Camera Images Research Articles

Strain concentration applied on crack inspection based on marker-based and homography matrix algorithm

ABSTRACT The detection and identification of ship damages have become a significant area of research. This study proposes an image-based structural crack monitoring method that marks the crack locations in the form of strain concentrations by analyzing the overall strain of the structure. Most existing studies focus on using fixed cameras for structural crack monitoring. However, during the actual operation of structures, continuous monitoring around these structures is challenging. To overcome this limitation, this paper implements the transformation from non-fixed camera images to fixed images using a marker-based approach and homography matrix algorithm. In laboratory-scale structural experiments, the algorithm is capable of locating structural crack positions by observing the behavior of strain concentration. Additionally, this study explores the application of markers in structures and verifies the capability of marker attachment methods in crack inspection.

For a clinical application of optical triangulation to assess respiratory rate using an RGB camera and a line laser

This paper presents a non-contact and unrestrained respiration monitoring system based on the optical triangulation technique. The proposed system consists of a red-green-blue (RGB) camera and a line laser installed to face the frontal thorax of a human body. The underlying idea of the work is that the camera and line laser are mounted in opposite directions, unlike other research. By applying the proposed image processing algorithm to the camera image, laser coordinates are extracted and converted to world coordinates using the optical triangulation method. These converted world coordinates represent the height of the thorax of a person. The respiratory rate is measured by analyzing changes of the thorax surface depth. To verify system performance, the camera and the line laser are installed on the head and foot sides of a bed, respectively, facing toward the center of the bed. Twenty healthy volunteers were enrolled and underwent measurement for 100s. Evaluation results show that the optical triangulation-based image processing method demonstrates non-inferior performance to a commercial patient monitoring system with a root-mean-squared error of 0.30rpm and a maximum error of 1rpm (p>0.05\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p > 0.05$$\\end{document}), which implies the proposed non-contact system can be a useful alternative to the conventional healthcare method.

Developing Specific Emission Factors for Natural Gas Driven Pneumatic Devices.

A measurement study was conducted in 2023 to derive operator-specific emission factors for natural gas driven pneumatic devices at onshore production facilities in the United States. A total of 369 intermittent bleed and 26 continuous low-bleed pneumatic devices were measured using a high-volume sampler. Considering all intermittent bleed devices, the emission factor from this study was statistically lower than the factor in the revised Greenhouse Gas Reporting Rule (GHGRP) issued May 6, 2024. Intermittent devices were classified by inspection with an optical gas imaging camera as functioning or malfunctioning. Measurements of functioning intermittent bleed devices were statistically higher while measurements of malfunctioning intermittent bleed devices were statistically lower than the corresponding emission factor in the final revisions to the GHGRP. Measurements of continuous low-bleed pneumatic devices were statistically lower than the updated factor in the final revisions to GHGRP. Additionally, a Monte Carlo analysis was conducted to investigate the potential impact of measurement duration and sample size on emission factors derived for intermittent bleed devices. We conclude that while a short measurement duration may miss actuations of properly operating devices with low vent frequencies, potentially resulting in an emission factor with low bias, the sample size of greater than 300 measurements each greater than 3 min in duration, as included in this study, is unlikely to be biased low in a statistically significant manner, particularly when one considers the material contribution of malfunctioning devices.

Natural gas leakage detection from offshore platform by OGI camera and unsupervised deep learning

Natural gas leak from offshore platform poses a potential to cause explosion disaster and bring significant causalities and economic losses. Existing deep learning-based leak detection approaches are limited by the requirement of a large number of labeled leak datasets, and also has worse performance in the complex and changeable marine environment. This study proposes a detection approach of natural gas leakage from offshore platform by integrating optical gas imaging (OGI) camera and unsupervised deep probability learning. In this approach, unsupervised deep learning is applied to learn the changeable infrared features of offshore platform, and variational Bayesian inference is integrated to provide the larger epistemic uncertainty contour corresponding to the infrared natural gas plume. An epistemic uncertainty-based detection score is proposed as the detection criterion to improve the accuracy of natural gas plume detection and localization. An OGI imaging experiment of natural gas leak from offshore platform is conducted to construct the benchmark dataset. With such datasets, two pre-defined parameters, namely Monte Carlo sampling number m = 100 and dropout probability p=0.1 are determined to guarantee the detection accuracy and efficiency. Comparison between the proposed approach and prevalent unsupervised deep learning approach is also conducted. The results demonstrate that our proposed approach has the higher detection accuracy with AUC = 0.9753, as well as the real-time capability with inference time of 4s/frame. Overall, our proposed approach provides a more accurate and generalized approach of natural gas detection for safety monitoring and detection management of offshore platforms.

Development of an online prediction system for soil organic matter and soil moisture content based on multi-modal fusion

Realizing accurate determination of in situ soil organic matter (SOM) content and soil moisture (SM) content in the field is of great importance for improving agricultural production efficiency. However, the feature information of a single sensor is limited, and there is still a certain gap in modeling accuracy compared to traditional laboratory methods. The variations of SM in the field also interfere with data collection, limiting the application of single sensor. In order to realize the efficient detection of in-situ SOM and SM content, this study developed an online detection system for SOM and SM content based on the fusion of characteristic wavelengths, visible images and thermal imaging image features. Firstly, based on a vehicle-mounted platform, a visible-thermal imaging camera and a characteristic wavelength integration device were integrated in a deep pine plow to realize the simultaneous acquisition of in-situ soil multi-sensor data. Then, a lightweight multimodal network was constructed to obtain thermal visible light image features and characteristic wavelength features through branch networks, achieving deep fusion of different modal data and predicting SOM and SM content. Finally, output the forecasting results of SOM and SM content, and transmit the predictive information to the cloud platform for storage. It was verified that the Multi-modal proposed in this study worked best in the laboratory environment, with a predicted R2 of 0.91 and RMSE of 2.9 g/kg for SOM, and a predicted R2 of 0.92 and RMSE of 0.77 % for SM. Compared with single image or characteristic spectral data, the fusion of visible image and characteristic wavelength effectively improved the prediction accuracy of SOM. The real-time prediction of SM was realized by fusing thermal imaging data, and the elimination of the moisture effect of visible images and characteristic wavelengths was also realized with the help of deep fusion of Multi-modal network. After field validation, the R2 of the Multi-modal system was 0.84 and the RMSE was 5.0 g/kg for SOM, and the R2 of the SM was 0.88 and the RMSE was 1.03 %. Despite the differences in soil types between the validation field and the sampling field, the system still demonstrated strong generalization and achieved high accuracy prediction of in-situ SOM and SM in the field, which effectively improves the efficiency and applicability of field. The efficiency and applicability of soil testing effectively improve the efficiency and provide technical guidance for precision management in the field.

Methods for Assessing the Effectiveness of Modern Counter Unmanned Aircraft Systems

Given the growing threat posed by the widespread availability of unmanned aircraft systems (UASs), which can be utilised for various unlawful activities, the need for a standardised method to evaluate the effectiveness of systems capable of detecting, tracking, and identifying (DTI) these devices has become increasingly urgent. This article draws upon research conducted under the European project COURAGEOUS, where 260 existing drone detection systems were analysed, and a methodology was developed for assessing the suitability of C-UASs in relation to specific threat scenarios. The article provides an overview of the most commonly employed technologies in C-UASs, such as radars, visible light cameras, thermal imaging cameras, laser range finders (lidars), and acoustic sensors. It explores the advantages and limitations of each technology, highlighting their reliance on different physical principles, and also briefly touches upon the legal implications associated with their deployment. The article presents the research framework and provides a structural description, alongside the functional and performance requirements, as well as the defined metrics. Furthermore, the methodology for testing the usability and effectiveness of individual C-UAS technologies in addressing specific threat scenarios is elaborated. Lastly, the article offers a concise list of prospective research directions concerning the analysis and evaluation of these technologies.

Flat mirrors, virtual rear-view cameras, and camera-mirror calibration

Flat mirrors are useful to generate virtual cameras and capture a scene from multiple viewpoints. However, the resultant camera-mirror setup is cumbersome to calibrate because of the mirror image reversion. The typical calibration flaws are omitting the checkerboard symmetry and assuming that virtual cameras are front-view. This paper explains the rear-view abstraction and its usefulness in calibrating a camera-mirror setup to obtain the equivalent mirrorless configuration. The underlying theory is reviewed, including camera imaging, the calibration process, and the laws of reflection. The proposed approach is illustrated by calibrating a camera-mirror setup and then using the calibrated camera to generate mirror images synthetically. This work offers a practical insight into calibrating more sophisticated systems such as mirrored camera-projector profilometers.

Structural dynamics of individual plant fibres: a contribution to the understanding of damping properties of bio-based composites

The field of bio-based composites faces challenges in understanding the physical mechanisms providing excellent compromise between stiffness and damping. Potential sources of damping, including the matrix, the fibre, and the interfaces (fibres/fibres and fibres/matrix) are identified in the literature. To better understand damping at the composite scale, it is necessary to understand the dissipation causes at each scale of the material. However, the mechanical characteristics of individual plant fibres, such as modulus and damping, can be uncertain due to the complexity of measuring their properties at such small scale (10 µm diameter, a few mm long). To address this knowledge gap, this work proposes a method based on dynamic response of individual plant fibres. By exciting the fibre with a piezoelectric actuator and using high-speed camera image processing to evaluate displacements along the fibre, it becomes possible to determine intrinsic dynamic properties such as the loss factor and storage modulus. Controlled environment tests are conducted to investigate the influence of external parameters such as temperature and pressure on the measurement results. By utilizing this method, the aim is to obtain a better understanding of the damping properties of plant fibres and ultimately improve design choices for bio-based composite materials.

Methods to mitigate the impact of work environment on the measurement of building surface temperature by unmanned aerial vehicle onboard thermal imaging system

With the widespread adoption of UAVs and compact infrared cameras, UAV infrared remote sensing technology has extensive applications in qualitative research fields such as finding thermal bridges and air tightness detection in construction projects, but less in quantitative research, mainly due to its large temperature measurement error. When measuring temperature with UAV-mounted thermal imaging cameras, factors such as operational wind conditions and operation methods, in addition to the inherent accuracy of the instrument, can introduce measurement errors. To investigate the impact of operational wind conditions on temperature measurements by UAV-mounted thermal imaging cameras, both field and laboratory experiments were conducted to analyze the error characteristics caused by wind conditions. Devices were designed to mitigate the impact of wind on temperature measurements, and their effectiveness was validated through experiments. Based on these findings, methods were developed for reducing the influence of wind conditions on temperature measurements in UAV operations. The results indicate that wind condition errors primarily affect the stability of temperature data. Under the influence of wind conditions, the temperature data recorded by thermal infrared cameras exhibit periodic and significant fluctuations, with a temperature difference of approximately 4.7 °C between the highest and lowest points within a single cycle, constituting the main source of measurement error. However, by following the prescribed measurement procedures, the temperature measurement error can be reduced by 32 %, from ±5 °C to ±1.6 °C, approaching the accuracy of tripod-mounted thermal imaging cameras. This establishes a foundation for quantitative research in low-altitude infrared remote sensing.

Automated wireless system for monitoring the technical condition of chimneys

The present paper considers the technology of monitoring the technical condition of chimneys in order to identify areas of increased heat losses. The technology involves an automated wireless monitoring system that includes an unmanned aircraft with additional equipment. Aim. To study the operation safety of reinforced concrete chimneys used in hazardous industrial facilities. The presented monitoring technology tackles the task of prompt identification of defects and damages to reduce the risks of potentially hazardous conditions. While developing technology for monitoring the technical condition, special attention is paid to its autonomy and intelligence. At the first stage, sensors, coordinators and remote thermometry system are installed with the transmission of data on the temperature of the chimney trunk to the control center. At the next stage, the outer surface of the chimney is examined using an unmanned aicraft equipped with a thermal imaging camera to verify information about heat losses. The final stage involves creating a thermogram of the object and a scheme of the possible locations of the identified defects in order to plan repair works. In addition, the paper introduces main advantages of the suggested technology.

B-019 Reduction in Culture Media Costs, without Impacting Turnaround Time, Through Implementation of an Automated Microbiology Platform and Gram Stain Interpretation

Abstract Background Automated plating instruments have been shown to improve quality in Microbiology laboratories in a number of ways including consistent streaking patterns that provide better isolation of bacterial colonies compared to manual methods. The time per culture set up and automated camera imaging is dependent upon the amount of media set, such that more individual plates slow down turn around time. Conversely, smaller media surface area and the use of non-selective media may result in mixed colonies that require sub-culture for isolation before identification and susceptibilities can be performed. Based upon these findings, we attempted to reduce our media usage per culture with the implementation of automation, with the goal of using as few plates as possible while at the same time not negatively impacting the turn around time of the culture via the need to subculture poorly isolated colonies. We conducted a pre-and post-implementation study evaluating the costs per culture and turn around time delays related to subculturing in a subset. Methods In the pre-implementation period, from April-January 2023, three individual media plates (sheep blood agar, chocolate agar and CNA/MacConkey bi-plate) were routinely set for general aerobic bacterial culture sources including swabs and body fluids. In the post implementation period, from April-January 2024, a single sheep blood/chocolate agar bi-plate was set unless the gram stain showed evidence of mixed organisms (defined as more than one morphology) at which time a secondary selective media (CNA/MacConkey) was manually set. For urine cultures, in the pre-implementation period 2 plates were set (sheep blood and CNA/MacConkey bi-plate) and in the post-implementation period a single chromogenic agar/sheep blood bi-plate was set. Total numbers of cultures in both periods were collected as well as the number of cultures for which additional media were set based on gram stain results. Orders of subculture media in the laboratory information system were compared per positive cultures. Statistical analysis was performed in GraphPad Prism. Results In the pre- versus post-implementation periods, a total of 16,454 vs 17,782 general aerobic cultures and 50,680 vs 53,916 urine cultures were set, respectively. Only 3.6% of general cultures required additional media set based on gram stain. The difference in cost between pre- and post-implementation was approximately a two-thirds reduction in cost for general cultures and a 25% reduction for urine cultures, with a total savings of approximately $34,238 in media avoidance in the post-implementation period. The frequency of positive cultures requiring subculture was not significantly different between the pre- (average 36% of positive growth) and post-implementation (average 22% of positive growth) periods for both culture types and may actually have been reduced due to automation. Conclusions The implementation of reduced media concomitant with streaking via automation resulted in significant cost savings while not negatively impacting isolation, which in turn impacts culture turn around times. The automation may have reduced need for subculture, perhaps due to better isolation, thus subsequent analyses will explore this additional finding.

Thermal Threat Monitoring Using Thermal Image Analysis and Convolutional Neural Networks

Monitoring of the vital signs or environment of disabled people is currently very popular because it increases their safety, improves their quality of life and facilitates remote care. The article proposes a system for automatic protection against burns based on the detection of thermal threats intended for blind or visually impaired people. Deep learning methods and CNNs were used to analyze images recorded by mobile thermal cameras. The proposed algorithm analyses thermal images covering the field of view of a user for the presence of objects with high or very high temperatures. If the user’s hand appears in such an area, the procedure warning about the possibility of burns is activated and the algorithm generates an alarm. To achieve this effect, the thermal images were analyzed using the 15-layered convolutional neural network proposed in the article. The proposed solution provided the efficiency of detecting threat situations of over 99% for a set of more than 21,000 images. Tests were carried out for various network configurations, architecture and both the accuracy and precision of hand detection was 99.5%, whereas sensitivity reached 99.7%. The effectiveness of burn risk detection was 99.7%—a hot object—and the hand appeared simultaneously in the image. The presented method allows for quick, effective and automatic warning against thermal threats. The optimization of the model structure allows for its use with mobile devices such as smartphones and mobile thermal imaging cameras.

Immiscible non-Newtonian displacement flows in stationary and axially rotating pipes

We examine immiscible displacement flows in stationary and rotating pipes, at a fixed inclination angle in a density-unstable configuration, using a viscoplastic fluid to displace a less viscous Newtonian fluid. We employ non-intrusive experimental methods, such as camera imaging, planar laser-induced fluorescence (PLIF), and ultrasound Doppler velocimetry (UDV). We analyze the impact of key dimensionless numbers, including the imposed Reynolds numbers (Re, Re*), rotational Reynolds number (Rer), capillary number (Ca), and viscosity ratio (M), on flow patterns, regime classifications, regime transition boundaries, interfacial instabilities, and displacement efficiency. Our experiments demonstrate distinct immiscible displacement flow patterns in stationary and rotating pipes. In stationary pipes, heavier fluids slump underneath lighter ones, resulting in lift-head and wavy interface stratified flows, driven by gravity. Decreasing M slows the interface evolution and reduces its front velocity, while increasing Re* shortens the thin layer of the interface tail. In rotating pipes, the interplay between viscous, rotational, and capillary forces generates swirling slug flows with stable, elongated, and chaotic sub-regimes. Progressively, decreasing M leads to swirling dispersed droplet flow, swirling fragmented flow, and, eventually, swirling bulk flow. The interface dynamics, such as wave formations and velocity profiles, is influenced by rotational forces and inertial effects, with Fourier analysis showing the dependence of the interfacial front velocity's dominant frequency on Re and Rer. Finally, UDV measurements reveal the existence/absence of countercurrent flows in stationary/rotating pipes, while PLIF results provide further insight into droplet formation and concentration field behavior at the pipe center plane.

Process analytical technology based quality assurance of API concentration and fiber diameter of electrospun amorphous solid dispersions

In this study, a novel quality assurance system was developed utilizing Process analytical technology (PAT) tools and artificial intelligence (AI). Our goal was to monitor the critical quality attributes (CQAs) like drug concentration, morphology and fiber diameter of electrospun amorphous solid dispersion (ASD) formulations with fast at-line techniques. Doxycycline-hyclate (DOX), a tetracycline-type antibiotic was used as a model drug with 2-hydroxypropyl-β-cyclodextrin (HP-β-CD) as the matrix excipient. The water-based formulations were electrospun with high-speed electrospinning (HSES). Raman and NIR sensors and machine vision-based color measurement techniques were employed to accurately determine the drug concentration. Given that morphology can influence the solubility of the drug, a convolutional neural network (CNN)-based AI model was developed to examine this property and detect manufacturing defects. Additionally, the diameter of electrospun fibrous samples was measured using camera images and a trained AI model, enabling rapid analysis of fiber diameter with results similar to that of scanning electron microscopy (SEM). These methods and models demonstrate potential in-line analytical tools, offering rapid, cheap and non-destructive analysis of ASD formulations.

Unsupervised detection and mapping of sparks in the Electrochemical Discharge Machining (ECDM) process

Material removal in electrochemical discharge machining is caused by sparks generated in a tool immersed in an electrolytic solution. Being the primary machining agent in this non-contact machining process, mapping the locations of microscopic sparks is of great interest. The distribution of sparks around the tool surface could give insights into the machined hole properties like the size, surface finish, and depth as compared to the machining parameters such as applied voltage, tool size, rotation speed, and feed rate. This paper is focused on detecting sparks in photographs of the ECDM process captured using a high-speed camera. A novel approach of using a tri-planar reflective surface for capturing the location of sparks in 3D space using a 2D camera output is attempted. Traditional spark detection methods use neural network classifiers that need labeled data for training. This labeled data often comes from human intervention and contains inherent biases that could lead to misclassification. In this paper, an unsupervised spark detection methodology is demonstrated, which eliminates the need for human intervention and relies on the number of neighboring pixels detected in regions of interest (ROIs). The feasibility of using adaptive background modeling to classify thousands of images and identify the ones with sparks is demonstrated in this work. The masking technique combining effects of erosion followed by dilation is used to determine the exact boundaries of the spark contours in every image. Centroids for each of these contours are then transformed from the skewed coordinate system as observed in camera images, to a three-dimensional orthogonal coordinates system centered around the tool. The same procedure is repeated for various voltages to benchmark the distribution of sparks around a tool tip in an ECDM process.

Identification of High-redshift Galaxy Overdensities in GOODS-N and GOODS-S

We conduct a systematic search for high-redshift galaxy overdensities at 4.9 < z spec < 8.9 in both the Great Observatories Origins Deep Survey (GOODS)-N and GOODS-S fields using James Webb Space Telescope/Near-Infrared Camera (JWST/NIRCam) imaging from the JWST Advanced Deep Extragalactic Survey and JWST Extragalactic Medium-band Survey in addition to JWST/NIRCam wide field slitless spectroscopy from the First Reionization Epoch Spectroscopic Complete Survey. High-redshift galaxy candidates are identified using Hubble Space Telescope + JWST photometry spanning λ = 0.4–5.0 μm. We confirmed the redshifts for roughly a third of these galaxies using JWST spectroscopy over λ = 3.9–5.0 μm through identification of either Hα or OIIIλ5008 around the best-fit photometric redshift. The rest-ultraviolet magnitudes and continuum slopes of these galaxies were inferred from the photometry: the brightest and reddest objects appear in more dense environments and thus are surrounded by more galaxy neighbors than their fainter and bluer counterparts, suggesting accelerated galaxy evolution within overdense environments. We find 17 significant (δ gal ≥ 3.04, N gal ≥ 4) galaxy overdensities across both fields (seven in GOODS-N and 10 in GOODS-S), including the two highest redshift spectroscopically confirmed galaxy overdensities to date at zspec=7.954 and zspec=8.222 (representing densities around ∼6 and ∼12 times that of a random volume). We estimate the total halo mass of these large-scale structures to be 11.5≤log10Mhalo/M⊙≤13.4 using an empirical stellar mass-to-halo mass relation, which are likely underestimates as a result of incompleteness. These protocluster candidates are expected to evolve into massive galaxy clusters with log10Mhalo/M⊙≳14 by z = 0.

High-quality Extragalactic Legacy-field Monitoring (HELM) with DECam: Project Overview and First Data Release

High-quality Extragalactic Legacy-field Monitoring (HELM) is a long-term observing program that photometrically monitors several well-studied extragalactic legacy fields with the Dark Energy Camera (DECam) imager on the CTIO 4 m Blanco telescope. Since 2019 February, HELM has been monitoring regions within COSMOS, XMM-LSS, CDF-S, S-CVZ, ELAIS-S1, and SDSS Stripe 82 with few-day cadences in the (u)gri(z) bands, over a collective sky area of ∼38 deg2. The main science goal of HELM is to provide high-quality optical light curves for a large sample of active galactic nuclei (AGNs), and to build decades-long time baselines when combining past and future optical light curves in these legacy fields. These optical images and light curves will facilitate the measurements of AGN reverberation mapping lags, as well as studies of AGN variability and its dependencies on accretion properties. In addition, the time-resolved and coadded DECam photometry will enable a broad range of science applications from galaxy evolution to time-domain science. We describe the design and implementation of the program and present the first data release that includes source catalogs and the first ∼3.5 yr of light curves during 2019A–2022A.

New insights into the geological evolution history of Mare Fecunditatis

The Luna 16 probe returned 101 g of lunar regolith from the northeast of the Mare Fecunditatis in 1970. Studies on these samples were primarily conducted in the 1970s and 1980s, and limited scientific achievements were obtained due to available technology at that time. China received 1.5 g Luna 16 samples in 2023 and it is expected to conduct in-depth research in the near future. We conducted a thorough investigation on several fundamental issues in this region to provide a refined geological background in the paper. Firstly, a detailed division of the basaltic units within the Mare Fecunditatis was conducted based on the TiO2 content, and impact craters in each geological unit were mapped with the high-resolution Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) images. With the method of lunar surface dating from the crater size-frequency distribution, we found that the model ages of these basalt units range from 3.71 Ga to 3.31 Ga. Next, the thickness of the basalt within the Mare Fecunditatis is estimated with different types of craters, and local basalt thickness ranges from 3 m to 304 m from seven rim-completely-exposed craters in the mare. The rim-completely-exposed craters on the mare-highland boundary, the rim-partially-exposed craters in the mare, and rim-completely-buried craters in the maria also provide information on the thickness of local basalt. Finally, numerical simulations of the formation process of the largest young impact crater in this region, Langrenus crater, was conducted. The simulations show that the Langrenus crater could be formed by a 11.2-km-diameter asteroid hitting the lunar surface with a speed of ~10 km/s. The average thickness of the ejecta from the Langrenus crater is ~4.9 m near Luna 16 landing site, indicating that a significant fraction of Luna 16 samples might be the ejecta from the Langrenus crater. The simulations also reveal that the maximum shock pressure on the materials ejected to Luna 16 landing site can reach 92 GPa, and their maximum source depth is approximately 7.1 km. These interpretations can provide valuable information for further study of the Luna 16 samples and inferring the geological history of the Mare Fecunditatis.

Real-Time Decoding of Snapshot Compressive Imaging Using Tensor FISTA-Net.

Snapshot compressive imaging (SCI) cameras compress high-speed videos or hyperspectral images into measurement frames. However, decoding the data frames from measurement frames is compute-intensive. Existing state-of-the-art decoding algorithms suffer from low decoding quality or heavy running time or both, which are not practical for real-time applications. In this article, we exploit the powerful learning ability of deep neural networks (DNN) and propose a novel tensor fast iterative shrinkage-thresholding algorithm net (Tensor FISTA-Net) as a real-time decoder for SCI cameras. Since SCI cameras have an accurate physical model, we can trade training time for the decoding time by generating abundant synthetic data and training a decoder on the cloud. Tensor FISTA-Net not only learns a sparse representation of the frames through convolution layers but also reduces the decoding time and memory consumption significantly through tensor operations, which makes Tensor FISTA-Net an appropriate approach for a real-time decoder. Our proposed Tensor FISTA-Net obtains an average PSNR improvement of 0.79-2.84 dB (video images) and 2.61-4.43 dB (hyperspectral images) over the state-of-the-art algorithms, along with more clear and detailed visual results on real SCI datasets, Hammer and Wheel, respectively. Our Tensor FISTA-Net reaches 45 frames per second in video datasets and 70 frames per second in hyperspectral datasets, meeting the real-time requirement. Besides, the trained model occupies only a 12 -MB memory footprint, making it applicable to real-time Internet of Things (IoT) applications.

Novel multi-function setup comprising ultra-bright uniform source for spectral and radiometric calibrations of digital imaging sensors and multiple spectrometric applications

Abstract A new setup is being developed for calibrating CCD/CMOS sensors and cameras, and measuring spectrophotometric quantities in both point-wise and imaging modes. The setup includes a laser-driven light source (LDLS), double-grating monochromator, and an integrating sphere with specific design for two-dimensional (2D) spectrophotometric measurements of complex samples and spectral responsivity calibration of digital imaging sensors and cameras in the visible to near infrared (VIS-NIR) region. The setup can cover a spectral range from 250 nm to 2000 nm with relatively high radiance. The study presents optimization and validation work in the visible through NIR spectral range, up to 1000 nm. Preliminary experiments suggest improved accuracy and lower uncertainties for better metrological performance and sophisticated applications. The developed instrument promises a variety of novel applications while suppressing systematic errors and inaccuracies. The presented measurements results include the validation of 2D spectral reflectance/transmittance measurements in the 360-1000 nm spectral region using selected homogenous standards. Additionally provided are the findings of demonstration measurements for 2D spectral transmittance and the CCD camera’s spectral responsivity within the same spectral range.