Scanning Device Research Articles

Sistem Informasi Pengolahan Data Absensi dan Cuti Karyawan Berbasis WEB Studi Kasus Pada PT. Intinusa Selareksa

Maintaining and improving employee productivity is one of the efforts that can be done to enhance the company's success. To ensure that the quality of employee productivity is maintained, the company needs an information system that can manage employees and provide services to all employees. The problems faced by PT. Intinusa Selareksa includes the current attendance system that uses a barcode scanner. The attendance system has several weaknesses such as damage to the barcode scanner device, making it difficult to read the barcode code on employee identification cards, and many cases of lost or misplaced employee identification cards. Another problem faced is the non-computerized managerial activities such as the leave, permit, and overtime application process. This causes difficulties for the HRD department to provide information quickly and accurately because the report-making process is still manual. This research aims to obtain a system design result and develop a web-based attendance and leave data processing information system for employees. The system development method used in this research refers to the waterfall method. The stages of this research include analysis, design, implementation, and system testing. The system design uses Unified Modelling Language (UML). A website-based information system can present information related to employee administration management, specifically aimed at improving services in the HRD department

A Low-Cost 3-D Imaging Device Using 2-D LiDAR and Reflectors

Laser techniques are widely used to perform topographic scenario surveys. However, in the absence of calibration in the Two-Dimensional (2-D)-based low-cost Three-Dimensional (3-D) scanning devices, a nonnegligible distortion between the ground truth and the reconstructed point cloud exists because of the imperfect structure of the 3-D scanning devices. Meanwhile, the real-time imaging performance is poor and insufficient to capture the shape of moving targets. Therefore, a simple but efficient 3-D scanning structure is developed to decrease the distortion. For real-time processing, the backward emitted laser beams are reflected by the reflectors and the then the reflected beams are available to detect the objects in front of the LiDAR. In the direction where objects of interest exist, the scanning frequency can be multiplied. Through a careful theoretical analysis, the spatial relationship between the points of the 2-D LiDAR, the placement of reflectors, and the 3-D objects is deduced. A solid experiment is performed in which several 3-D scanning devices are compared. It verifies the superiority of the proposed device in reducing the distortion and feasibility to alleviate the real-time processing problem.

Time Coding-Based Single-Photon-Counting Lidar for Obtaining Spatial Location

This paper proposes a single-photon-counting lidar based on time coding that can obtain the target’s spatial location and measure the distance and azimuth angle in real time without needing a scanning device. Multiple optical fibers were used to introduce laser echo photons into a single-pixel single-photon detector. According to the deviation in the detection time of the echo photons passing through different optical fibers, multiple distances can be obtained simultaneously. Combining the measured distances with the fiber spacing allows the calculation of the distance, azimuth angle, and spatial coordinates of the target. This lidar has the advantages of high photon detection efficiency, short signal acquisition time, and low cost compared to array detectors.

The impact of software updates on accuracy of intraoral scanners

BackgroundDigital workflow is showing an increasing tendency in everyday dentistry. Accuracy is essential during digital dental workflows for all indication areas. The present study aimed to evaluate the effect of software updates on the accuracy of intraoral scanner (IOS) devices.Methods3Shape Trios 3 Pod with software versions 18.1.2. (TRI3_1) and 20.1.2. (TRI3_2); 3Shape Trios 4 Move, version 19.2.2. (TRI4_1); and 3Shape Trios 4 Pod, version 20.1.1. (TRI4_2) were used to take direct optical impressions from a polymethyl methacrylate (PMMA) full arch reference model with prepared teeth (FDI 11,14,17 for crowns and FDI 26 for onlay) and an edentulous region (between FDI 14 and 17). The scanners were used eight times; STL files were imported into Geomagic Control X for accuracy assessment by comparing them to a reference data set created by an industrial high-precision scanner (AICON SmartScan-3D C5). The average deviation of the surface points was calculated in three locations: across a full arch (Parameter 1), the region of a four-unit bridge (Parameter 2), and a single prepared abutment (Parameter 3).ResultsIn parameter 1 and 2, the newest model with the latest software (TRI4_2) reached the highest accuracy (31.06 ± 5.24 µm and 21.69 ± 7.50 µm). In parameter 3, an older generation scanner running legacy software produced the highest accuracy: TRI4_1, 11.75 ± 0.35 µm.ConclusionAppropriate software updates can significantly increase the trueness and precision of intraoral scanner devices. With updated software, the older generation can match the accuracy level of latest equipment.

Genetic and Molecular Scanning, Manipulation and Modification Device

Genetic and Molecular Scanning, Manipulation and Modification Device

Use of scanning devices for object 3D reconstruction by photogrammetry and visualization in virtual reality

This article aims to compare two different scanning devices (360 camera and digital single lens reflex (DSLR) camera) and their properties in the three-dimensional (3D) reconstruction of the object by the photogrammetry method. The article first describes the various stages of the process of 3D modeling and reconstruction of the object. A point cloud generated to the 3D model of the object, including textures, is created in the following steps. The scanning devices are compared under the same conditions and time from capturing the image of a real object to its 3D reconstruction. The attributes of the scanned image of the recon-structed 3D model, which is a mandarin tree in a citrus greenhouse in a daylight environment, are also compared. Both created models are also compared visually. That visual comparison reveals the possibilities in the application of both scanning devices can be found in the process of 3D reconstruction of the object by photogrammetry. The results of this research can be applied in the field of 3D modeling of a real object using 3D models in virtual reality, 3D printing, 3D visualization, image analysis, and 3D online presentation.

Handwritten Digit Prediction Using CNN

Abstract: For many years, numerous methods have been used in extensive research on handwriting recognition. The capacity to create an effective algorithm that can recognise handwritten digits given by users via scanner, tablet, and other digital devices is at the core of the issue. The automatic processing of bank checks, postal addresses, and other sorts of data already makes substantial use of handwritten digit recognition. Computational intelligence methods like artificial neural networks used by several current systems. CNN and the MNIST data set will be used to complete this. Handwriting Recognition, Deep Learning, CNN, and Computational Intelligence are key terms.

Evaluating 3D human face reconstruction from a frontal 2D image, focusing on facial regions associated with foetal alcohol syndrome

Foetal alcohol syndrome (FAS) is a preventable condition caused by maternal alcohol consumption during pregnancy. The FAS facial phenotype is an important factor for diagnosis, alongside central nervous system impairments and growth abnormalities. Current methods for analysing the FAS facial phenotype rely on 3D facial image data, obtained from costly and complex surface scanning devices. An alternative is to use 2D images, which are easy to acquire with a digital camera or smart phone. However, 2D images lack the geometric accuracy required for accurate facial shape analysis. Our research offers a solution through the reconstruction of 3D human faces from single or multiple 2D images. We have developed a framework for evaluating 3D human face reconstruction from a single-input 2D image using a 3D face model for potential use in FAS assessment. We first built a generative morphable model of the face from a database of registered 3D face scans with diverse skin tones. Then we applied this model to reconstruct 3D face surfaces from single frontal images using a model-driven sampling algorithm. The accuracy of the predicted 3D face shapes was evaluated in terms of surface reconstruction error and the accuracy of FAS-relevant landmark locations and distances. Results show an average root mean square error of 2.62 mm. Our framework has the potential to estimate 3D landmark positions for parts of the face associated with the FAS facial phenotype. Future work aims to improve on the accuracy and adapt the approach for use in clinical settings. Significance: Our study presents a framework for constructing and evaluating a 3D face model from 2D face scans and evaluating the accuracy of 3D face shape predictions from single images. The results indicate low generalisation error and comparability to other studies. The reconstructions also provide insight into specific regions of the face relevant to FAS diagnosis. The proposed approach presents a potential cost-effective and easily accessible imaging tool for FAS screening, yet its clinical application needs further research.

A Compact Two-Dimensional Varifocal Scanning Imaging Device Actuated by Artificial Muscle Material.

This paper presents a compact two-dimensional varifocal-scanning imaging device, with the capability of continuously variable focal length and a large scanning range, actuated by artificial muscle material. The varifocal function is realized by the principle of laterally shifting cubic phase masks and the scanning function is achieved by the principle of the decentered lens. One remarkable feature of these two principles is that both are based on the lateral displacements perpendicular to the optical axis. Artificial muscle material is emerging as a good choice of soft actuators capable of high strain, high efficiency, fast response speed, and light weight. Inspired by the artificial muscle, the dielectric elastomer is used as an actuator and produces the lateral displacements of the Alvarez lenses and the decentered lenses. A two-dimensional varifocal scanning imaging device prototype was established and validated through experiments to verify the feasibility of the proposed varifocal-scanning device. The results showed that the focal length variation of the proposed varifocal scanning device is up to 4.65 times higher (31.6 mm/6.8 mm), and the maximum scanning angle was 26.4°. The rise and fall times were 110 ms and 185 ms, respectively. Such a varifocal scanning device studied here has the potential to be used in consumer electronics, endoscopy, and microscopy in the future.

A protocol to establish trust on biometric authentication devices

AbstractOne of the most extensively utilized mechanisms for person authentication is a system built using biometric‐based authentication. However, many applications use biometric authentication devices that do not support any device authentication mechanisms. As a result, a counterfeit scanning device may be substituted for the genuine one. Non‐authentic biometric authentication devices may perform some additive / subtractive or malicious functions. This paper proposes a technique for establishing trust in biometric authentication devices. The device authentication procedure is essential to build trust in biometric authentication devices such that non‐genuine biometric authentication devices are not used, which may compromise the loss of authentication factor and its replay when the genuine user is not getting authenticated. The protocol uses strong cryptographic mechanisms to authenticate the biometric authentication device with the application server and includes mechanisms for protection against the tampering of biometric templates and to prevent replay attacks. We also perform a formal verification using BAN logic to demonstrate that the proposed protocol meets the defined objectives. The proposed protocol can be used with any biometric authentication device to achieve the same objectives.

Dysfunction of microstructure and metabolism in corpus callosum in juvenile schizophrenia

IntroductionThe corpus callosum (CC) is one of the important structures responsible for communication between the brain hemispheres. Its role is particularly important in cognitive tasks performance, information processing, concentration of attention, in mnestic processes. The corresponding dysfunctions are the major symptoms of schizophrenia, and hence, structural characteristics of CC in schizophrenics are in the focus of attention.ObjectivesThe aim of the study was to analyze the microstructural and metabolic features of the corpus callosum in recently onset schizophrenia.MethodsThe study was carried out in 13 men with juvenile endogenous paroxysmal psychosis (disease standing ≤ 5 years after first manifestation) aged 17-27 years (median 22.0±3.1 years). The studies were carried out during unfolding remission or in remission. Control group consisted of 15 mentally healthy young men (18-28 years). MRI and 1H-MRS studies were carried out on Achieva 3T MRI scanner device (Phillips). Diffuse tensor images were obtained in the axial plane using echo-planar pulse sequence. Diffuse gradients were applied in 32 noncolinear vectors. The spectra were recorded by single voxel 1H-MRS. The spectroscopic voxel (2×1×1 cm) was placed in the CC genu region. The PRESS sequence was used (TR/TE=1500/40 msec).ResultsStatistical analysis showed no abnormal diffusion values in the CC splenium in the patients. Significant changes in the parameters were found in the CC genu. The values of ADC and RD increased, while FA coefficient decreased in the CC genu of patients with the initial stage of schizophrenia; PD values were normal. The increase of RD in the presence of unchanged PD indicated a decrease of water diffusion velocity and anisotropy in the direction perpendicular to the axon orientation. A typical 1H-MRS of the CC genu was presented in Figure 1. The results of statistical analysis of metabolite signal intensities in the CC genu of patients and normal subjects were presented in Figure 2. NAA level was reduced significantly in the patients. No appreciable changes in Cho values in the CC genu were detected in the patients vs. normal subjects.Image:Image 2:ConclusionsThe increase of RD could be caused by several factors: impairment of myelin membranes, axon damage because of impairment of axon cytoskeleton, and changed organization of fibrils. Our results showed that RD increase in patients with early schizophrenia did not conform to active demyelination, which was proven by the normal level of Cho, while axon damage, shown by low level of NAA, did not lead to PD reduction.The decrease of NAA level detected in our study indicated axonal damage in the CC genu of patients in the early stage of schizophrenia. The increase of RD in the presence of normal Cho level seemed to indicate disorders in the axon cytoskeleton damage, but not active demyelination.Disclosure of InterestNone Declared

Test-Retest Reliability of 3D Scanning Devices for Measuring Lower Limb Volume

Test-Retest Reliability of 3D Scanning Devices for Measuring Lower Limb Volume

Detecting Multi-Density Urban Hotspots in a Smart City: Approaches, Challenges and Applications

Leveraged by a large-scale diffusion of sensing networks and scanning devices in modern cities, huge volumes of geo-referenced urban data are collected every day. Such an amount of information is analyzed to discover data-driven models, which can be exploited to tackle the major issues that cities face, including air pollution, virus diffusion, human mobility, crime forecasting, traffic flows, etc. In particular, the detection of city hotspots is de facto a valuable organization technique for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial datasets, which are a valuable support for planners, scientists, and policymakers. However, while classic density-based clustering algorithms show to be suitable for discovering hotspots characterized by homogeneous density, their application on multi-density data can produce inaccurate results. In fact, a proper threshold setting is very difficult when clusters in different regions have considerably different densities, or clusters with different density levels are nested. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering seems to be more appropriate for discovering city hotspots. Indeed, such algorithms rely on multiple minimum threshold values and are able to detect multiple pattern distributions of different densities, aiming at distinguishing between several density regions, which may or may not be nested and are generally of a non-convex shape. This paper discusses the research issues and challenges for analyzing urban data, aimed at discovering multi-density hotspots in urban areas. In particular, the study compares the four approaches (DBSCAN, OPTICS-xi, HDBSCAN, and CHD) proposed in the literature for clustering urban data and analyzes their performance on both state-of-the-art and real-world datasets. Experimental results show that multi-density clustering algorithms generally achieve better results on urban data than classic density-based algorithms.

Synthetic cranial MRI from 3D optical surface scans using deep learning for radiation therapy treatment planning

BackgroundOptical scanning technologies are increasingly being utilised to supplement treatment workflows in radiation oncology, such as surface-guided radiotherapy or 3D printing custom bolus. One limitation of optical scanning devices is the absence of internal anatomical information of the patient being scanned. As a result, conventional radiation therapy treatment planning using this imaging modality is not feasible. Deep learning is useful for automating various manual tasks in radiation oncology, most notably, organ segmentation and treatment planning. Deep learning models have also been used to transform MRI datasets into synthetic CT datasets, facilitating the development of MRI-only radiation therapy planning.AimsTo train a pix2pix generative adversarial network to transform 3D optical scan data into estimated MRI datasets for a given patient to provide additional anatomical data for a select few radiation therapy treatment sites. The proposed network may provide useful anatomical information for treatment planning of surface mould brachytherapy, total body irradiation, and total skin electron therapy, for example, without delivering any imaging dose.MethodsA 2D pix2pix GAN was trained on 15,000 axial MRI slices of healthy adult brains paired with corresponding external mask slices. The model was validated on a further 5000 previously unseen external mask slices. The predictions were compared with the “ground-truth” MRI slices using the multi-scale structural similarity index (MSSI) metric. A certified neuro-radiologist was subsequently consulted to provide an independent review of the model’s performance in terms of anatomical accuracy and consistency. The network was then applied to a 3D photogrammetry scan of a test subject to demonstrate the feasibility of this novel technique.ResultsThe trained pix2pix network predicted MRI slices with a mean MSSI of 0.831 ± 0.057 for the 5000 validation images indicating that it is possible to estimate a significant proportion of a patient’s gross cranial anatomy from a patient’s exterior contour. When independently reviewed by a certified neuro-radiologist, the model’s performance was described as “quite amazing, but there are limitations in the regions where there is wide variation within the normal population.” When the trained network was applied to a 3D model of a human subject acquired using optical photogrammetry, the network could estimate the corresponding MRI volume for that subject with good qualitative accuracy. However, a ground-truth MRI baseline was not available for quantitative comparison.ConclusionsA deep learning model was developed, to transform 3D optical scan data of a patient into an estimated MRI volume, potentially increasing the usefulness of optical scanning in radiation therapy planning. This work has demonstrated that much of the human cranial anatomy can be predicted from the external shape of the head and may provide an additional source of valuable imaging data. Further research is required to investigate the feasibility of this approach for use in a clinical setting and further improve the model’s accuracy.

Scene point cloud understanding and reconstruction technologies in 3D space

Scene point cloud understanding and reconstruction technologies in 3D space

Prototype-Guided Autoencoder for OCT-Based Fingerprint Presentation Attack Detection

Anti-spoofing ability is vital for fingerprint identification systems. Conventional fingerprint scanning devices can only obtain information from the fingertip surfaces, and their performance is susceptible to skin conditions and presentation attacks (PAs). However, optical coherence tomography (OCT) can scan subcutaneous tissue and obtain 3D fingerprint structures, naturally enhancing its PA detection (PAD) ability from the perspective of hardware. Existing unsupervised PAD methods are based on image reconstruction. However, the reconstruction error is easily affected by OCT noise and the rich details of OCT images. Therefore we propose feature-based reconstruction to alleviate this problem, called the prototype-guided autoencoder. The model consists of a memory module and a denoising autoencoder without the requirement of PA fingerprints. As only bona fide fingerprints are available during the training phase, the memory module contains the prototype features of the bona fide fingerprints. During the inference phase, as the prototype memory module is frozen, the reconstructed representation of the bona fide input is close to the bona fide fingerprint features. Calculating the distance between the original features and the prototype reconstructed representation of the sample can achieve PAD. To obtain a better decision making boundary, we propose a representation consistency constraint, which reduces the bona fide representation reconstruction distance closer, so that it is easier to differentiate between fingerprints and PAs.

Consumer Document Analytical Accelerator Hardware

Document scanning devices are used for visual character recognition, followed by text analytics in the software. Often such character extraction is insecure, and any third party can manipulate the information. On the other hand, near-edge processing devices are restrained by limited resources and connectivity issues. The primary factors that lead to exploring independent hardware devices with natural language processing (NLP) capabilities are latency during cloud processing and computing costs. This paper introduces a hardware accelerator for information retrieval using memristive TF-IDF implementation. In this system, each sentence is represented using a memristive crossbar layer, with each column containing a single word. The number of matching scores for the TF and IDF values was implemented using operational amplifier-based comparator accumulator circuits. The circuit is designed with a 180nm CMOS process, Knowm Multi-Stable Switch memristor model, and WOx device parameters. We compared its performance with that of a standard benchmark dataset. Variability and device-to-device related issues were also taken into consideration in the analysis. This paper concludes with implementing TF-IDF score calculation for applications such as information retrieval and text summarization.

Accuracy of intraoral optical scan versus stereophotogrammetry for complete-arch digital implant impression: An in vitro study.

To assess and compare the accuracies of intraoral scanners (IOS) and stereophotogrammetry (SPG) devices for complete-arch digital implant impressions. A 4-analog model was digitized using a desk scanner to obtain a reference file. Thirty test scans were conducted using the investigated IOS device, while an additional 30 scans were performed using the SPG device. Using the best-fit algorithm, the resulting 60 test files were aligned with the reference file. Linear (ΔX, ΔY, and ΔZ-axis) and angular deviations (ΔANGLE) were evaluated. Three-dimensional (3D) deviation was calculated based on the Euclidean distance (ΔEUC). The analysis was stratified according to the scanning device and implant position. Fisher's F and t-tests were used to compare the variances and expected values of the two scanning systems. IOS expressed a higher 3D (ΔEUC) mean deviation than SPG (52.8 µm vs. 33.4 µm, P < 0.0001), with extreme measurements up to 181.9 µm. A significantly higher standard deviation (SD) was associated with IOS (37.1 µm vs. 17.7 µm, P < 0.0001). Considering angular deviations, the IOS showed slightly higher angular mean deviations (ΔANGLE) than the SPG (0.28° vs. 0.24°, P = 0.0022), with extreme measurements of up to 0.73°. The SPG SD values were significantly lower than the IOS SD values (0.14° vs. 0.04°, P < 0.0001). The SPG showed significantly higher 3D and angular accuracies for complete arch implant impressions, with consistent repeatability. IOS scanning revealed significantly higher extreme deviations exceeding the acceptable threshold value. Despite study limitations, SPG appears more feasible than IOS for complete-arch digital implant impressions.

Point Clouds Outlier Removal Method Based on Improved Mahalanobis and Completion

Point clouds have been regarded as a representative format for 3D visualization of real-world objects or scenes. However, point clouds acquired from depth cameras or laser scanning devices commonly contain outliers. Outlier removal performance will directly affect the downstream applications. Existing methods mainly perform outlier removal directly on the raw data, which are designed without finding a proper balance between outlier removal precision and detail feature retention. In this letter, we propose a novel outlier removal method called CIMD that can obtain outlier-free data by conducting the improved Mahalanobis distance and point clouds completion. A layered statistical outlier removal approach is introduced as preprocessing strategy to obtain feature points and incomplete point clouds. We filter feature points to fill the incomplete point clouds according to the improved Mahalanobis distances. The theoretical analysis proves that the improved Mahalanobis distance can magnify the difference between outliers and ground truth compared with the original way. Using publicly available dataset PointCleanNet and real-world scanned data, the experimental results show that the proposed method has superior performance compared with state-of-the-art methods.

Semi-Automated Enzymatic Determination of Ethanol in Beverages: Collaborative Study for RIDA&amp;#174;CUBE Ethanol

Easy and quick methods to quantify ethanol reliably in beverages are always important. In 2022, the EnzytecTM Liquid Ethanol test kit was approved as AOAC Official MethodSM 2017.07 Final Action after a collaborative study was conducted with different beverages such as kombucha, juices, and beer. During set-up of this collaborative test, small sized companies asked to include the RIDA®CUBE Ethanol/RIDA®CUBE SCAN device since it is easy to use, suitable for a few samples only and contains the identical reagents as the EnzytecTM Liquid system. It is applicable to quantify ethanol in diluted kombucha, fruit juices, and alcohol-free beer samples around 0.5% alcohol-by-volume within 12 min. The overall relative reproducibility standard deviation across a wide concentration range for kombucha, was calculated to be 6.29%. Analysis of juices and beer showed an overall higher variation with an estimated overall RSD(R) value by regression of 14.4%. The data obtained by this collaborative study show that the RIDA®CUBE Ethanol in combination with the RIDA®CUBE SCAN device is suitable to quantify ethanol from matrices representing important alcohol-free liquid food categories.