Scanning Device Research Articles

INCREASING THE RELIABILITY OF DEFECT DETECTION THREAD, FIBRE AND YARN

A method and algorithm for increasing the reliability of decision-making in the problem of detecting defects in extended products – threads, wires, cables, fibres, yarn, etc., based on the following principles are proposed: using several statistical criteria at the same time rather than one, developing a rule for coor-dinated decision-making based on the results of testing using several statistical criteria, implementing the testing procedure in the online mode as data are received and checked. The algorithm is a sequential test of the hypothesis about the violation of stationarity by the average level and variability of the data flow from the scanning device using simultaneously used Student, Wilcoxon, Fisher and Ansari – Bradley tests, taking into account the information already accumulated about the flow at the previous stages of testing.

Evaluation of Two-Dimensional DBH Estimation Algorithms Using TLS

Terrestrial laser scanning (TLS) has become a vital tool in forestry for accurately measuring tree parameters, such as diameter at breast height (DBH). However, its application in Mexican forests remains underexplored. This study evaluates the performance of five two-dimensional DBH estimation algorithms (Nelder–Mead, least squares, Hough transform, RANSAC, and convex hull) within a temperate Mexican forest and explores their broader applicability across diverse ecosystems, using published point cloud data from various scanning devices. Results indicate that algorithm accuracy is influenced by local factors like point cloud density, occlusion, vegetation, and tree structure. In the Mexican study area, the Nelder–Mead algorithm achieved the highest accuracy (R² = 0.98, RMSE = 1.59 cm, MAPE = 6.12%), closely followed by least squares (R² = 0.98, RMSE = 1.67 cm, MAPE = 6.42%), with different outcomes in other sites. These findings advance DBH estimation methods by highlighting the importance of tailored algorithm selection and environmental considerations, thereby contributing to more accurate and efficient forest management across various landscapes.

APPLICATION OF SIZE SPECIFIC DOSE ESTIMATE (SSDE) AS A LOCAL DIAGNOSTIC REFERENCE LEVEL IN HEAD CT SCAN EXAMINATION AT INDRIATI HOSPITAL SOLOBARU, DR. MOEWARDI HOSPITAL SURAKARTA, SLEMAN HOSPITAL, AND NATIONAL BRAIN CENTER HOSPITAL JAKARTA

CT scan is a modality that produces a higher radiation dose compared to other modalities in radiodiagnostic installations. The radiation dose received by patients from examinations with this CT scan device is expressed in the volume CT Dose Index (CTDIvol) and Dose Length Product (DLP), which are dose indicators from the Indonesian Diagnostic Reference Level (DRL) value regulated in the Decree of the Head of the Nuclear Energy Regulatory Agency (BAPETEN) nationally. However, the CTDIvol value is only an indicator of the output dose from the CT scan, and the DLP value is an indicator of the total dose during the examination, which is influenced by the length of the scan. Both do not represent the specific dose received by the patient because the patient's dose depends not only on the output dose but also on the patient's characteristics. The patient's dose, which includes the output dose and patient characteristics, is called the Size-Specific Dose Estimate (SSDE), which is used to estimate the dose received by the patient by including a correction factor depending on the patient's dimensions. This study aims to calculate the SSDE value in a CT Head examination with a standard protocol using the same type of device, namely a 128-slice CT scan in several hospitals. The values of CTDIvol and DLP patients are obtained from the dose report displayed on the CT scan monitor screen in the IndoseCT software. The patient samples studied were patients undergoing CT Head examinations from March 2023 to February 2024, with 100 patients in each hospital. The results of this study obtained local DRL values based on SSDE calculated at Indriati Hospital, Moewardi Hospital, Sleman Hospital, and the National Brain Center Hospital and then compared them with the national TPD values. Based on the SSDE value from Indriati Hospital, the TPD value was 30.26 mGy, Sleman Hospital was 39.5 mGy, Dr. Moewardi Hospital was 49.3 mGy, and RSPON was 51.7 mGy. The TPD value of CT Head examinations without contrast from each hospital was still below the national TPD value of 60 mGy. Keywords: CT scan, diagnostic reference level (DRL), local DRL, national DRL, Size-Specific Dose Estimate (SSDE), IndoseCT.

Image-driven prediction system: Automatic extraction of aggregate gradation of pavement core samples integrating deep learning and interactive image processing framework

Aggregate gradation plays a crucial role in determining the asphalt pavement performance, necessitating assessment post-construction. Additionally, efficient gradation measurement is vital for the design and construction of reclaimed pavement. This study proposes a methodology for predicting gradation based on imagery. Initially, a scanning device is devised to capture unfolded side surface images of pavement core samples. Subsequently, an interactive software named PyImageJ is developed for image post-processing and acquiring aggregate feature information. A morphological model is then introduced to determine the mass ratios among various grading levels of coarse aggregates with nominal particle sizes greater than 2.36 mm. Finally, sequence prediction models are established to forecast the mass ratios among different grading levels of fine aggregates with nominal particle sizes smaller than 2.36 mm. Simple conversion of the mass ratios can yield the gradation. Experimental results indicate that the Long Short-Term Memory (LSTM) network offers more accurate predictions for the mass ratios of fine aggregates in stone mastic asphalt (SMA-13), with an R-squared value of 0.8985. Conversely, one-dimensional convolutional neural networks (1D CNN) are better suited for predicting the fine aggregate mass ratios in asphalt concrete (AC-20 and AC-25), with R-squared values of 0.9010 and 0.8824, respectively. Visualization results demonstrate that the predicted complete gradation closely aligns with the actual values (R-squared > 0.99), opening a new avenue for evaluating pavement quality.

Automated Attendance System with GPS-Integrated with Enhanced Navigation Tracking

With the rise of automation and smart technologies in educational institutions, efficient and reliable attendancetracking methods have become increasingly essential. Traditional attendance systems, which often involve manual or barcode scanning methods, are time-consuming and prone to inaccuracies. This research paper presents an innovative approach to student attendance tracking, utilising GPS-integrated barcode identification with wireless data transmission. The proposed system is designed to improve attendance accuracy and streamline student identification without the need for students to physically interact with scanning devices. By integrating GPS and barcode systems into the door panel, this attendance solution detects student presence in classrooms, records attendance, and transmits data in real time.

Introduction to the Investigation of reproduction of the real geometry of UBM panels

The article concisely describes UBM technology (Ultimate Building Machine), that can be used as a solution for buildings and roof structures. The structure of this technology is made of double corrugated thin-walled steel profiles manufactured on the construction site by a self-contained UBM manufacturing factory on wheels. These panels serve as both the building envelope and the structural system. The specificity of the construction poses many design problems, especially the determination of the strength parameters and stiffness of the double-corrugated panels from which the structure is made. The article presents the results of spatial scanning tests of double-corrugated steel sheets, which were carried out using commonly available 3D scanning devices: Leica 3D Disto and MagiScan app. Additionally, results of numerical analyses performed on scanned samples and a comparison of these results with preliminary laboratory tests have been presented in the article. The purpose of scanning was to obtain an accurate and real geometry of the UBM panels, to implement it into a numerical software, and then to perform numerical analyses. Commonly available 3D scanning devices were used because using advanced 3D scanners is not popular nowadays for economic reasons, and hand-built geometric models pose a lot of problems and are not accurate enough. Promising results were obtained, which form the basis for further research.

Filling the Holes on 3D Heritage Object Surface Based on Automatic Segmentation Algorithm

ABSTRACTReconstructing and processing 3D objects are activities in the research field of computer graphics, image processing and computer vision. The 3D objects are processed based on geometric modelling (a branch of applied mathematics and computational geometry) or machine learning algorithms based on image processing. The computation of geometrical objects includes processing the curves and surfaces, subdivision, simplification, meshing, holes filling or reconstructing the 3D surface's objects on both point cloud data and triangular mesh. While the machine learning methods are developed using deep learning models. With the support of 3D laser scan devices and LiDAR techniques, the obtained dataset is close to the original shape of the real objects. Besides, photography and its application based on modern techniques in recent years help us collect data and process the 3D models more precisely. This article proposes a new method for filling holes on the 3D object's surface based on automatic segmentation. Instead of filling the hole directly as the existing methods, we now subdivide the hole before filling it. The hole is first determined and segmented automatically based on the computation of its local curvature. It is then filled on each part of the hole to match its local curvature shape. The method can work on both 3D point cloud surfaces and triangular mesh surfaces. Compared to the state‐of‐the‐art (SOTA) methods, our proposed method obtained higher accuracy of the reconstructed 3D objects.

Pulp Vitality Diagnosis by Means of an Optical Pulp Scanning Device.

This study aimed to evaluate the diagnostic accuracy of an Optical Pulp Scanning (OPS) device. A total of 421 teeth from 107 emergency patients were investigated. Pulp vitality was assessed using conventional diagnostic methods (ConvDia), cold pulp testing (CPT), and a final diagnosis (FinDia) based on the pulp chamber contents (PCCs) of teeth requiring endodontic treatment (EmeTe). Following ConvDia, OPS screening was performed, endodontic access was prepared, and the FinDia was established. The specificity, sensitivity, as well as positive and negative predictive values of ConvDia, CPT, and FinDia were calculated against the OPS results. The null hypothesis stated that specificity and sensitivity would be equal to or lower than 90% and 80%, respectively. OPS sensitivity was 66.2% and 72.2% (EmeTe) for ConvDia, 66.2% and 69.8% (EmeTe) for CPT, and 76.9% for FinDia. Specificity was 26.4% and 25.5% (EmeTe) for ConvDia, 26.9% and 21.7% (EmeTe) for CPT, and 31.5% for FinDia. These results suggest that, at its current stage of development, the OPS device does not provide an acceptable level of diagnostic accuracy, particularly in vital teeth.

Detection of artificial spots in fundus images using modified U-Net based semantic segmentation

Today, deep learning algorithms are playing a very crucial role in the initial stage diagnosis of several fundus diseases like glaucoma, hypertension, and diabetic retinopathy. Lots of research is going on in this area now-a-days. During the acquisition of fundus images some artificial spots (e.g. because of the device itself, dust particles in the surroundings) have been added in captured images. In this paper, artificial spots in the fundus images that are generated due to the non-standardized conditions of scanning devices, are detected with the help of a newly proposed modified UNet (mU-Net) semantic segmentation model. Initially, preprocessing methods such as Gaussian blur, thresholding, and Hough transform have been used to create artificial spots. Now these preprocessed images have been used for training the proposed model. To make the proposed model more effective, the following modifications like, Regularization techniques (early stopping, greater weight decay, and Adam optimizer), decay learning rate scheduler, categorical cross-entropy loss function, and a significant number of filters have been modified in simple U-Net model. Apart from these mentioned modifications in the base U-Net model, a feature injecting module (FIM) has been added between the expansion and the contraction section of the simple U-Net model. FIM adds the features of the input image at the time of up-sampling. The addition of FIM to a simple U-Net model improves the detection of artificial spots and enhances the performance of the model. The mU-Net has been compared with other models, namely simple U-Net, V-Net, UNet++, ResUnet-a, WideU-Net, and Swin-Unet. The Friedman test that has been conducted on IOU, DICE, MAE, PSNR, and SSIM scores, found that mU-Net balances evaluation metrics well. It appears that the nonparametric Friedman test will improve reproducibility by demonstrating statistical significance. The IOU, DICE, MAE, PSNR, and SSIM scores of the proposed model indicate superior performance as compared to other models.

Photonic lantern TIRF microscopy for highly efficient, uniform, artifact-free imaging

We report a method for generating uniform, artifact-free total internal reflection fluorescence (TIRF) excitation via a photonic lantern. Our tapered waveguide, consisting of a multimode input and nine few-mode outputs, enables single-shot TIRF illumination from nine azimuthal directions simultaneously without the introduction of nonstationary devices. Utilizing the photonic lantern for multi-beam excitation provides a low-loss mechanism that supports a wide range of light sources, including high-coherence lasers and various wavelengths in the visible spectrum. Our excitation system also allows tuning of the TIRF penetration depth. The high-quality excitation produced by photonic lantern TIRF (PL-TIRF) enables unbiased imaging across the entire illumination field-of-view. The simplicity and robustness of our technique provides advantages over other TIRF approaches, which often have complicated setups with scanning devices or other impracticalities. In this paper we discuss the lantern design process, characterize its performance, and demonstrate flat-field super-resolution imaging and shadowless live-cell imaging using PL-TIRF.

A novel deep domain adaptation method based on three-way decisions for identifying autistic patients

Combining machine learning with functional magnetic resonance imaging (fMRI) technology to build an effective autism recognition model has become a research hotspot. However, the performance of existing machine learning models is highly dependent on large-scale labelled data, and the cost of fMRI data acquisition and labelling limits the application of models in autism recognition. Although the availability of multisite data increases the sample size, the resolution, image contrast, and noise level of fMRI data acquired at different sites vary considerably due to the different scanning devices or scanning protocols used. The heterogeneity of fMRI data between sites leads to limited generalisation of the trained models. To tackle this problem, we propose a deep domain adaptation approach based on three-way decisions. First, we introduce a pseudolabel optimisation method based on three-way decisions, which fully considers the structural information of the target domain and makes secondary decisions on the uncertain objects in the target domain, thus improving the accuracy of the pseudolabel in the target domain. Then, we construct a new autism recognition network using a stacked autoencoder and combine the proposed pseudolabel optimisation model to adapt the domain shift. Finally, the self-training strategy is used to retrain the network after domain adaptation to improve the generalisability of the model to the target domain. Experimental results on several cross-domain autism recognition tasks show the effectiveness of the proposed method.

Invasion Characteristics of Marginal Water under the Control of High-Permeability Zones and Its Influence on the Development of Vertical Heterogeneous Gas Reservoirs

In-depth understanding of the gas–water seepage law caused by different degrees of gas layer perforation and varying gas production rates is key to determining a reasonable development technology policy for vertical heterogeneous edge-water gas reservoirs. Based on core physical data from the entire section of the X2 well, a large-scale high-pressure positive-rhythm profile model that takes into account the influence of “discontinuous interlayer” was innovatively established. The water intrusion process of the gas layer profile under different gas production rates and degrees of gas layer perforation was simulated using an electrical resistivity scanning device. The experimental model has an area of 3000 cm2, with a maximum pressure of 70 MPa and a maximum temperature resistance of 150 °C. It includes 456 evenly distributed fluid saturation test points to accurately monitor the gas–water distribution, addressing the issues of small bearing pressure and insufficient saturation monitoring points found in other large-scale models. The experimental results show that, in heterogeneous reservoirs, the high-permeability zone controls the invasion path of edge water, which is the main reason for the uneven invasion of edge water. For the positive-rhythm profile of the F layer, a higher gas production rate (1000 mL/min) shortens the water-free gas recovery period of the gas well and reduces the recovery rate. Perforating the upper two-thirds of the layer can inhibit edge-water breakthrough, prolong the water-free gas recovery period of the gas well, enable the gas–water interface to advance more uniformly, and enhance the recovery degree. The results of this study greatly enhance our understanding of the water invasion characteristics of positive-rhythm reservoirs under the influence of different gas production rates and varying degrees of gas layer perforation.

Automated recognition and rebar dimensional assessment of prefabricated bridge components from low-cost 3D laser scanner

Dimension quality inspections for rebar spacing and length of prefabricated bridge components are critical for subsequent efficient assembly on-site. Currently, the traditional manual inspection method is time-consuming, labor-intensive, and error-prone. The existing commercial 3D LiDAR-based methods are difficult in making balances between inspection cost, efficiency, and accuracy. Thus, the automated recognition and segmentation method of 3D point clouds acquired by a self-developed low-cost LiDAR is proposed to evaluate the rebar spacing and length of bridge prefabricated components. Due to the high-cost commercial 3D laser scanner, a spinning 2D LiDAR-based low-cost 3D laser scanner device and geometric calibration model were developed to acquire the 3D spatial point cloud. Then, two-stage automated point cloud segmentation framework is proposed with coarse extraction of the point cloud in the interest region and fine recognition of point cloud using machine learning methods, including plane segmentation and circle fitting employing random sample consensus (RANSAC) algorithm, and rebars segmentation utilizing Gaussian mixture model (GMM) and density-based spatial clustering of applications with noise (DBSCAN) algorithm. The proposed system was evaluated in two prefabricated concrete columns and shows the potential for improving the quality inspection efficiency and accuracy.

Three-dimensional reconstruction of a shooting crime scene

The crime scene is technically the place where an illegal act took place. The investigation first starts at the crime scene. Documentation of the crime scene is of great importance in order to make correct decisions in the investigation and prosecution stages, With the development of technology; the use of different devices and different technologies in crime scene investigation and crime scene documentation has created a new working area for many forensic scientists and law enforcement officers. Taking a general view of the crime scene without contamination, low margin of error in measurements can be shown as the main reasons for the use of laser scanning devices at the crime scene. A firearm crime scene was staged using different evidence. In the staged crime scene, a laser scanning device was also used along with the use of photography, sketching and note-taking methods from traditional documentation methods. In this study, the adequacy of the traditional methods alone, the adequacy of the laser scanning device alone, and the adequacy of the two methods together for reconstruction were discussed. The aim of this study is to determine the contributions and limitations of documenting the crime scene with a laser scanning device in combination with traditional documentation methods to the three-dimensional reconstruction of the crime scene.

A method for accurate extraction of three-dimensional point cloud feature data in loading test of container ships

The accuracy of container slot dimensions directly affects the efficiency and safety of container loading and unloading. However, traditional methods of container loading tests need to be improved for their long cycles, high costs, and low production efficiency. With the application of three-dimensional laser scanning devices in ship manufacturing, the use of simulated container loading test methods based on point cloud becomes a breakthrough in key technology for container ship construction. This paper presents a multi-objective segmentation method of cargo hold feature point cloud for container ship simulated loading tests. Data preprocessing algorithms are applied to remove noise and pseudo-image, and then a Random Sample Consensus algorithm is used to remove bulkhead and cargo bottom point clouds. Based on the density-based spatial clustering of applications with noise algorithm and the region growing algorithm, an algorithm for multiple segmentation of guide rail and bottom cone point clouds is devised, and a four-plane segmentation of guide rail point clouds is realized. Finally, a method for three-dimensional point cloud data flatness assessment and a Hausdorff distance based method for calculating the guide rail lateral and longitudinal spacing are proposed. These methods are validated through the loading test of a 16,000 TEU container ship.

IPUNet: Iterative Cross Field Guided Point Cloud Upsampling.

Point clouds acquired by 3D scanning devices are often sparse, noisy, and non-uniform, causing a loss of geometric features. To facilitate the usability of point clouds in downstream applications, given such input, we present a learning-based point upsampling method, i.e., iPUNet, which generates dense and uniform points at arbitrary ratios and better captures sharp features. To generate feature-aware points, we introduce cross fields that are aligned to sharp geometric features by self-supervision to guide point generation. Given cross field defined frames, we enable arbitrary ratio upsampling by learning at each input point a local parameterized surface. The learned surface consumes the neighboring points and 2D tangent plane coordinates as input, and maps onto a continuous surface in 3D where arbitrary ratios of output points can be sampled. To solve the non-uniformity of input points, on top of the cross field guided upsampling, we further introduce an iterative strategy that refines the point distribution by moving sparse points onto the desired continuous 3D surface in each iteration. Within only a few iterations, the sparse points are evenly distributed and their corresponding dense samples are more uniform and better capture geometric features. Through extensive evaluations on diverse scans of objects and scenes, we demonstrate that iPUNet is robust to handle noisy and non-uniformly distributed inputs, and outperforms state-of-the-art point cloud upsampling methods.

Automated Pain Spots Recognition Algorithm Provided by a Web Service-Based Platform: Instrument Validation Study.

Understanding the causes and mechanisms underlying musculoskeletal pain is crucial for developing effective treatments and improving patient outcomes. Self-report measures, such as the Pain Drawing Scale, involve individuals rating their level of pain on a scale. In this technique, individuals color the area where they experience pain, and the resulting picture is rated based on the depicted pain intensity. Analyzing pain drawings (PDs) typically involves measuring the size of the pain region. There are several studies focusing on assessing the clinical use of PDs, and now, with the introduction of digital PDs, the usability and reliability of these platforms need validation. Comparative studies between traditional and digital PDs have shown good agreement and reliability. The evolution of PD acquisition over the last 2 decades mirrors the commercialization of digital technologies. However, the pen-on-paper approach seems to be more accepted by patients, but there is currently no standardized method for scanning PDs. The objective of this study was to evaluate the accuracy of PD analysis performed by a web platform using various digital scanners. The primary goal was to demonstrate that simple and affordable mobile devices can be used to acquire PDs without losing important information. Two sets of PDs were generated: one with the addition of 216 colored circles and another composed of various red shapes distributed randomly on a frontal view body chart of an adult male. These drawings were then printed in color on A4 sheets, including QR codes at the corners in order to allow automatic alignment, and subsequently scanned using different devices and apps. The scanners used were flatbed scanners of different sizes and prices (professional, portable flatbed, and home printer or scanner), smartphones with varying price ranges, and 6 virtual scanner apps. The acquisitions were made under normal light conditions by the same operator. High-saturation colors, such as red, cyan, magenta, and yellow, were accurately identified by all devices. The percentage error for small, medium, and large pain spots was consistently below 20% for all devices, with smaller values associated with larger areas. In addition, a significant negative correlation was observed between the percentage of error and spot size (R=-0.237; P=.04). The proposed platform proved to be robust and reliable for acquiring paper PDs via a wide range of scanning devices. This study demonstrates that a web platform can accurately analyze PDs acquired through various digital scanners. The findings support the use of simple and cost-effective mobile devices for PD acquisition without compromising the quality of data. Standardizing the scanning process using the proposed platform can contribute to more efficient and consistent PD analysis in clinical and research settings.

On-site micro-XRF mapping of enameled porcelain paintings and sculpture. First demonstration

We present the first study of painted enameled ceramics with a mobile micro-XRF (MA-XRF) scanning device at the Cité de la Ceramique, Sèvres on 18th to 20th century soft- and hard-paste palettes, 2D or 3D, Castel Durante 16th century majolica, two 19th century porcelain reproducing paintings by W. Bouguereau and J.-E. Aubert and a set combining a porcelain figurine and flowers mounted with gilded bronze. Major elements (Si, Ca, K, Pb for enamels and porcelain body, Cu, Zn, Au, Hg for fire gildings and gilded bronze) and many minor elements, coloring agents (Fe, Co, Mn, Cu, Cr, Ni, Sb, U, Au, Pt, Y, Ta), opacifier (As, Sn, Zn) or impurities (Ba, Ti, Bi, Rb, Sr, Zr, Y, Ta, Ga, Cl) are identified and mapped. The comparison of images obtained for different transitions of the same element provides stratigraphic information. Restorations and damage are highlighted.

RE-PU: A Self-Supervised Arbitrary-Scale Point Cloud Upsampling Method Based on Reconstruction

The point clouds obtained directly from three-dimensional scanning devices are often sparse and noisy. Therefore, point cloud upsampling plays an increasingly crucial role in fields such as point cloud reconstruction and rendering. However, point cloud upsampling methods are primarily supervised and fixed-rate, which restricts their applicability in various scenarios. In this paper, we propose a novel point cloud upsampling method, named RE-PU, which is based on the point cloud reconstruction and achieves self-supervised upsampling at arbitrary rates. The proposed method consists of two main stages: the first stage is to train a network to reconstruct the original point cloud from a prior distribution, and the second stage is to upsample the point cloud data by increasing the number of sampled points on the prior distribution with the trained model. The experimental results demonstrate that the proposed method can achieve comparable outcomes to supervised methods in terms of both visual quality and quantitative metrics.

A new approach for discriminating spatially acquired SERS spectra using antiretroviral drug emtricitabine as a test sample

AbstractIn this study, an antiretroviral drug emtricitabine (also known as FTC or Emtriva) was detected and quantified down to 40 ng/ml by using surface‐enhanced Raman spectroscopy (SERS). Its aqueous standards were tested with two types of silver nanoparticles (colloidal and dendritic) using a specially developed aluminum well plate. The SERS spectra were acquired using a Raman scanning device with 30‐μm spatial resolution. The spectral data were analyzed using a new approach for the discrimination of the spatially acquired spectra based on the Quality index (Qi). After the Qi is calculated, the spectral data are sorted based on the Qi. This is followed by selecting only the spectra with a high Qi index and comparing the average of all the spatially acquired spectra. This results in an improvement in the spectral signal‐to‐noise and higher analytical sensitivity of the built calibration curves.