Types Of Cameras Research Articles

Color‐Accurate Camera Capture with Multispectral Illumination and Multiple Exposures

AbstractCameras cannot capture the same colors as those seen by the human eye because the eye and the cameras' sensors differ in their spectral sensitivity. To obtain a plausible approximation of perceived colors, the camera's Image Signal Processor (ISP) employs a color correction step. However, even advanced color correction methods cannot solve this underdetermined problem, and visible color inaccuracies are always present. Here, we explore an approach in which we can capture accurate colors with a regular camera by optimizing the spectral composition of the illuminant and capturing one or more exposures. We jointly optimize for the signal‐to‐noise ratio and for the color accuracy irrespective of the spectral composition of the scene. One or more images captured under controlled multispectral illuminants are then converted into a color‐accurate image as seen under the standard illuminant of D65. Our optimization allows us to reduce the color error by 20–60% (in terms of CIEDE 2000), depending on the number of exposures and camera type. The method can be used in applications in which illumination can be controlled, and high colour accuracy is required, such as product photography or with a multispectral camera flash. The code is available at https://github.com/gfxdisp/multispectral_color_correction.

Web-Based Expert System Chatbot Determines The Best Cheap Camera For New Users Using The Vikor Method

In this technological era, a variety of new products have emerged, including in transportation, industry, health, entertainment and business. Cameras are an example of an increasingly diverse range of products, especially in mobile devices. However, the wide variety of cameras can be confusing. The VIKOR method is an approach to ranking the closest solution to the ideal solution in ranking. This is useful in situations where criteria conflict with each other, helping to find the optimal solution through a multicriteria ranking index. Especially in discrete decision making, VIKOR helps determine the ideal solution from conflicting alternatives. Testing this method is used in assessing cameras to help beginners choose the best type of camera based on user answers. By processing camera data through the VIKOR method and filter questions,

Authenticity markers of cultivated blueberries (Vaccinium sp.) and European wild blueberry (V. myrtillus) assessed by hyperspectral imaging and machine learning algorithms

The present study was undertaken to tackle food fraud in the food chain by developing a novel panel of analytical fingerprints to distinguish the fruit of bilberry (Vaccinium myrtillus), a wild European blueberry grown in Finland, from their commercial cultivated counterparts based on V. corymbosum or other originally American Vaccinium species. The analytical fingerprints were derived using hyperspectral imaging (HSI). The cultivated domestic blueberry samples from Finland (N = 188), those imported (N = 338), and domestic Finnish wild bilberry fruit (N = 247) were imaged using Specim IQ and FX17 HSI cameras. The fruit HSI data were then classified using seven machine learning model types. The supervised evolutionary feature selection algorithm was used to recognize the most separating subset of wavelengths (nm predictors, colors) between the fruit class labels, which were 1) wild bilberry fruit vs. cultivated blueberry fruit and 2) domestic cultivated blueberry fruit vs. imported cultivated blueberry fruit. Moreover, we ran 190,822 individual models and 54,137 predictor combinations to determine whether subsets or all wavelengths better distinguished fruit class labels. Overall, the best model type, the rbf kernel support vector machine (rbf-SVM), showed excellent prediction performance when classifying wild bilberries vs. cultivated blueberries with HSI data obtained using both camera types (4.1% and 3.2% holdout set errors). Also the rbf-SVM performed well when classifying domestic cultivated blueberries vs. those imported with HSI data obtained using an IQ camera (2.3% holdout set error). Our results showed that hyperspectral imaging could predict food fraud attempts concerning the Vaccinium fruit of different origin and species.

Estimating the Spectral Response of Eight-Band MSFA One-Shot Cameras Using Deep Learning

Eight-band one-shot MSFA (multispectral filter array) cameras are innovative technologies used to capture multispectral images by capturing multiple spectral bands simultaneously. They thus make it possible to collect detailed information on the spectral properties of the observed scenes economically. These cameras are widely used for object detection, material analysis, and agronomy. The evolution of one-shot MSFA cameras from 8 to 32 bands makes obtaining much more detailed spectral data possible, which is crucial for applications requiring delicate and precise analysis of the spectral properties of the observed scenes. Our study aims to develop models based on deep learning to estimate the spectral response of this type of camera and provide images close to the spectral properties of objects. First, we prepare our experiment data by projecting them to reflect the characteristics of our camera. Next, we harness the power of deep super-resolution neural networks, such as very deep super-resolution (VDSR), Laplacian pyramid super-resolution networks (LapSRN), and deeply recursive convolutional networks (DRCN), which we adapt to approximate the spectral response. These models learn the complex relationship between 8-band multispectral data from the camera and 31-band multispectral data from the multi-object database, enabling accurate and efficient conversion. Finally, we evaluate the images’ quality using metrics such as loss function, PSNR, and SSIM. The model evaluation revealed that DRCN outperforms others in crucial performance. DRCN achieved the lowest loss with 0.0047 and stood out in image quality metrics, with a PSNR of 25.5059, SSIM of 0.8355, and SAM of 0.13215, indicating better preservation of details and textures. Additionally, DRCN showed the lowest RMSE 0.05849 and MAE 0.0415 values, confirming its ability to minimize reconstruction errors more effectively than VDSR and LapSRN.

AI upscaling Supporting Image Alignment in Photogrammetric Reconstruction

Abstract. This research aims to investigate and analyze the contribution provided by preventive image processing using AI in the SfM data acquisition process. Specifically, the objective is to observe qualities and defects of "AI upscaling" integrated into the normal workflow of digital restitution, with the hypothesis that greater sharpness and resolution can lead to better alignments and better model generations. Other similar experiments have been carried out previously on the AI intervention in the photos to improve the alignments, but a generic procedure and with untrained public AI has not yet been experimented. In the first phase of the research, the most suitable AI upscaling method for the purpose is selected, processing example images and comparing them with the original and various settings of the AI parameters to maintain likeness. In the second phase, the procedure efficiency was evaluated in several ways: through the automatic evaluation of Agisoft Metashape's Image Quality, by directly comparing the resulting model with the standard digital reconstruction of the same objects, comparing with a model produced with the native AI upscaling of the return program, and finally comparing with a high-level survey without defects. This test was repeated for four case studies specifically diversified by several factors, including subject scale, level of detail, lighting, color, blur, and type of camera used. Clear improvements emerge in all cases of AI upscaling, even reaching successful reconstructions that would not have started with the canonical method. It can therefore be stated that this preventive upscaling procedure is decisive in cases of low quality or damaged datasets, while in cases of already qualitatively sufficient photos it can be a useful support for increasing the level of detail.

An Image-Based Sensor System for Low-Cost Airborne Particle Detection in Citizen Science Air Quality Monitoring.

Air pollution poses significant public health risks, necessitating accurate and efficient monitoring of particulate matter (PM). These organic compounds may be released from natural sources like trees and vegetation, as well as from anthropogenic, or human-made sources including industrial activities and motor vehicle emissions. Therefore, measuring PM concentrations is paramount to understanding people's exposure levels to pollutants. This paper introduces a novel image processing technique utilizing photographs/pictures of Do-it-Yourself (DiY) sensors for the detection and quantification of PM10 particles, enhancing community involvement and data collection accuracy in Citizen Science (CS) projects. A synthetic data generation algorithm was developed to overcome the challenge of data scarcity commonly associated with citizen-based data collection to validate the image processing technique. This algorithm generates images by precisely defining parameters such as image resolution, image dimension, and PM airborne particle density. To ensure these synthetic images mimic real-world conditions, variations like Gaussian noise, focus blur, and white balance adjustments and combinations were introduced, simulating the environmental and technical factors affecting image quality in typical smartphone digital cameras. The detection algorithm for PM10 particles demonstrates robust performance across varying levels of noise, maintaining effectiveness in realistic mobile imaging conditions. Therefore, the methodology retains sufficient accuracy, suggesting its practical applicability for environmental monitoring in diverse real-world conditions using mobile devices.

Myocardial Perfusion Scintigraphy in Diagnosic of Coronary Heart Disease

<i>Introduction: </i>Atherosclerosis plays a key role in the etiopathogenesis of cardiovascular diseases. Atherosclerosis is a generalized chronic inflammatory disease of the vascular wall, which results in anatomical and histological changes that, together with functional changes, lead to endothelial dysfunction, narrowing of the arterial lumen, and insufficient blood supply to the tissues. The shift in the incidence of cardiovascular diseases to younger age groups is alarming. The presence of microvascular changes in the myocardium is significant. The high incidence of cardiovascular diseases, especially ischemic heart disease, requires early diagnosis and modern treatment. <i>Aim: </i>The aim of the paper is to analyse and point out early diagnosis of coronary heart disease using nuclear medicine methods. <i>Set of examined patients and methodology: </i>Using nuclear medicine methods, we try to detect these changes in time and thus prevent the occurrence of acute coronary events. In nuclear cardiology, instead of a large-area scintillation detector, a new type of cardio-gamma camera "Discovery CZT 530c" based on the principle of semiconductor detectors began to be used. The abbreviation CZT stands for semiconductor composition (Cadmium-Zinc-Tellur). The set of respondents consisted of 4270 people examined in the years 2014-2016 by myocardial perfusion scintigraphy. There were 950 diabetic patients in the analysed group of the 4270 examined. <i>Results:</i> In the analysed group of 4270 respondents, 61% had negative findings and 39% had positive findings in terms of the presence of ischemic heart disease. In the group of diabetic patients, there was a negative finding in 28% of respondents and a positive finding in 72% of the respondents in the sense of positive ischemic heart disease. Using myocardial perfusion scintigraphy in the diagnosis of functional changes, we confirmed a great benefit in detecting early changes in coronary heart disease, including in the diagnosis of microvascular angina. <i>Conclusion:</i> Nuclear medicine methods are of great benefit for the diagnosis of small vessel disease, diabetic cardiomyopathy and cardiac autonomic neuropathy in patients with diabetes mellitus. The new type of cardio gamma camera "Discovery CZT 530c" allows more accurate assessment of myocardial perfusion abnormalities and at the same time the reduction of the radiopharmaceutical dose reduces the patient's radiation load by 50%.

Apple varieties and growth prediction with time series classification based on deep learning to impact the harvesting decisions

Apples are among the most popular fruits globally due to their health and nutritional benefits for humans. Artificial intelligence in agriculture has advanced, but vision, which improves machine efficiency, speed, and production, still needs to be improved. Managing apple development from planting to harvest affects productivity, quality, and economics. In this study, by establishing a vision system platform with a range of camera types that conforms with orchard standard specifications for data gathering, this work provides two new apple collections: Orchard Fuji Growth Stages (OFGS) and Orchard Apple Varieties (OAV), with preliminary benchmark assessments. Secondly, this research proposes the orchard apple vision transformer method (POA-VT), incorporating novel regularization techniques (CRT) that assist us in boosting efficiency and optimizing the loss functions. The highest accuracy scores are 91.56 % for OFGS and 94.20 % for OAV. Thirdly, an ablation study will be conducted to demonstrate the importance of CRT to the proposed method. Fourthly, the CRT outperforms the baselines by comparing it with the standard regularization functions. Finally, time series analyses predict the ‘Fuji’ growth stage, with the outstanding training and validation RMSE being 19.29 and 19.26, respectively. The proposed method offers high efficiency via multiple tasks and improves the automation of apple systems. It is highly flexible in handling various tasks related to apple fruits. Furthermore, it can integrate with real-time systems, such as UAVs and sorting systems. This research benefits the growth of apple’s robotic vision, development policies, time-sensitive harvesting schedules, and decision-making.

Multi-cameras calibration System Based Deep Learning Approach and Beyond: A Survey

The process of determining camera settings to deduce geometric attributes from recorded sequences is known as camera calibration. This process is essential in the fields of robotics and computer vision, encompassing both two-dimensional and three-dimensional applications. Traditional calibration methods, however, are time-consuming and require specific expertise. Recent endeavors have demonstrated that learning-based systems can replace the monotonous tasks associated with manual calibrations. Responses have been examined through a range of learning techniques, networks, geometric assumptions, and datasets. A thorough examination of camera calibration systems that rely on learning algorithms is offered in this paper, assessing their advantages and disadvantages. The primary categories of calibration presented are the regular pinhole camera model, distortion camera model, cross-sensor model, and cross-view model. These categories align with current research trends and have diverse applications. As there is no existing standard in this field, a large dataset of calibration has been created, which can be used as a public platform to assess the effectiveness of current methods. This collection consists of both artificially generated and genuine data, including images and videos obtained from various cameras in different locations. The difficulties faced will be analyzed, and alternative avenues for further research will be suggested in the next stage of this project. This survey represents the initial attempt to perform camera calibration using learning-based methods spanning a period of eight years. Our findings indicate that learning-based methods significantly reduce the time and expertise required for calibration while maintaining or improving accuracy compared to traditional methods. Specifically, our research demonstrates a calibration error reduction of up to 20% and speed improvements by a factor of three compared to traditional methods, as well as better adaptability to different camera types and environments.

NeRF-OR: neural radiance fields for operating room scene reconstruction from sparse-view RGB-D videos.

RGB-D cameras in the operating room (OR) provide synchronized views of complex surgical scenes. Assimilation of this multi-view data into a unified representation allows for downstream tasks such as object detection and tracking, pose estimation, and action recognition. Neural radiance fields (NeRFs) can provide continuous representations of complex scenes with limited memory footprint. However, existing NeRF methods perform poorly in real-world OR settings, where a small set of cameras capture the room from entirely different vantage points. In this work, we propose NeRF-OR, a method for 3D reconstruction of dynamic surgical scenes in the OR. Where other methods for sparse-view datasets use either time-of-flight sensor depth or dense depth estimated from color images, NeRF-OR uses a combination of both. The depth estimations mitigate the missing values that occur in sensor depth images due to reflective materials and object boundaries. We propose to supervise with surface normals calculated from the estimated depths, because these are largely scale invariant. We fit NeRF-OR to static surgical scenes in the 4D-OR dataset and show that its representations are geometrically accurate, where state of the art collapses to sub-optimal solutions. Compared to earlier work, NeRF-OR grasps fine scene details while training 30 faster. Additionally, NeRF-OR can capture whole-surgery videos while synthesizing views at intermediate time values with an average PSNR of 24.86dB. Last, we find that our approach has merit in sparse-view settings beyond those in the OR, by benchmarking on the NVS-RGBD dataset that contains as few as three training views. NeRF-OR synthesizes images with a PSNR of 26.72dB, a 1.7% improvement over state of the art. Our results show that NeRF-OR allows for novel view synthesis with videos captured by a small number of cameras with entirely different vantage points, which is the typical camera setting in the OR. Code is available via: github.com/Beerend/NeRF-OR .

EvHandPose: Event-Based 3D Hand Pose Estimation With Sparse Supervision.

Event camera shows great potential in 3D hand pose estimation, especially addressing the challenges of fast motion and high dynamic range in a low-power way. However, due to the asynchronous differential imaging mechanism, it is challenging to design event representation to encode hand motion information especially when the hands are not moving (causing motion ambiguity), and it is infeasible to fully annotate the temporally dense event stream. In this paper, we propose EvHandPose with novel hand flow representations in Event-to-Pose module for accurate hand pose estimation and alleviating the motion ambiguity issue. To solve the problem under sparse annotation, we design contrast maximization and hand-edge constraints in Pose-to-IWE (Image with Warped Events) module and formulate EvHandPose in a weakly-supervision framework. We further build EvRealHands, the first large-scale real-world event-based hand pose dataset on several challenging scenes to bridge the real-synthetic domain gap. Experiments on EvRealHands demonstrate that EvHandPose outperforms previous event-based methods under all evaluation scenes, achieves accurate and stable hand pose estimation with high temporal resolution in fast motion and strong light scenes compared with RGB-based methods, generalizes well to outdoor scenes and another type of event camera, and shows the potential for the hand gesture recognition task.

A neural network to retrieve cloud cover from all‐sky cameras: A case of study over Antarctica

AbstractWe present a new model based on a convolutional neural network (CNN) to predict daytime cloud cover (CC) from sky images captured by all‐sky cameras, which is called CNN‐CC. A total of 49,016 daytime sky images, recorded at different Spanish locations (Valladolid, La Palma, and Izaña) from two different all‐sky camera types, are manually classified into different CC (oktas) values by trained researchers. Subsequently, the images are randomly split into a training set and a test set to validate the model. The CC values predicted by the CNN‐CC model are compared with the observations made by trained people on the test set, which serve as reference. The predicted CC values closely match the reference values within 1 oktas in 99% of the cloud‐free and overcast cases. Moreover, this percentage is above 93% for the rest of partially cloudy cases. The mean bias error (MBE) and standard deviation (SD) of the differences between the predicted and reference CC values are calculated, resulting in oktas and oktas. The MBE and SD are also represented for different intervals of measured aerosol optical depth and Ångström exponent values, revealing that the performance of the CNN‐CC model does not depend on aerosol load or size. Once the model is validated, the CC obtained from a set of images captured every 5 min, from January 2018 to March 2022, at the Antarctic station of Marambio (Argentina) is compared against direct field observations of CC (not from images) taken at this location, which is not used in the training process. As a result, the model slightly underestimates the observations with an MBE of 0.3 oktas. The retrieved data are analyzed in detail. The monthly and annual CC values are calculated. Overcast conditions are the most frequent, accounting for 46.5% of all observations throughout the year, rising to 64.5% in January. The annual mean CC value at this location is 5.5 oktas, with a standard deviation of approximately 3.1 oktas. A similar analysis is conducted, separating data by hours, but no significant diurnal cycles are observed except for some isolated months.

A new method with C-means segmentation for non-uniform image coordinate system definition in panoramic imaging employing Ladybug2 camera

A panorama ensures a stunning wide-angle field of view up to 360° representation of a scene, exceeding the limits of a normal photograph. Panoramic cameras satisfy the single-viewpoint characteristic. There are several types of panoramic cameras for 360-degree imaging. Multi-camera panoramic imaging systems pose a difficulty in obtaining a single projection center for the cameras. In a variety of practical implementations of panoramic cameras, it is possible to calculate three-dimensional coordinates from a panoramic image, especially using the Direct Linear Transformation (DLT) method. In this study, not only a defining method of the non-uniform image coordinate system is presented by utilizing the C-Means algorithm for a single panoramic image, captured with a Ladybug2 panoramic camera in a panoramic calibration room but also the use of an elliptical panoramic projection coordinate system is defined by the Singular Value Decomposition method in a panoramic view. The results of the suggested method have been compared with the DLT algorithm for a single panoramic image which defined a conventional photogrammetric image coordinate system. It has been observed that the proposed method provides more accurate results for the 3D coordinate definition.

Comparative Evaluation of the Performance of a Mobile Device Camera and a Full-Frame Mirrorless Camera in Close-Range Photogrammetry Applications.

The comparative evaluation of the performance of a mobile device camera and an affordable full-frame mirrorless camera in close-range photogrammetry applications involves assessing the capabilities of these two types of cameras in capturing images for 3D measurement purposes. In this study, experiments are conducted to compare the distortion levels, the accuracy performance, and the image quality of a mobile device camera against a full-frame mirrorless camera when used in close-range photogrammetry applications in various settings. Analytical methodologies and specialized digital tools are used to evaluate the results. In the end, generalized conclusions are drawn for using each technology in close-range photogrammetry applications.

OPTICAL DEEP LEARNING LANDMINE DETECTION BASED ON LIMITED DATASET OF AERIAL IMAGERY

Landmine detection is one of the most innovative applications of unmanned aerial vehicles that became possible due to rapid development of both aerial vehicles equipped by different optical cameras and sensors using different physical principles, and object classification and detection methods, including machine learning and especially deep learning. Optical detection is an essential part of the overall landmine detection process that can be performed either separately or in combination with data processing from other types of cameras or sensors. The development of deep convolutional neural networks has dramatically changed the landscape of optical detection by making them de-facto choice number one for the majority of object classification, detection and segmentation tasks. However, the deterrent factor in the case of landmine detection is limited availability of appropriate data for training that different researchers try to overcome in different ways. The assessment of necessary amount of training data for any particular object detection problem still remains an experimental task. Despite several years of development in this area, still there is a shortage of research based on real landmine imagery obtained from unmanned aerial vehicles, so currently any public effort in this direction is valuable and works as an inspiration for new researchers. This paper describes such a study, namely its first iteration in which popular open-source tools are used to build detection pipeline and estimation of their efficiency is done using limited amount of data. It is shown that the problem of limited amount of training data can be effectively overcome by data augmentation and iterational process of training optical landmine detector is demonstrated. The effectiveness of open-source tools and libraries for neural networks training, object detection and dataset preparation is also demonstrated.

A review of occupancy sensing technologies and approaches in smart buildings

Comprehensive occupancy information in smart buildings has become more imperative in order to develop new control strategies in energy management systems. Several techniques can be used to collect occupancy information considering accurate sensing techniques, such as passive infrared (PIR), carbon dioxide (CO2) and different types of cameras (i.e., thermal, or optical cameras). Recent studies show the usefulness of integrating occupancy information into energy management systems to reduce energy consumption while maintaining the occupants’ comfort. The purpose of this work is to elaborate a comprehensive review on occupancy detection systems in smart buildings. This study presents a set of comparison standards including methods, occupancy resolution, type of buildings and sensors. A classification of different approaches, which can be implemented and integrated into the building management system for detecting indoor occupancy, is introduced. Summary and discussions are given by highlighting the usefulness of machine learning for enabling predictive control of active systems in smart buildings.

P-256 AI-enhanced precision: unveiling the future of blastocyst quality assessment

Abstract Study question Can artificial intelligence (AI) effectively assess various regions of embryos to estimate the quality of human embryos captured through an optical microscope? Summary answer Artificial Intelligence excels in analyzing diverse embryonic regions based on morphology, ensuring precise grade predictions, assessing human embryos via optical microscopes. What is known already In recent research, the integration of AI and computer vision has marked significant strides in embryo selection, providing precise predictions of clinical pregnancy based on images of human embryos, particularly at Day 5. This study extends its findings by incorporating techniques that exhibit resilience to variations in camera and microscope types. Notably, the AI algorithms underwent rigorous testing on double-blind datasets, comprising optical microscopic images sourced from Optical microscope systems across diverse regions such as India, Europe, the UK, the US, and Australia. These results signify a promising advancement in the standardization and applicability of AI-assisted embryo assessment methodologies. Study design, size, duration Approximately 11824 static 2D images of Day 5 blastocysts with related pregnancy, preimplantation genetic testing for aneuploidy (PGT-A) outcomes, demographic and clinic geographical location information have been collected. Images were divided into three groups: training, validation and blind test sets. An AI model was trained on 3473 embryo images, validated on 1184 and tested on further blind set of 1253 images from a separate clinics and demographsic. Gardner based grading system was used. Participants/materials, setting, methods Dataset included optical-microscope images from fertility patients across 25 IVF laboratories in six countries was utilized to train, validate, and test the AI. Dataset was graded by 9 embryologists and final grade was decided based on max-voting. The identity of participants was kept secret and did not associate with used data. Assessment encompassed accuracy, distribution, and robustness concerning camera/microscope type. The dataset spanned various embryo development stages, excluding frozen, degenerated, non-blastocyst embryos. Main results and the role of chance The study distinguishes itself by incorporating data from diverse environments, enhancing generalizability. Leveraging transformer-based attention models ensures meticulous focus on regions relevant to the Gardner grading system, ensuring accurate predictions akin to human judgment and minimizing errors prone to subjectivity. AI’s predictive accuracy on blind dataset for expansion is 82.18%, for ICM is 76.53%, and for trophectoderm is 77.53%, surpassing previous studies. The model demonstrates specificity 79.16%, sensitivity of 85.95% and accuracy of 82.44% in estimating embryo quality. Direct application on embryoscope images is hindered by differing morphology, excluding the embryo part. However, employing segmentation and applying models yields comparable performance with approx. 2.87% drop in accuracy. These findings suggest that the proposed method, with AI trained on a globally diverse dataset, establishes a generalizable AI robust to variations in camera type and focal settings. Limitations, reasons for caution Data collected for this solution has been limited to mainly static images. The study’s focus on good-quality Day 5 blastocyst embryos might limit generalizability to other stages. The dataset’s global diversity may not fully capture demographic nuances. Ongoing AI advancements may necessitate continual updates for relevance and accuracy. Wider implications of the findings Broader implications of the findings suggests transformative potential for AI in embryo quality assessment. The methodology’s success in diverse environments and robustness to variations in camera types underscore its applicability on global scale. This could lead to standardized, automated, and precise embryo evaluations, offering significant advancements in assisted reproductive technology. Trial registration number not applicable

Calibration and characterization of optical and x-ray streak cameras using a Ti:Sapphire laser system.

Optical and x-ray streak cameras are used to study transient phenomena, particularly in the high-energy density physics regime. The Orion laser facility employs many different types of streak cameras, which are used to collect data on laser-plasma interactions as well as to verify the temporal profile and timing between the multiple Orion beamlines. Streak cameras are complex devices with very precise timing associated with them, which can often malfunction, resulting in the loss of shot data. Since Orion is a kJ-class Nd:glass laser system, it is not optimal to try and fault-find using Orion shots since this is both time consuming and prohibitively expensive. To enable the facile set-up, fault-finding, timing-in, and in-house calibration of Orion optical and x-ray diagnostics, a single laboratory system has been commissioned to provide an adjustable stimulus to streak cameras and other Orion short pulse diagnostics (such as pulse dilation photomultiplier tubes). The system comprises a Ti:Sapphire laser system capable of generating 400nm or 266nm laser pulses of duration less than 0.1ps; an optical system to deliver single pulses, pulse pairs, or a train of pulses; and timing electronics to synchronize the streak cameras with the laser and to record output data. The system can operate at Hertz repetition rates rather than the sub-mHz rate of the Orion laser. We present the commissioned system and results from initial testing of both optical and x-ray streak cameras used on Orion laser-plasma experiments.

Evolution of flight control systems and aerial photography in unmanned agricultural aircraft

The advancement of UAV technologies has enabled the automated capture of photos and videos, eliminating the need for manual intervention in flight control. (Research purpose) This research aims to conduct a retrospective analysis of the evolution offlight control systems and the development of aerial photography equipment for agricultural land, covering the period from the mid-19th century to present. (Materials and methods) A systematic literature review was conducted using the historical-analytical method. The paper examines original works by both domestic and international authors, including monographs, scientific journals, conference proceedings, museum exhibitions, photographic archives, and open-source software code. (Results and discussion) The paper identifies six distinct phases in the development of aerial photography and flight control systems. The classification is based on key innovations in camera types, control systems, and aircraft designs. Each phase highlights the predominant cameras, control systems, and aircraft utilized for agricultural applications. (Conclusions) Over the past 165 years, notable changes have occurred in aerial photography parameters, including the type of photographic material, image spatial and spectral resolution, camera weight and mounting, shutter types and their mechanisms, inertial control units, integrated GPS/GLONASS receivers, and light sensors. In terms of flight control systems for UAVs, significant developments over the last 106 years include variations in flight control types, the number offlight-stabilizing sensors, obstacle detection systems, size of the flight control units, flight modes, and takeoff/landing techniques, along with interfaces for attachments. It is anticipated that future intellectualization and miniaturization of flight control systems will not only boost UAV performance but also reduce the economic costs associated with the aerial monitoring of agricultural biological assets.

Is there a difference in quality of photographs obtained using smartphones, compared to the photographs obtained via digital single-lens reflex camera?

PurposeThe purpose of this study was to compare the quality of photographs obtained with three different cameras: I-phone, Samsung, and DSLR, as assessed by Oral and Maxillofacial Surgeons (OMSs). MethodsThis was an anonymous online survey study. The study population consisted of OMSs in New Jersey, New York, Pennsylvania, and Massachusetts who were members of their state societies. Primary outcome variable was ratings on the quality of extraoral and intraoral photographs obtained with three different cameras. Predictor variable was type of camera used to obtain the photographs: I-Phone, Samsung, and DSLR camera. Descriptive statistics were computed. Spearman test was utilized to analyze correlation between the outcome variables. ResultsMajority of respondents were from full time private practice (n = 84, 77.06%), followed by full time academia (n = 12, 11.01%). Spearman test revealed negligible correlation between extraoral and intraoral photographs on the best quality rating (0.15, p=0.13). Negligible correlation between extraoral and intraoral photographs were noted on the second best (0.15, p=0.11), and least quality rating (0.09, p=0.32). ConclusionStudy results revealed that there is no difference between the quality of photographs obtained using smartphones, compared to the photographs obtained via DSLR camera. Future discussions on an ideal application on smartphones would allow for consent and transfer of high-quality photographs via a secure messaging system between healthcare providers to address ethical concerns of confidentiality.