Local Point Research Articles

Comprehensive assessment of nonuniform image quality: Application to imaging near metal.

Comprehensive assessment of image quality requires accounting for spatial variations in (i) intensity artifact, (ii) geometric distortion, (iii) signal-to-noise ratio (SNR), and (iv) spatial resolution, among other factors. This work presents an ensemble of methods to meet this need, from phantom design to image analysis, and applies it to the scenario of imaging near metal. A modular phantom design employing a gyroid lattice is developed to enable the co-registered volumetric quantitation of image quality near a metallic hip implant. A method for measuring spatial resolution by means of local point spread function (PSF) estimation is presented and the relative fitness of gyroid and cubic lattices is examined. Intensity artifact, geometric distortion, and SNR maps are also computed. Results are demonstrated with 2D-FSE and MAVRIC-SL scan protocols on a 3T MRI scanner. The spatial resolution method demonstrates a worst-case error of 0.17 pixels for measuring in-plane blurring up to 3 pixels (full width at half maximum). The gyroid outperforms a cubic lattice design for the local PSF estimation task. The phantom supports four configurations toggling the presence/absence of both metal and structure with good spatial correspondence for co-registered analysis of the four quality factors. The marginal scan time to evaluate one scan protocol amounts to five repetitions. The phantom design can be fabricated in 2 days at negligible material cost. The phantom and associated analysis methods can elucidate complex image quality trade-offs involving intensity artifact, geometric distortion, SNR, and spatial resolution. The ensemble of methods is suitable for benchmarking imaging performance near metal.

Complex charge density waves in simple electronic systems of two-dimensional III2–VI3 materials

AbstractCharge density wave (CDW) is the phenomenon of a material that undergoes a spontaneous lattice distortion and modulation of the electron density. Typically, the formation of CDW is attributed to Fermi surface nesting or electron-phonon coupling, where the CDW vector (QCDW) corresponds to localized extreme points of electronic susceptibility or imaginary phonon frequencies. Here, we propose a new family of multiple CDW orders, including chiral Star-of-David configuration in nine 2D III2–VI3 van der Waals materials, backed by first-principles calculations. The distinct feature of this system is the presence of large and flat imaginary frequencies in the optical phonon branch across the Brillouin zone, which facilitates the formation of the diverse CDW phases. The electronic structures of 2D III2–VI3 materials are relatively simple, with only III-s,p and VI-p orbitals contributing to the formation of the CDW order. Despite that, the CDW transitions involve both metal-to-insulator and insulator-to-insulator transitions, accompanied by a significant increase in the bandgap caused by an enhanced electronic localization. Our study not only reveals a new dimension in the family of 2D CDWs, but is also expected to offer deeper insights into the origins of the CDWs.

A Similar Feature Point Matching Method for Aerial Electric Power Tower Images Based on a One-Ring Neighborhood of Vertices

Due to the susceptibility of images to various factors such as scale changes, imaging conditions, and image noise, traditional feature point matching methods are difficult to achieve ideal matching accuracy, leading to many challenges in the automatic analysis and processing of power tower images. To improve the matching accuracy of similar feature points in aerial images of electric power tower, this study proposes a method for matching similar feature points in aerial images of electric power tower based on a vertex ring neighborhood, addressing the problem of large matching errors caused by factors such as scale and imaging conditions. This method first adopts the feature point extraction technique of electric power tower image based on decomposition and filtering, and constructs the Laplacian pyramid of the original image. Subsequently, filter banks are used to decompose the pyramid image in different directions, extract local extremum points as candidate feature point sets, and merge them to obtain the final feature point set. On this basis, taking into account the spatial relationships and geometric characteristics around the feature points, a texture mapping algorithm based on vertex ring neighborhood and patch classification is applied to construct a vertex ring neighborhood structure and extract geometrically representative electric power tower features. Finally, by using a texture feature point matching method based on the principle of similarity, the similarity between the real-time image and the reference image features is calculated for matching, and combined with the Babbitt coefficient to remove mismatched feature points, accurate matching of similar feature points in aerial electric power tower images is achieved. The experimental results show that this method has a mean square error of less than 0.1Pixel in matching similar texture feature points of aerial power tower images under various working conditions, significantly improving the matching accuracy and providing an effective tool for automatic analysis and processing of power tower images.

Arthroscopically assisted accurate location of the bone tunnel entrance for lateral ankle ligament reconstruction may be a better choice for patients with chronic ankle instability: a retrospective study.

The anatomical location for lateral ligament reconstruction remains a significant challenge. With the development of arthroscopic technology, arthroscopic-assisted lateral ligament reconstruction can significantly facilitate the correct localization of anatomical points. However, there is limited evidence on the clinical efficacy of arthroscopic ligament reconstruction compared with percutaneous ligament reconstruction. This study included 72 patients who underwent lateral ligament reconstruction for chronic lateral ankle instability from 2018 to 2022. The follow-up duration in the percutaneous(n = 35) and the arthroscopic(n = 37) groups was 6-30 months. The patients were evaluated before the operation, at 6 months after surgery, and at the final follow-up. The American Orthopaedic Foot and Ankle Society score (AOFAS), Karlsson-Peterson score, and Visual Analog Scale (VAS) were evaluated at each time point. The complications and the time required to return to sports were documented during the follow-up. All clinical evaluations significantly improved after surgery in both groups. There was no significant difference between the two groups in the AOFAS score, VAS score, and complications. The Karlsson-Peterson score (85.4 vs 83.6, p = 0.044), surgical duration (50.4min vs 60.2min, p < 0.001), and time to return to sports (17.8 weeks vs 20.5 weeks, p = 0.033) were all improved in the arthroscopic group. Satisfactory clinical outcomes can be achieved through both arthroscopic and percutaneous anatomic ligament reconstruction. The ligament remnant and anatomical localization point can be observed directly by using an arthroscope. Arthroscopic ligament reconstruction can be a more viable alternative for patients with chronic ankle instability due to its minimal disturbance to the stump of the ligament and reduced operative aggression.

Species distribution modeling of North American beavers from the late Pliocene into the future

Abstract Beavers have occurred in North America since at least 7 Ma, but relatively little is known about their distribution across the continent. We modeled distributions of beavers in the late Pliocene (3.3 Ma), Pleistocene (130 ka and 21 ka), and recent Holocene (1970 to 2000) to understand their dispersal across North America, predict future distributions and predict their possible response to future climate and habitat changes. Occurrence data for Castor canadensis were derived from the Global Biodiversity Information Facility. Those data were used with both modern (1970 to 2000) and modeled future (EC-Earth-Veg 2081 to 2100) bioclimatic variables from WorldClim as well as past (Pliocene Marine Isotope Stage M2, Pleistocene Last Interglacial, and Pleistocene Last Glacial Maximum) bioclimatic variables from PaleoClim to model beaver distributions through time. Fossil locality points for Castor extracted from the New and Old Worlds Database of Fossil Mammals (NOW), NEOTOMA Paleoecology Database, and Paleobiology Database were overlain on past projection models to use as validation points. Models were run using MaxEnt with post-processing in ArcGIS. Accuracy for the 5 models ranged between 59.6% and 60.2%. Results for the present model (1970 to 2000) showed habitat suitability in areas beavers inhabit today. During the Pliocene MIS M2 cooling event (3.3 Ma) and Pleistocene Last Glacial Maximum (21 ka), habitat suitability shifted further south into Mexico and peninsular Florida and away from more periglacial northern regions. During the Last Interglacial period (130 ka) and modeled future (2081 to 2100) EC-Earth-Veg 2081 to 2100, habitat suitability was higher in coastal and central regions in North America and lower in southern regions compared to their present distribution. Distributions were most affected by precipitation seasonality, isothermality, and mean annual temperature. High variability in seasonal precipitation and temperatures is likely to influence surface water availability, vegetation type, and riparian vegetation composition, which consequently may reduce available food resources and habitat for beavers. Observed shifts during warmer periods may indicate areas in the late Miocene that facilitated dispersal into North America. Future models using other predicted climatic scenarios and shared socioeconomic pathways may provide better resolution of potential future shifts in beaver distribution with best- and worst-case climate scenarios, thereby permitting at-risk areas to be prioritized for conservation in the face of climate change.

Single-Shot, Monochrome, Spatial Pixel-Encoded, Structured Light System for Determining Surface Orientations

This study introduces a technique for determining surface orientations by projecting a monochrome, spatial pixel-encoded pattern and calculating the surface normals from single-shot measurement. Our method differs from traditional methods, such as shape from shading and shape from texture, in that it does not require relating the local surface orientations of adjacent points. We propose a multi-resolution system incorporating symbols varying in sizes from 8 × 8, 10 × 10, 12 × 12, 14 × 14, and 16 × 16 pixels. Compared to previous methods, we have achieved a denser reconstruction and obtained a 5.2 mm resolution using an 8 × 8 pattern at a depth of 110 cm. Unlike previous methods, which used local point orientations of grid intersection and multiple colors, we have used the monochrome pattern and deterministic centroid positions to compute the unit vector or direction vector between the neighboring symbols. The light plane intersections are used to calculate the tangent vectors on the surface. Surface normals are determined by the cross-product of two tangent vectors on the surface. A real experiment was conducted to measure simple plane surfaces, circular surfaces, and complex sculptures. The results show that the process of calculating surface normals is fast and reliable, and we have computed 1654 surface normals in 29.4 milliseconds for complex surfaces such as sculptures.

Multi-View Fusion-Based Automated Full-Posture Cattle Body Size Measurement

Cattle farming is an important part of the global livestock industry, and cattle body size is the key indicator of livestock growth. However, traditional manual methods for measuring body sizes are not only time-consuming and labor-intensive but also incur significant costs. Meanwhile, automatic measurement techniques are prone to being affected by environmental conditions and the standing postures of livestock. To overcome these challenges, this study proposes a multi-view fusion-driven automatic measurement system for full-attitude cattle body measurements. Outdoors in natural light, three Zed2 cameras were installed covering different views of the channel. Multiple images, including RGB images, depth images, and point clouds, were automatically acquired from multiple views using the YOLOv8n algorithm. The point clouds from different views undergo multiple denoising to become local point clouds of the cattle body. The local point clouds are coarsely and finely aligned to become a complete point cloud of the cattle body. After detecting the 2D key points on the RGB image created by the YOLOv8x-pose algorithm, the 2D key points are mapped onto the 3D cattle body by combining the internal parameters of the camera and the depth values of the corresponding pixels of the depth map. Based on the mapped 3D key points, the body sizes of cows in different poses are automatically measured, including height, length, abdominal circumference, and chest circumference. In addition, support vector machines and Bézier curves are employed to rectify the missing and deformed circumference body sizes caused by environmental effects. The automatic body measurement system measured the height, length, abdominal circumference, and chest circumference of 47 Huaxi Beef Cattle, a breed native to China, and compared the results with manual measurements. The average relative errors were 2.32%, 2.27%, 3.67%, and 5.22%, respectively, when compared with manual measurements, demonstrating the feasibility and accuracy of the system.

Research on combined damage characteristics of underwater armor-piercing and explosion based on supercavitating projectile

Recent studies of supercavitating projectiles primarily focus on the formation and evolution of the cavity, as well as its underwater ballistic characteristics, while neglecting the terminal damage effects. Little attention has been given to exploring the combined damage effects of armor-piercing-explosion supercavitating projectiles (APESP). Therefore, this study comparatively analyzes the response processes and failure modes of an underwater aluminum alloy cylindrical shell target under the action of three different types of loads: armor piercing, explosion, and combined armor-piercing and explosion. This study investigates the underwater combined damage mechanisms of the APESP, clarifies each damage phase under the combined effect, discusses the advantages of damage resulting from the combined armor-piercing and explosion effect based on the target responses and damage modes, and explores the reasons for dissipation of explosion energy. The results show that: the APESP combines localized point damage characteristics of armor piercing with overall surface damage features of underwater explosion. Depending on load stages and target responses, the target response process under the action of the APESP can be divided into the hydrodynamic ram phase, penetration phase, shock wave phase, stable vibration phase, and bubble pulsation phase. The entire physical system can be abstracted as a low-frequency series spring system (equivalent to bubble pulsation frequency) with high-frequency external energy input, based on the energy relationship of the medium and the structure. The concept of the 'blower effect' is proposed based on target behavior during the stable vibration phase. Following the application of different loads, the plastic deformation of the target in a stable state is ranked as: underwater explosion > combined armor-piercing and explosion > underwater armor piercing. Supercavity, shell casing and penetration hole will cause the dissipation of explosion energy.

Microfocused Ultrasound in Regenerative Aesthetics: A Narrative Review on Mechanisms of Action and Clinical Outcomes.

Microfocused ultrasound with visualization (MFU-V) is widely used in aesthetic medicine for skin tightening and rejuvenation. However, its role in regenerative aesthetics and its precise mechanism of action are not fully understood. This narrative review aims to contextualize and articulate the mechanism of action of MFU-V, evaluate its role in regenerative aesthetics, and assess its effectiveness based on existing clinical, histological, and skin-mechanical studies. A comprehensive literature search was performed to collect and analyze studies on MFU's biological mechanisms, clinical outcomes, and impact on extracellular matrix (ECM) regeneration. The review integrates findings from clinical trials, histological analyses, and biomechanical assessments to provide a cohesive understanding of MFU-V's role in aesthetic medicine. MFU-V emits focused ultrasound energy that penetrates multiple skin layers and the superficial musculoaponeurotic system, creating localized thermal coagulation points. These points initiate biological responses that recruit fibroblasts and stimulate the production of new collagen and elastin fibers. Enhanced ECM protein synthesis leads to significant improvements in skin biomechanics and quality, reducing skin laxity and enhancing appearance. Clinical studies support these findings, showing improvements in skin firmness and texture following MFU-V treatment. Through analyzing the underlying biological mechanisms and the observable clinical outcomes, this narrative review sets the stage for a comprehensive understanding of the mechanism of action and role of MFU-V in regenerative aesthetics.

Equivalence between face nonsignaling correlations, full nonlocality, all-versus-nothing proofs, and pseudotelepathy

We show that a quantum correlation p is in a face of the nonsignaling polytope with no local points if and only if p has nonlocal content 1, if and only if p allows for a Greenberger-Horne-Zeilinger-like proof, and if and only if p provides a perfect strategy for a nonlocal game. That is, face nonsignaling (FNS) correlations, full nonlocality (FN), all-versus-nothing (AVN) proofs, and pseudotelepathy (PT) are equivalent. This shows that different resources behind a wide variety of fundamental results are in fact the same resource. We demonstrate that quantum correlations with FNS = FN = AVN = PT do not need to maximally violate a tight Bell inequality. We introduce a method for identifying quantum FNS = FN = AVN = PT correlations and use it to prove quantum mechanics does not allow for FNS = FN = AVN = PT neither in the (3,3;3,2) nor in the (3,2;3,4) Bell scenarios. This solves an open problem that, because of the FNS = FN = AVN = PT equivalence, has implications in several fields. Published by the American Physical Society 2024

Displacement field monitoring of tunnel faces using terrestrial laser scanning data

The instability of tunnel faces is a serious geological hazard, thus requiring monitoring and prediction of the stability of tunnel faces. This paper presents a high-accuracy method for estimating the displacement fields of tunnel faces from terrestrial laser scanning data. The proposed method consists of four parts: i) the development of a stable rigid-body reference under planar strain assumption, ii) the fine alignment of the rigid-body references using the developed local deformation constrained rigid-body assumption (LDC-RBA) and local cross-validation iterative closest point (LCV-ICP) algorithm, iii) the segmentation of tunnel faces using a machine learning classifier, and iv) tracking of tunnel face displacement vectors under the LDC-RBA. Qualitative experimental results demonstrate that the estimated displacement fields of the tunnel face using the proposed method conform to the deformation law. Furthermore, quantitative experimental results indicate that the estimation accuracy of tunnel face displacement fields achieves millimeter-level.

Boosting point cloud understanding through graph convolutional network with scale measurement and high-frequency enhancement

Graph-based methods have exhibited exceptional performance in point cloud understanding by capturing local geometric relationships. However, existing approaches often struggle to characterize the overall spatial scale of local graphs. In addition, they fail to capture the differences between nodes effectively, which is crucial for distinguishing different classes. This study introduces SM-HFEGCN, a novel graph convolutional network that addresses these limitations through two key innovations: scale measurement and high-frequency enhancement. First, we introduce a spatial scale feature derived from the diagonal vectors of the neighborhood, which serves as a unique graph-specific property related to the geometry and density of the local point cloud. This feature can characterize the overall spatial scale of the local point cloud. Second, we enhance the high-frequency information to capture node variations and integrate it with smoothed information to represent the differences and similarities between nodes simultaneously. Extensive experiments demonstrate the effectiveness of SM-HFEGCN in point cloud classification and segmentation tasks.

A convexity-constrained parameterization of the random effects generalized partial credit model.

An alternative closed-form expression for the marginal joint probability distribution of item scores under the random effects generalized partial credit model is presented. The closed-form expression involves a cumulant generating function and is therefore subjected to convexity constraints. As a consequence, complicated moment inequalities are taken into account in maximum likelihood estimation of the parameters of the model, so that the estimation solution is always proper. Another important favorable consequence is that the likelihood function has a single local extreme point, the global maximum. Furthermore, attention is paid to expected a posteriori person parameter estimation, generalizations of the model, and testing the goodness-of-fit of the model. Procedures proposed are demonstrated in an illustrative example.

Computing Quadratic Eigenvalues and Solvent by a New Minimization Method and a Split-Linearization Technique

To solve quadratic eigenvalue problems (QEPs), especially the gyroscopic systems, two methods are proposed: an iterative direct detection method (DDM) of the complex eigenvalues of the original QEP, and a split-linearization method (SLM) for finding the solvent matrix, which results to a standard linear eigenvalue problem (LEP) being solved to compute all eigenvalues by the symmetry extension. Reducing the dimension to one-half, the LEP is recast in a simpler QEP involving the square of the solvent. We set up two new merit functions which are minimized to detect the complex eigenvalues from the original QEP and a simpler QEP. For each eigen-parameter the merit function consists of the Euclidean norm of each derived eigen-equation, whose vector variable is solved from a derived inhomogeneous linear system. Then, the golden section search algorithm is employed to minimize the merit functions and locate the complex eigenvalue as a local minimal point. The results are compared with that computed by the cyclic-reduction-based solvent (CRS) method.

Localization in MR-based Indoor Navigation System using point cloud registration

Abstract. This paper addresses the pervasive challenge of localization within indoor navigation systems. Many existing systems grapple with this issue, relying on marker-based methods, Bluetooth beacon localization, and image processing. However, these solutions often necessitate the placement of predetermined markers within the environment, presenting limitations such as marker deterioration over time, spatial constraints, and associated costs. In response, we propose a novel point cloud registration-based approach. This method involves the creation of a comprehensive global point cloud representing the entire indoor environment, effectively serving as a three-dimensional virtual map. Concurrently, a mixed-reality device is utilized to generate a local point cloud specific to the device’s current location within the building. By aligning and registering the local cloud with the global counterpart, the device’s precise location within the virtual map can be determined. This approach enables seamless navigation within indoor environments without the reliance on physical markers, offering a versatile and cost-effective solution to the challenge of localization in indoor navigation systems. Finally, an error analysis was also done to test localization accuracy for different illumination conditions, demonstrating the effectiveness of this method.

Smooth Dispersion Is Physically Appropriate: Assessing and Amending the D4 Dispersion Model.

The addition of dispersion corrections to density functionals is essential for accurate energy and geometry predictions. Among them, the D4 scheme is popular due to its low computational cost and high accuracy. However, due to its design, the D4 correction can occasionally lead to anomalies, such as unphysical curvature and bumps in the potential energy surface. We find these anomalies are common in the D4 model, although observable consequences are rarer than in the D3 model for reasons we explain. Nevertheless, we uncover instances of unphysical local minima and stationary points with the D4 scheme and propose two solutions that yield smoother dispersion energy as a function of nuclear position. One is trivial to implement, based on a smoother reparametrization of Gaussian weighting (D4S) to find the effective coordination number. The other replaces Gaussian weighting with soft linear interpolation (D4SL). These new approaches usually remove artificial extremum points, while maintaining accuracy.

PillarVTP: vehicle trajectory prediction method based on local point cloud aggregation and receptive field expansion

PillarVTP: vehicle trajectory prediction method based on local point cloud aggregation and receptive field expansion

Vibration Position Detection of Robot Arm Based on Feature Extraction of 3D Lidar.

In the process of construction, pouring and vibrating concrete on existing reinforced structures is a necessary process. This paper presents an automatic vibration position detecting method based on the feature extraction of 3D lidar point clouds. Compared with the image-based method, this method has better anti-interference performance to light with reduced computational consumption. First, lidar scans are used to capture multiple frames of local steel bar point clouds. Then, the clouds are stitched by Normal Distribution Transform (NDT) for preliminary matching and Iterative Closest Point (ICP) for fine-matching. The Graph-Based Optimization (g2o) method further refines the precision of the 3D registration. Afterwards, the 3D point clouds are projected into a 2D image. Finally, the locations of concrete vibration points and concrete casting points are discerned through point cloud and image processing technologies. Experiments demonstrate that the proposed automatic method outperforms ICP and NDT algorithms, reducing the mean square error (MSE) by 11.5% and 11.37%, respectively. The maximum discrepancies in identifying concrete vibration points and concrete casting points are 0.059 ± 0.031 m and 0.089 ± 0.0493 m, respectively, fulfilling the requirement for concrete vibration detection.

Adversarial sample attack method based on loss smoothing

Deep neural networks (DNNs) are vulnerable to adversarial examples.Although the existing momentum-based adversarial example generation method can achieve a close 100%white-box attack success rate, it is still not ideal when attacking other models, and the black- box attack success rate is low. To address this, an adversarial example attack method based on loss smoothing is proposed to improve the transferability of adversarial examples. In the iterative process of calculating the gradient at each step, the current gradient is not used directly, but the local average gradient is used to accumulate momentum, so as to suppress the local oscillation phenomenon on the loss function surface, thereby stabilizing the update direction and escaping the local extreme point. A large number of experimental results on the ImageNet dataset show that compared with the existing momentum-based method, the average black-box attack success rate of the proposed method in single model attack experiments is improved by 38.07%and 27.77%, and the average black-box attack success rate in integrated model attack experiments is improved by and.Rising32.50%and28.63%

Class agnostic and specific consistency learning for weakly-supervised point cloud semantic segmentation

This paper focuses on Weakly Supervised 3D Point Cloud Semantic Segmentation (WS3DSS), which involves annotating only a few points while leaving a large number of points unlabeled in the training sample. Existing methods roughly force point-to-point predictions across different augmented versions of inputs close to each other. While this paper introduces a carefully-designed approach for learning class agnostic and specific consistency, based on the teacher–student framework. The proposed class-agnostic consistency learning, to bring the features of student and teacher models closer together, enhances the model robustness by replacing the traditional point-to-point prediction consistency with the group-to-group consistency based on the perturbed local neighboring points’ features. Furthermore, to facilitate learning under class-wise supervisions, we propose a class-specific consistency learning method, pulling the feature of the unlabeled point towards its corresponding class-specific memory bank feature. Such a class of the unlabeled point is determined as the one with the highest probability predicted by the classifier. Extensive experimental results demonstrate that our proposed method surpasses the SOTA method SQN (Huet al., 2022) by 2.5% and 8.3% on S3DIS dataset, and 4.4% and 13.9% on ScanNetV2 dataset, on the 0.1% and 0.01% settings, respectively. Code is available at https://github.com/jasonwjw/CASC.