Search Results (14)

Wafer-level thin-film stress measurement is essential for reliable semiconductor fabrication. However, existing techniques present limitations in practice. Interferometry achieves high precision but at a cost that becomes prohibitive for large wafers. Meanwhile laser-scanning systems are more affordable but can only provide sparse data points. This work develops a phase-measuring deflectometry (PMD) system to bridge this gap and deliver a full-field solution for wafer stress mapping. The implementation addresses three key challenges in adapting PMD. First, screen positioning and orientation are refined using an inverse bundle-adjustment approach, which performs multi-parameter optimization without re-optimizing the camera model and simultaneously uses residuals to quantify screen deformation. Second, a backward-propagation ray-tracing framework benchmarks two iterative strategies to resolve the slope-height ambiguity which is a fundamental challenge in PMD caused by the absence of a fixed optical center on the source side. The reprojection constraint strategy is selected for its superior convergence precision. Third, this strategy is integrated with regional wavefront reconstruction based on Hermite interpolation to effectively eliminate edge artifacts. Experimental results demonstrate a peak-to-valley error in the reconstructed topography of 0.48 µm for a spherical mirror with a radius of 500 mm. The practical utility of the system is confirmed through curvature mapping of a 12-inch patterned wafer and further validated by stress measurements on an 8-inch bare wafer, which show less than 5% deviation from industry-standard instrumentation. These results validate the proposed PMD method as an accurate and cost-effective approach for production-scale thin-film stress inspection. Full article

Light Detection and Ranging systems serve as robust tools for creating three-dimensional representations of the Earth’s surface. These representations are known as point clouds. Point cloud scene segmentation is essential in a range of applications aimed at understanding the environment, such as infrastructure planning and monitoring. However, automating this process can result in notable challenges due to variable point density across scenes, ambiguous object shapes, and substantial class imbalances. Consequently, manual intervention remains prevalent in point classification, allowing researchers to address these complexities. In this work, we study the elements contributing to the automatic semantic segmentation process with deep learning, conducting empirical evaluations on a self-captured dataset by a hybrid airborne laser scanning sensor combined with two nadir cameras in RGB and near-infrared over a 247 km² terrain characterized by hilly topography, urban areas, and dense forest cover. Our findings emphasize the importance of employing appropriate training and inference strategies to achieve accurate classification of data points across all categories. The proposed methodology not only facilitates the segmentation of varying size point clouds but also yields a significant performance improvement compared to preceding methodologies, achieving a mIoU of 94.24% on our self-captured dataset. Full article

Prefabricated installation, a pivotal study in the realm of contemporary construction practices, delves into the utilization of prefabrication within mechanical, electrical, and plumbing (MEP) systems. Despite its ascending prominence, the domain grapples with ambiguities in application pathways, uncertain developmental trajectories, and the absence of a holistic technical paradigm. This research endeavors to bridge these gaps by conducting a thorough and multidimensional investigation into the current landscape of prefabricated MEP installation initiatives. This study meticulously dissects the paradigm from five critical vantage points: historical evolution, standards and regulations, life cycle analysis, technological applications, and corporate implementation strategies. At present, there is still a lack of standards and specifications specifically for the field of assembled MEP installation. The analysis reveals a trend towards intelligent and sustainable installation practices in prefabricated MEP projects. The research predominantly focuses on the design, production, and installation stages. Notably, building information modeling (BIM) emerges as the most prominent technology, followed by the Internet of Things (IoT) and 3D laser scanning, with extended reality (XR) technologies gaining traction. Large, state-owned construction firms are spearheading innovative applications in this realm. In summary, this paper provides an overview and outlook for the development direction and the application of cutting-edge technologies in prefabricated MEP installation projects, with the aim of supporting the industry’s advancement. Full article

The use of lasers in various precise material removal processes has emerged as a viable and efficient alternative to traditional mechanical methods. However, the laser ablation of materials is a complex, multi-parameter process where scanning paths need to be repeated multiple times. This repetition causes changes in the absorption and temperature distribution along the scanning path, thereby affecting the accuracy of the ablation. Therefore, it is crucial to thoroughly study these phenomena. This article presents an experimental and numerical study on the laser ablation of bronze (DIN: 1705) in a multi-track ablation process. Specifically, six consecutive passes using a ns laser at three different energy densities were conducted. After each pass, measurements of the ablation depth and pile-up height were taken at three distinct points along the track (start, middle, and end) to evaluate the efficiency and quality of the process. To gain a deeper understanding of the underlying physical mechanisms, a numerical simulation model based on the Finite Element Method (FEM) was developed. The effective absorptivity was defined through reverse engineering, and the material’s cooling rates were also estimated. This study’s findings provide significant insights into the influence of machining parameters on the ablation process and its progression with varying numbers of consecutive repetitions. A primarily linear correlation was deduced between the ablation depth, energy density, and number of repetitions, while the relationship with the pile-up height appeared to be more ambiguous and nonlinear. The estimated cooling rates ranged from 10⁶ to 10¹⁰ [K/s]. Additionally, a heat accumulation phenomenon and a gradual temperature increase resulting from consecutive laser scans were also observed. A good agreement between the simulation results and experiments for the ablation depths was observed. Full article

Tire defect detection, as an important application of automatic inspection techniques in the industrial field, remains a challenging task because of the diversity and complexity of defect types. Existing research mainly relies on X-ray images for the inspection of defects with clear characteristics. However, in actual production lines, the major threat to tire products comes from defects of low visual quality and ambiguous shape structures. Among them, bubbles, composing a major type of bulge-like defects, commonly exist yet are intrinsically difficult to detect in the manufacturing process. In this paper, we focused on the detection of more challenging defect types with low visibility on tire products. Unlike existing approaches, our method used laser scanning technology to establish a new three-dimensional (3D) dataset containing tire surface scans, which leads to a new detection framework for tire defects based on 3D point cloud analysis. Our method combined a novel 3D rendering strategy with the learning capacity of two-dimensional (2D) detection models. First, we extracted accurate depth distribution from raw point cloud data and converted it into a rendered 2D feature map to capture pixel-wise information about local surface orientation. Then, we applied a transformer-based detection pipeline to the rendered 2D images. Our method marks the first work on tire defect detection using 3D data and can effectively detect challenging defect types in X-ray-based methods. Extensive experimental results demonstrate that our method outperforms state-of-the-art approaches on 3D datasets in terms of detecting tire bubble defects according to six evaluation metrics. Specifically, our method achieved 35.6, 40.9, and 69.1 mAP on three proposed datasets, outperforming others based on bounding boxes or query vectors. Full article

The Proterozoic Athabasca Basin hosts a large number of high-grade, large-tonnage unconformity-related uranium (U) deposits, many of which are also enriched in rare earth elements (REE). The basin also contains hydrothermal REE mineralization unassociated with U. Previous studies postulated that U and REE were derived from either the basin or the basement; however, the exact source of the metals remains ambiguous. This study provides evidence of U- and REE-rich fluids throughout the Athabasca Basin through laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) mapping of barren sandstone distal to mineralized areas. The results indicate that elevated U and REE concentrations mainly occur in the matrix; there are strong positive correlations between U and REE, Th, P and Sr, and moderate positive correlations between U and Zr, Ba, Fe, Al, K and Ca, but the few spots with the highest U are unrelated to these elements. Quantitative evaluation of the element correlations, together with scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS) analysis, suggests that most of the elevated U and REE are hosted in aluminum phosphate sulfate (APS) minerals rather than apatite and monazite. As the APS minerals are of diagenetic-hydrothermal origin, the results testify to the presence of U- and REE-rich fluids within the Athabasca Basin. The elevated Th/U ratio (~10) and REE pattern (strong heavy rare earth element (HREE) depletion) are consistent with a model in which large amounts of U and REE (especially HREE) were leached from the sandstone within the Athabasca Basin and contributed to U and REE mineralization near the unconformity between the sedimentary rocks in the basin and underlying basement rocks. This study demonstrates that LA-ICP-MS mapping can be effectively used to evaluate microscale distribution of elements and their mobility in sedimentary rocks to address mineralization related problems. Full article

Free radical-mediated activation of inflammatory macrophages remains ambiguous with its limitation to study within biological systems. U-937 and HL-60 cell lines serve as a well-defined model system known to differentiate into either macrophages or dendritic cells in response to various chemical stimuli linked with reactive oxygen species (ROS) production. Our present work utilizes phorbol 12-myristate-13-acetate (PMA) as a stimulant, and factors such as concentration and incubation time were considered to achieve optimized differentiation conditions. ROS formation likely hydroxyl radical (HO^●) was confirmed by electron paramagnetic resonance (EPR) spectroscopy combined with confocal laser scanning microscopy (CLSM). In particular, U-937 cells were utilized further to identify proteins undergoing oxidation by ROS using anti-DMPO (5,5-dimethyl-1-pyrroline N-oxide) antibodies. Additionally, the expression pattern of NADPH Oxidase 4 (NOX4) in relation to induction with PMA was monitored to correlate the pattern of ROS generated. Utilizing macrophages as a model system, findings from the present study provide a valuable source for expanding the knowledge of differentiation and protein expression dynamics. Full article

Terrestrial laser scanning (TLS) is an important part of urban reconstruction and terrain surveying. In TLS applications, 4-point congruent set (4PCS) technology is widely used for the global registration of point clouds. However, TLS point clouds usually enjoy enormous data and uneven density. Obtaining the congruent set of tuples in a large point cloud scene can be challenging. To address this concern, we propose a registration method based on the voxel grid of the point cloud in this paper. First, we establish a voxel grid structure and index structure for the point cloud and eliminate uneven point cloud density. Then, based on the point cloud distribution in the voxel grid, keypoints are calculated to represent the entire point cloud. Fast query of voxel grids is used to restrict the selection of calculation points and filter out 4-point tuples on the same surface to reduce ambiguity in building registration. Finally, the voxel grid is used in our proposed approach to perform random queries of the array. Using different indoor and outdoor data to compare our proposed approach with other 4-point congruent set methods, according to the experimental results, in terms of registration efficiency, the proposed method is more than 50% higher than K4PCS and 78% higher than Super4PCS. Full article

Heritage documentation is implemented by digitally recording historical artifacts for the conservation and protection of these cultural heritage objects. As efficient spatial data acquisition tools, laser scanners have been widely used to collect highly accurate three-dimensional (3D) point clouds without damaging the original structure and the environment. To ensure the integrity and quality of the collected data, field inspection (i.e., on-spot checking the data quality) should be carried out to determine the need for additional measurements (i.e., extra laser scanning for areas with quality issues such as data missing and quality degradation). To facilitate inspection of all collected point clouds, especially checking the quality issues in overlaps between adjacent scans, all scans should be registered together. Thus, a point cloud registration method that is able to register scans fast and robustly is required. To fulfill the aim, this study proposes an efficient probabilistic registration for free-form cultural heritage objects by integrating the proposed principal direction descriptor and curve constraints. We developed a novel shape descriptor based on a local frame of principal directions. Within the frame, its density and distance feature images were generated to describe the shape of the local surface. We then embedded the descriptor into a probabilistic framework to reject ambiguous matches. Spatial curves were integrated as constraints to delimit the solution space. Finally, a multi-view registration was used to refine the position and orientation of each scan for the field inspection. Comprehensive experiments show that the proposed method was able to perform well in terms of rotation error, translation error, robustness, and runtime and outperformed some commonly used approaches. Full article

Registration of point clouds is a central problem in many mapping and monitoring applications, such as outdoor and indoor mapping, high-speed railway track inspection, heritage documentation, building information modeling, and others. However, ensuring the global consistency of the registration is still a challenging task when there are multiple point clouds because the different scans should be transformed into a common coordinate frame. The aim of this paper is the registration of multiple terrestrial laser scanner point clouds. We present a plane-based matching algorithm to find plane-to-plane correspondences using a new parametrization based on complex numbers. The multiplication of complex numbers is based on analysis of the quadrants to avoid the ambiguity in the calculation of the rotation angle formed between normal vectors of adjacent planes. As a matching step may contain several matrix operations, our strategy is applied to reduce the number of mathematical operations. We also design a novel method for global refinement of terrestrial laser scanner data based on plane-to-plane correspondences. The rotation parameters are globally refined using operations of quaternion multiplication, while the translation parameters are refined using the parameters of planes. The global refinement is done non-iteratively. The experimental results show that the proposed plane-based matching algorithm efficiently finds plane correspondences in partial overlapping scans providing approximate values for the global registration, and indicate that an accuracy better than 8 cm can be achieved by using our global fine plane-to-plane registration method. Full article

Airborne laser scanning (ALS) has proven to be a strong basis for 3D building reconstruction. While ALS data allows for a highly automated processing workflow, a major drawback is often in the point spacing. As a consequence, the precision of roof plane and ridge line parameters is usually significantly better than the precision of gutter lines. To cope with this problem, the paper presents an approach for geometric refinement of building models reconstructed from ALS data using monoscopic aerial images. The core idea of the proposed modeling method is to obtain refined roof edges by intersecting roof planes accurately and reliably extracted from 3D point clouds with viewing planes assigned with building edges detected in a high resolution aerial image. In order to minimize ambiguities that may arise during the integration of modeling cues, the ALS data is used as the master providing initial information about building shape and topology. We evaluate the performance of our algorithm by comparing the results of 3D reconstruction executed using only laser scanning data and reconstruction enhanced by image information. The assessment performed within a framework of the International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark shows an increase in the final quality indicator up to 8.7%. Full article

Localization is an essential issue for robot navigation, allowing the robot to perform tasks autonomously. However, in environments with laser scan ambiguity, such as long corridors, the conventional SLAM (simultaneous localization and mapping) algorithms exploiting a laser scanner may not estimate the robot pose robustly. To resolve this problem, we propose a novel localization approach based on a hybrid method incorporating a 2D laser scanner and a monocular camera in the framework of a graph structure-based SLAM. 3D coordinates of image feature points are acquired through the hybrid method, with the assumption that the wall is normal to the ground and vertically flat. However, this assumption can be relieved, because the subsequent feature matching process rejects the outliers on an inclined or non-flat wall. Through graph optimization with constraints generated by the hybrid method, the final robot pose is estimated. To verify the effectiveness of the proposed method, real experiments were conducted in an indoor environment with a long corridor. The experimental results were compared with those of the conventional GMappingapproach. The results demonstrate that it is possible to localize the robot in environments with laser scan ambiguity in real time, and the performance of the proposed method is superior to that of the conventional approach. Full article

We propose a generative framework for 3D human pose estimation that is able to operate on both individual point sets and sequential depth data. We formulate human pose estimation as a point set registration problem, where we propose three new approaches to address several major technical challenges in this research. First, we integrate two registration techniques that have a complementary nature to cope with non-rigid and articulated deformations of the human body under a variety of poses. This unique combination allows us to handle point sets of complex body motion and large pose variation without any initial conditions, as required by most existing approaches. Second, we introduce an efficient pose tracking strategy to deal with sequential depth data, where the major challenge is the incomplete data due to self-occlusions and view changes. We introduce a visible point extraction method to initialize a new template for the current frame from the previous frame, which effectively reduces the ambiguity and uncertainty during registration. Third, to support robust and stable pose tracking, we develop a segment volume validation technique to detect tracking failures and to re-initialize pose registration if needed. The experimental results on both benchmark 3D laser scan and depth datasets demonstrate the effectiveness of the proposed framework when compared with state-of-the-art algorithms. Full article

An automated laser rangefinding instrument was developed to characterize overstorey and understorey vegetation dynamics over time. Design criteria were based on information needs within the statewide forest monitoring program in Victoria, Australia. The ground-based monitoring instrument captures the key vegetation structural information needed to overcome ambiguity in the estimation of forest Leaf Area Index (LAI) from satellite sensors. The scanning lidar instrument was developed primarily from low cost, commercially accessible components. While the 635 nm wavelength lidar is not ideally suited to vegetation studies, there was an acceptable trade-off between cost and performance. Tests demonstrated reliable range estimates to live foliage up to a distance of 60 m during night-time operation. Given the instrument’s scan angle of 57.5 degrees zenith, the instrument is an effective tool for monitoring LAI in forest canopies up to a height of 30 m. An 18 month field trial of three co-located instruments showed consistent seasonal trends and mean LAI of between 1.32 to 1.56 and a temporal LAI variation of 8 to 17% relative to the mean. Full article