette. In a subsequent acute experiment, cholera toxin subunit B - Alexa Fluor 555 was injected into the lateral geniculate nucleus at matching retinotopic locations. After a brief survival period, ganglion cells were examined in retinal flatmounts. The percentage of double-labeled cells varied locally, depending on the relative efficiency of retrograde transport by each tracer and the precision of retinotopic overlap of injection sites in each target nucleus. In counting boxes with extensive overlap, 76-98% of ganglion cells projecting to the superior colliculus were double-labeled. Cells projecting to the superior colliculus constituted 4.0 - 6.7% of the labeled ganglion cell population. In one particularly large zone, there were 5,746 cells labeled only by CTB-AF555, 561cells double-labeled by CTB-AF555 and CTB-AF488, but no cell labeled only by CTB-AF488. These data indicate that retinal input to the macaque superior colliculus arises from a collateral axonal branch supplied by about 5% of the ganglion cells that project to the lateral geniculate nucleus. Surprisingly, there exist no ganglion cells that project exclusively to the SC.Significance statement The retina contains a multitude of ganglion cell classes, projecting in parallel to different brain targets. The superior colliculus receives retinal input, but its source remains controversial. In two macaques, a green tracer was injected into the superior colliculus and an orange tracer into the lateral geniculate nucleus. When retinotopic overlap was optimal, ganglion cells containing the green tracer were also labeled by the orange tracer. This finding indicates that retinal input to the superior colliculus arises from axon collaterals of ganglion cells that project to the lateral geniculate nucleus, even though these two retinal targets serve utterly different functions. The next challenge is to determine the purpose of this projection and why it is shared.

Systematic video analysis of ACL injuries in professional Spanish male football (soccer): injury mechanisms, situational patterns, biomechanics and neurocognitive errors – a study on 115 consecutive cases

ObjectiveA few video analysis studies have been published in recent years, but none specifically on Spanish football. We aimed to describe the mechanisms, situational patterns, biomechanics and neurocognitive errors related...

A spatiotemporal shape model fitting method for within-season crop phenology detection

Crop phenological information must be reliably acquired earlier in the growing season to benefit agricultural management. Although the popular shape model fitting (SMF) method and its various improved versions (e.g., SMF by the Separate phenological stage, SMF-S) have been successfully applied to after-season crop phenology detection, these existing methods cannot be applied to within-season crop phenology detection. This discrepancy arises due to the fact that, in the within-season scenario, phenological stages can beyond the defined cut-off time. Consequently, enhancing the alignment of the vegetation index (VI) curve segments prior to the cut-off time does not necessarily guarantee accurate within-season phenological detection. To resolve this issue, a new method named spatiotemporal shape model fitting (STSMF) was developed. STSMF does not seek to optimize the local curve matching between the target pixel and the shape model; instead, it determines similar local VI trajectories in the neighboring pixels of previous years. The within-season phenology of the target pixel was thus estimated from the corresponding phenological stage of the determined local VI trajectories. When compared with ground phenology observations, STSMF outperformed the existing SMF and SMF-S which were modified for the within-season scenario (SMFws and SMFSws) with the smallest mean absolute differences (MAE) between observed phenological stages and their corresponding model estimates. The MAE values averaged over all phenological stages for STSMF, SMFSws, and SMFws were 9.8, 12.4, and 27.1 days at winter wheat stations; 8.4, 14.9, and 55.3 days at corn stations; and 7.9, 12.4, and 64.6 days at soybean stations, respectively. Intercomparisons between after-season and within-season regional phenology maps also demonstrated the superior performance of STSMF (e.g., correlation coefficients for STSMF and SMFSws are 0.89 and 0.80 at the maturity stage of winter wheat). Furthermore, the performance of STSMF was less affected by the detection time and the determination of shape models. In conclusion, the straightforward, effective, and stable nature of STSMF makes it suitable for within-season detection of agronomic phenological stages.

CorMatcher: A corners-guided graph neural network for local feature matching

Local feature matching is crucial for various computer vision tasks like Structure from Motion (SfM) and visual localization. In human visual matching, individuals typically scan image pairs to detect and match prominent corners, then refine matches by leveraging the geometric structure information from these corners. Inspired by this observation, we propose CorMatcher, a corners-guided detector-free network that investigates the guiding role of prominent corners in local feature matching. The concept of CorMatcher is utilizing the corners-images interaction module (CIIM) and deep supervision technique (DST) to replicate the aforementioned human behaviors, thus generating more human-intuitive features for accurate feature matching. CIIM imitates human behavior that first observes corners to guide the matching process, and then extend the matching space from corners to entire images. Technically, In particular, CIIM first integrates global information across all keypoints to ensure overall perception. Then, CIIM extracts reliable corners, performs feature enhancement among them, and propagates the enhanced features of corners to entire images. Finally, CIIM interacts features of all keypoints to ensure that the global information is comprehensively considered to establish final matches. In addition, we apply DST to the corners, rather than all keypoints, to supervise matching results after each feature enhancement process, thus simulating the human behavior that appraises matching results after each observation. Comprehensive experiments demonstrate that CorMatcher exhibits superior performance compared to several benchmarks.

Siamese Tracking Network with Multi-attention Mechanism

Object trackers based on Siamese networks view tracking as a similarity-matching process. However, the correlation operation operates as a local linear matching process, limiting the tracker’s ability to capture the intricate nonlinear relationship between the template and search region branches. Moreover, most trackers don’t update the template and often use the first frame of an image as the initial template, which will easily lead to poor tracking performance of the algorithm when facing instances of deformation, scale variation, and occlusion of the tracking target. To this end, we propose a Simases tracking network with a multi-attention mechanism, including a template branch and a search branch. To adapt to changes in target appearance, we integrate dynamic templates and multi-attention mechanisms in the template branch to obtain more effective feature representation by fusing the features of initial templates and dynamic templates. To enhance the robustness of the tracking model, we utilize a multi-attention mechanism in the search branch that shares weights with the template branch to obtain multi-scale feature representation by fusing search region features at different scales. In addition, we design a lightweight and simple feature fusion mechanism, in which the Transformer encoder structure is utilized to fuse the information of the template area and search area, and the dynamic template is updated online based on confidence. Experimental results on publicly tracking datasets show that the proposed method achieves competitive results compared to several state-of-the-art trackers.

External Load Evaluation in Elite Futsal: Influence of Match Results and Game Location with IMU Technology.

The purpose of this study was to assess the external load demands in futsal, considering both home and away matches and their outcomes, in order to plan microcycles throughout the season based on the external load of each match. The external load of 10 players from a First Division team in the Spanish Futsal League was recorded throughout 15 official matches in the first half of the league championship. The players' external load was monitored using OLIVER devices. To analyse the influence of the match outcome and location on the external load, a univariate general linear model (GLM) analysis was conducted with Bonferroni post hoc. There are no differences between the variables neither comparing results nor location factors, except for accelerations of 2 to 3 m/s2 (m) per minute and the number of accelerations of 2 to 3 m/s2 per minute, reporting higher value winnings at home than away (p < 0.05). The location and results are not factors that influence on external load in futsal matches, except the number and distance performed in accelerations and distance covered at a low to medium speed. These findings are important for planning microcycles and providing the appropriate dosage to each player to achieve optimal performance in matches.

Infrared low-altitude and slow-speed small target detection via fusion of target sparsity and motion saliency

Infrared (IR) small target detection exerts a significant role in IR early warning and UAV surveillance. However, in the low-altitude slow-speed small (LSS) target detection scene, the existing algorithms cannot effectively suppress high-contrast corners and sparse edges in the low-altitude background, resulting in many false alarms. To solve this problem, we propose an IR LSS target detection method based on fusion of target sparsity and motion saliency (TSMS). In the low-rank sparse model, we introduce a robust dual-window gradient operator to construct a fine local prior, which avoids the influence of highlighted edges and corners; The Geman norm is used to approximate the background rank to accurately estimate the background and effectively extract sparse targets. Then, a motion saliency model based on inter-frame local matching is constructed to accurately extract the inter-frame features of small target. Finally, the real LSS target is obtained by fusing target sparsity and motion saliency. Experiments indicate that, compared with existing advanced methods, the proposed method has stronger robustness and can effectively detect LSS targets under complex low-altitude background.