Search Results (172)

The aim of the present research is to validate the combined use, through data fusion, of a Laser Induced Fluorescence (LIF) scanning system and a radar scanner (RGB-ITR, Red Green Blue Imaging Topological Radar system), as a unique tool to address the need for non-invasive, rapid, and low-cost techniques for both diagnostic and operational needs. The integrated system has been applied to the House of Diana complex in Ostia Antica. The main diagnostic objective of this research was to trace the materials used in different phases of restoration, from antiquity to modernity, on both masonry and pictorial surfaces, to reconstruct the history of the building. Due to the significant interest in this insula, other studies have been recently carried out on the House of Diana, but they once again highlighted the necessity of multiple approaches and non-invasive methods capable of providing quasi-real-time answers, delivering point-by-point information on very large surfaces to overcome the limits related to representativeness of sampling. The data acquired by the RGB-ITR system are quantitative, allowing for morphological and 3-colour analysis of the investigated artwork. In this work, the sensor has been used to create coloured 3D models useful for structural assessments and for locating different classes of materials. In fact, the LIF maps, which integrate knowledge about the original constituent materials and previous conservation interventions, have been used as additional layers of the tridimensional models. Therefore, the method can direct possible new investigations and restoration actions, piecing together the history of the House of Diana to build for it a safer future. Full article

Ultrasonic sensing may become a useful technique for distance measurement and object detection when optical visibility is not available. However, the research on detecting multiple target objects and locating their coordinates is limited. This makes it a valuable topic. Reflection signal data obtained from a single ultrasonic sensor may be just enough for the measurements of distance and reflection strength. On the other hand, if extracted properly, a scanned set of signal data by the same sensor holds a significant amount of information about the surrounding geometries. Evaluating this dataset from a single sensor scanning can be a perfect application for convolutional neural networks (CNNs). This study proposes an imaging technique based on a scanned dataset obtained by a single low-cost ultrasonic sensor. So that images are suitable for desired outputs in a CNN, a 3D printer is converted to an ultrasonic image scanner and automated to perform as a data acquisition system for the desired datasets. A deep learning model demonstrated by this work extracts object features using convolutional neural networks (CNNs) and performs coordinate estimation using regression layers. With the proposed solution, by training a reasonable amount of obtained data, 90% accuracy was achieved in the classification and position estimation of multiple objects with the CNN algorithm as a result of converting the signals obtained from ultrasonic sensors into images. Full article

The routine culling of male chicks in the laying hen industry raises significant ethical, animal welfare, and sustainability concerns. Current methods to determine chick embryo sex before hatching are costly, time-consuming, and invasive. This study aimed to develop a low-cost, non-invasive solution to predict chick embryo sex before hatching using the morphological features of eggs. A custom imaging apparatus was created using a smartphone and light box, enabling consistent image capture of chicken eggs. Egg length, width, area, eccentricity, and extent were measured, and machine learning models were trained to predict chick embryo sex. The wide neural network model achieved the highest accuracy of 88.9% with a mean accuracy of 81.5%. Comparison of the imaging apparatus to a high-cost industrial 3D scanner demonstrated comparable accuracy in capturing egg morphology. The findings suggest that this method can contribute to the prevention of up to 6.2 billion male chicks from being culled annually by destroying male embryos before they develop the capacity to feel pain. This approach offers a feasible, ethical, and scalable alternative to current practices, with potential for further improvements in accuracy and adaptability to different industry settings. Full article

Registering light detection and ranging (LiDAR) data with optical camera images enhances spatial awareness in autonomous driving, robotics, and geographic information systems. The current challenges in this field involve aligning 2D-3D data acquired from sources with distinct coordinate systems, orientations, and resolutions. This paper introduces a new pipeline for camera–LiDAR post-registration to produce colorized point clouds. Utilizing deep learning-based matching between 2D spherical projection LiDAR feature layers and camera images, we can map 3D LiDAR coordinates to image grey values. Various LiDAR feature layers, including intensity, bearing angle, depth, and different weighted combinations, are used to find correspondence with camera images utilizing state-of-the-art deep learning matching algorithms, i.e., SuperGlue and LoFTR. Registration is achieved using collinearity equations and RANSAC to remove false matches. The pipeline’s accuracy is tested using survey-grade terrestrial datasets from the TX5 scanner, as well as datasets from a custom-made, low-cost mobile mapping system (MMS) named Simultaneous Localization And Mapping Multi-sensor roBOT (SLAMM-BOT) across diverse scenes, in which both outperformed their baseline solutions. SuperGlue performed best in high-feature scenes, whereas LoFTR performed best in low-feature or sparse data scenes. The LiDAR intensity layer had the strongest matches, but combining feature layers improved matching and reduced errors. Full article

Terrestrial laser scanners (TLS) are portable dimensional measurement instruments used to obtain 3D point clouds of objects in a scene. While TLSs do not require the use of cooperative targets, they are sometimes placed in a scene to fuse or compare data from different instruments or data from the same instrument but from different positions. A contrast target is an example of such a target; it consists of alternating black/white squares that can be printed using a laser printer. Because contrast targets are planar as opposed to three-dimensional (like a sphere), the center of the target might suffer from errors that depend on the orientation of the target with respect to the TLS. In this paper, we discuss a low-cost method to characterize such errors and present results obtained from a short-range TLS and a long-range TLS. Our method involves comparing the center of a contrast target against the center of spheres and, therefore, does not require the use of a reference instrument or calibrated objects. For the short-range TLS, systematic errors of up to 0.5 mm were observed in the target center as a function of the angle for the two distances (5 m and 10 m) and resolutions (30 points-per-degree (ppd) and 90 ppd) considered for this TLS. For the long-range TLS, systematic errors of about 0.3 mm to 0.8 mm were observed in the target center as a function of the angle for the two distances (5 m and 10 m) at low resolution (28 ppd). Errors of under 0.3 mm were observed in the target center as a function of the angle for the two distances at high resolution (109 ppd). Full article

The flavor substances produced by the division of baijiu yeast during the winemaking process often determine the quality of white wine, and the difficulty of storing and transporting high-quality baijiu yeast is a bottleneck that restricts the development of China’s baijiu industry. It is widely accepted that drying microorganisms such as baijiu yeast is the best way to improve its storage and transport performance. Spray drying, as one of the most widely used microbial drying processes, with a high efficiency and low cost, is the hot spot of current research in the field of microbial drying, but it has the inherent defect of a low drying survival rate. In order to address this inherent defect, the present study was carried out with a high-quality white wine yeast, Modified Sporidiobolus Johnsonii A (MSJA), as the target. Firstly, an orthogonal experiment, Steep Hill Climbing experiment, and response surface experiment were sequentially designed to optimize the type and amount of protective agent added in the spray-drying process of MSJA. Then, the effects of glutamyl transaminase (TGase) treatment on the drying process of MSJA were revealed with the help of advanced equipment, such as laser particle sizer, environmental scanning electron microscope (ESEM), and Fourier-transform infrared scanner (FTIR). The results showed that the addition of “TGase-treated soybean isolate protein (SPI) + lactic protein (LP)” as an in vitro bacterial protectant and “14.15% trehalose + 7.10% maltose + 14.04% sucrose” TGase treatment can promote the cross-linking of protective proteins, reduce the distance between MSJA bacteria and protective proteins, and increase the glass transition temperature to enhance the protective effect of protective proteins, so as to improve the survival rate of MSJA during spray drying. Full article

In various types of industrial applications, such as reverse engineering, machine operation, technical metrology, or modern factory maintenance, it is important to have systems that enable the quick and easy scanning of selected mechanical parts. This study presents the design process and analysis of a low-cost, 3D scanning system which can be used in industrial applications. The system collects point cloud data using an infrared distance sensor based on optical triangulation, controlled by a 32-bit microcontroller. Communication with the system is enabled through a serial interface and a dedicated window application, allowing users to monitor and adjust scanning parameters. The output data in the form of a point cloud are saved in a text file in the scanner’s controller memory and then sent wirelessly to an external device, e.g., cloud and/or a diagnostic controller. The electronic system is equipped with a radio module that can be used to communicate with other devices in line with the idea of the Internet of Things and the concept of Industry 4.0. The results of the study are based on the accuracy of the three-dimensional digitization of the tested object and on the determination of the average measurement uncertainty. Full article

Precision agriculture relies on highly detailed soil maps to optimize resource use. Proximal sensing methods, such as EMI, require a certain number of soil samples and laboratory analysis to interpolate the characteristics of the soil. NIR diffuse reflectance spectroscopy offers a rapid, low-cost alternative that increases datapoints and map accuracy. This study tests and optimizes a methodology for high-detail soil mapping in a 2.5 ha hazelnut grove in Grosseto, Southern Tuscany, Italy, using both EMI sensors (GF Mini Explorer, Brno, Czech Republic) and a handheld NIR spectrometer (Neospectra Scanner, Si-Ware Systems, Menlo Park, CA, USA). In addition to two profiles selected by clustering, another 35 topsoil augerings (0–30 cm) were added. Laboratory analyses were performed on only five samples (two profiles + three samples from the augerings). Partial least square regression (PLSR) with a national spectral library, augmented by the five local samples, predicted clay, sand, organic carbon (SOC), total nitrogen (TN), and cation exchange capacity (CEC). The 37 predicted datapoints were used for spatial interpolation, using the ECa map, elevation, and DEM derivatives as covariates. Kriging with external drift (KED) was used to spatialize the results. The errors of the predictive maps were calculated using five additional validation points analyzed by conventional methods. The validation showed good accuracy of the predictive maps, particularly for SOC and TN. Full article

This article examines the potential of low-cost LiDAR technology for 3D modeling and assessment of the degradation of historic buildings, using a section of the Koszalin city walls in Poland as a case study. Traditional terrestrial laser scanning (TLS) offers high accuracy but is expensive. The study assessed whether more accessible LiDAR options, such as those integrated with mobile devices such as the Apple iPad Pro, can serve as viable alternatives. This study was conducted in two phases—first assessing measurement accuracy and then assessing degradation detection—using tools such as the FreeScan Combo scanner and the Z+F 5016 IMAGER TLS. The results show that, while low-cost LiDAR is suitable for small-scale documentation, its accuracy decreases for larger, complex structures compared to TLS. Despite these limitations, this study suggests that low-cost LiDAR can reduce costs and improve access to heritage conservation, although further development of mobile applications is recommended. Full article

Conventional strategies aimed at mitigating beam-hardening artifacts in computed tomography (CT) can be categorized into two main approaches: (1) postprocessing following conventional reconstruction and (2) iterative reconstruction incorporating a beam-hardening model. While the former fails in low-dose and/or limited-data cases, the latter substantially increases computational cost. Although deep learning-based methods have been proposed for several cases of limited-data CT, few works in the literature have dealt with beam-hardening artifacts, and none have addressed the problems caused by randomly selected projections and a highly limited span. We propose the deep learning-based prior image constrained (PICDL) framework, a hybrid method used to yield CT images free from beam-hardening artifacts in different limited-data scenarios based on the combination of a modified version of the Prior Image Constrained Compressed Sensing (PICCS) algorithm that incorporates the L2 norm (L2-PICCS) with a prior image generated from a preliminary FDK reconstruction with a deep learning (DL) algorithm. The model is based on a modification of the U-Net architecture, incorporating ResNet-34 as a replacement of the original encoder. Evaluation with rodent head studies in a small-animal CT scanner showed that the proposed method was able to correct beam-hardening artifacts, recover patient contours, and compensate streak and deformation artifacts in scenarios with a limited span and a limited number of projections randomly selected. Hallucinations present in the prior image caused by the deep learning model were eliminated, while the target information was effectively recovered by the L2-PICCS algorithm. Full article

This paper presents a robust point cloud registration method based on a multi-scale covariance matrix descriptor and an accurate transformation estimation. Compared with state-of-the-art feature descriptors, such as FPH, 3DSC, spin image, etc., our proposed multi-scale covariance matrix descriptor is superior for dealing with registration problems in a higher noise environment since the mean operation in generating the covariance matrix can filter out most of the noise-damaged samples or outliers and also make itself robust to noise. Compared with transformation estimation, such as feature matching, clustering, ICP, RANSAC, etc., our transformation estimation is able to find a better optimal transformation between a pair of point clouds since our transformation estimation is a multi-level point cloud transformation estimator including feature matching, coarse transformation estimation based on clustering, and a fine transformation estimation based on ICP. Experiment findings reveal that our proposed feature descriptor and transformation estimation outperforms state-of-the-art feature descriptors and transformation estimation, and registration effectiveness based on our registration framework of point cloud is extremely successful in the Stanford 3D Scanning Repository, the SpaceTime dataset, and the Kinect dataset, where the Stanford 3D Scanning Repository is known for its comprehensive collection of high-quality 3D scans, and the SpaceTime dataset and the Kinect dataset are captured by a SpaceTime Stereo scanner and a low-cost Microsoft Kinect scanner, respectively. Full article

In this work, a microfluidic paper-based analytical device (µPAD) for simultaneous detection of Fe, Zn, and Mn ions using immobilized chromogenic reagents Ferene S, xylenol orange, and 1-(2-pyridylazo)-2-naphthol, respectively, is presented. As the effective recognition of analytes via respective chromogens takes place under extremely different pH conditions, experiments reported in this publication are focused on optimization of the µPAD architecture allowing for the elimination of potential cross effects. The paper-based microfluidic device was fabricated using low-cost and well-reproducible wax-printing technology. For optical detection of color changes, an ordinary office scanner and self-made RGB-data processing program were applied. Optimized and stable over time, µPADs allow fast, selective, and reproducible multianalyte determinations at submillimolar levels of respective heavy metal ions, which was confirmed by results of the analysis of solutions mimicking real samples of wastewater. The presented concept of simultaneous determination of different analytes that required extremely different conditions for detection can be useful for the development of other multianalyte microfluidic paper-based devices in the µPAD format. Full article

Piezoelectric actuators (PEAs) are extensively used for scanning and positioning in scanning probe microscopy (SPM) due to their high precision, simple construction, and fast response. However, there are significant challenges for instrument designers due to their nonlinear properties. Nonlinear properties make precise and accurate control difficult in cases where position feedback sensors cannot be employed. However, the performance of PEA-driven scanners can be significantly improved without position feedback sensors if an accurate mathematical model with low computational costs is applied to reduce hysteresis and other nonlinear effects. Various methods have been proposed for modeling PEAs, but most of them have limitations in terms of their accuracy and computational efficiencies. In this research, we propose a variant DenseNet-type neural network (NN) model for modeling PEAs in an AFM scanner where position feedback sensors are not available. To improve the performance of this model, the mapping of the forward and backward directions is carried out separately. The experimental results successfully demonstrate the efficacy of the proposed model by reducing the relative root-mean-square (RMS) error to less than 0.1%. Full article

The popularity of mobile laser scanning systems as a surveying tool is growing among construction contractors, architects, land surveyors, and urban planners. The user-friendliness and rapid capture of precise and complete data on places and objects make them serious competitors for traditional surveying approaches. Considering the low cost and constantly improving availability of Mobile Laser Scanning (MLS), mainly handheld surveying tools, the measurement possibilities seem unlimited. We conducted a comprehensive investigation into the quality and accuracy of a point cloud generated by a recently marketed low-cost mobile surveying system, the MandEye MLS. The purpose of the study is to conduct exhaustive laboratory tests to determine the actual metrological characteristics of the device. The test facility was the surveying laboratory of the University of Agriculture in Kraków. The results of the MLS measurements (dynamic and static) were juxtaposed with a reference base, a geometric system of reference points in the laboratory, and in relation to a reference point cloud from a higher-class laser scanner: Leica ScanStation P40 TLS. The Authors verified the geometry of the point cloud, technical parameters, and data structure, as well as whether it can be used for surveying and mapping objects by assessing the point cloud density, noise and measurement errors, and detectability of objects in the cloud. Full article

Structured-light scanning is a fast and efficient technique for the acquisition of 3D point clouds. However, the extensive and daily application of this class of scanners can be challenging because of the technical know-how necessary to validate the low-cost instrumentation. This challenge is worth accepting because of the large amount of data that can be collected accurately with the aid of specific technical protocols. This work is a preliminary study of the development of an acquisition protocol for anthropological remains performing tests in two opposite and extreme contexts: one characterised by a dark environment and one located in an open area and characterised by a very bright environment. This second context showed the influence of sunlight in the acquisition process, resulting in a colourless point cloud. It is a first step towards the development of a technical protocol for the acquisition of anthropological remains, based on the research of limits and problems associated with an instrument. Full article