Performance and Early Results from the Earth Surface Mineral Dust Source Investigation (EMIT) Imaging Spectroscopy Mission

The Earth Surface Mineral Dust Source Investigation (EMIT) acquires new observations of the Earth from a state-of-the-art, optically fast F/1.8 visible to short wavelength infrared imaging spectrometer with high signal-to-noise ratio and excellent spectroscopic uniformity. EMIT was launched to the International Space Station from Cape Canaveral, Florida, on July 14, 2022 local time. The EMIT instrument is the latest in a series of more than 30 imaging spectrometers and testbeds developed at the Jet Propulsion Laboratory, beginning with the Airborne Imaging Spectrometer that first flew in 1982. EMIT's science objectives use the spectral signatures of minerals observed across the Earth's arid and semi-arid lands containing dust sources to update the soil composition of advanced Earth System Models (ESMs) to better understand and reduce uncertainties in mineral dust aerosol radiative forcing at the local, regional, and global scale, now and in the future. EMIT has begun to collect and deliver high-quality mineral composition determinations for the arid land regions of our planet. Over 1 billion high-quality mineral determinations are expected over the course of the one-year nominal science mission. Currently, detailed knowledge of the composition of the Earth's mineral dust source regions is uncertain and traced to less than 5,000 surface sample mineralogical analyses. The development of the EMIT imaging spectrometer instrumentation was completed successfully, despite the severe impacts of the COVID-19 pandemic. The EMIT Science Data System is complete and running with the full set of algorithms required. These tested algorithms are open source and will be made available to the broader community. These include calibration to measured radiance, atmospheric correction to surface reflectance, mineral composition determination, aggregation to ESM resolution, and ESM runs to address the science objectives. In this paper, the instrument characteristics, ground calibration, in-orbit performance, and early science results are reported. © 2023 IEEE.

Comparative Analysis of Medical Imaging Techniques Used for the Detection of Thyroid Gland with an Emphasis on Thermogram

In today's scenario, every human being in the world is scared of the COVID-19 pandemic, and everyone in the world want early medication for COVID-19. So in this paper, a study of numerous medical imaging techniques used for detection of thyroid gland in the human being in different stages of human life is presented. Early thyroid illness discovery is that the main necessary in growing the speed of diagnosing cure and survival of the affected creature. There are a various medical imaging techniques used to detect thyroid diseases in human being. Some techniques are used to diagnose stages of thyroid cancer in humans. This paper is used to explain the procedure for the diagnosis of images, investigation of images, pros, cons, and limitations of imaging techniques. A comparative study of various medical imaging techniques explains the Thermogram image is the noninvasive system that detects the relative temperature variations in patients form thyroid diseases. In this paper, survey of the various algorithm implemented is studied for thermography, MRI, ultrasound, and mammography from the literature review and it is observed that detection of thyroid abnormalities using different techniques not only decides many factors such as segmentation of the region of thyroid gland, image quality, and extraction features and classifiers. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Automated Thermal Screening for COVID-19 Using Machine Learning

In the last two years, millions of lives have been lost due to COVID-19. Despite the vaccination programmes for a year, hospitalization rates and deaths are still high due to the new variants of COVID-19. Stringent guidelines and COVID-19 screening measures such as temperature check and mask check at all public places are helping reduce the spread of COVID-19. Visual inspections to ensure these screening measures can be taxing and erroneous. Automated inspection ensures an effective and accurate screening. Traditional approaches involve identification of faces and masks from visual camera images followed by extraction of temperature values from thermal imaging cameras. Use of visual imaging as a primary modality limits these applications only for good-lighting conditions. The use of thermal imaging alone for these screening measures makes the system invariant to illumination. However, lack of open source datasets is an issue to develop such systems. In this paper, we discuss our work on using machine learning over thermal video streams for face and mask detection and subsequent temperature screening in a passive non-invasive way that enables an effective automated COVID-19 screening method in public places. We open source our NTIC dataset that was used for training our models and was collected at 8 different locations. Our results show that the use of thermal imaging is as effective as visual imaging in the presence of high illumination. This performance stays the same for thermal images even under low-lighting conditions, whereas the performance with visual trained classifiers show more than 50% degradation. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Non-fever COVID-19 Detection by Infrared Imaging

This study proposed an infrared image-based method for febrile and non-febrile people screening to comply with the society needs for alternative, quick response, and effective methods for COVID-19 contagious people screening. The methodology consisted of: (i) Developing a method based on the face infrared imaging for early COVID-19 detection in people with and without fever;(ii) Recruiting 1206 emergency room (ER) patients to develop an algorithm for general application of the method, and (iii) Testing the method and algorithm effectiveness in 2558 cases (RTqPCR tested for COVID-19) from 227,261 workers evaluations in five different countries. Artificial intelligence was used with a convolutional neural network (CNN) to develop the algorithm that took face infrared images as input and classified the tested individuals into three groups: fever (high risk), non-febrile (medium risk), and without fever (low risk). The results showed that suspicious and confirmed COVID-19 (+) cases characterized by temperatures below the 37.5 °C fever threshold were identified. Also, average forehead and eye temperatures greater than 37.5 C were not enough to detect fever similarly to the proposed CNN algorithm. Most RT-qPCR confirmed COVID-19 (+) cases found in the 2558 cases sample (17 cases/89.5%) belonged to the CNN selected non-febrile COVID group. The COVID-19 (+) main risk factor was to be in the non-febrile medium-risk group, compared with age, diabetes, high blood pressure, smoking and others. In sum, the proposed method was shown to be a potentially important new tool for COVID-19 (+) people screening for air travel and public places in general. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Spatio-temporal variations of night-time lights at early stages of the COVID-19 epidemic in the United States

The COVID-19 epidemic swept the world and continued to spread. Without effective medical treatments and vaccine during the early stage of the pandemic, local governments in various countries had to lock down cities and adopt non-pharmaceutical interventions (NPIs), such as the stay-at-home order, social distancing, and so on. NPIs against the COVID-19 epidemic have significantly changed socioeconomic activities in cities. However, characteristics and patterns of urban socio-economic activities under this influence are still unclear. Benefiting from the development of earth observation technologies, such large-scale changes in socioeconomic activities are enough to be captured by satellites through remotely sensed night-time lights (NTL). In this study, we selected 20 major cities in the United States including New York, Chicago and Los Angeles to analyze spatio-temporal variations of NTL caused by the lockdown of cities. The first round of COVID-19 epidemic occurred in the United States in mid-March 2020. Since March 2020, American cities have successively issued stay-at-home orders, but there are differences in the time and strictness of policy implementation. Large cities have a higher population density and a higher intensity of social activities, so they are more susceptible to infectious diseases. The diversity of lockdown dates and strictness of lockdowns in cities in the United States are conducive to investigating the spatio-temporal variations of NTL. We acquired monthly averaged NPP VIIRS products of February, March and April, 2020, which are from Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (NPP). We further analyzed the spatial pattern, distance decay and disparities in land use types of changes in NTL. Results show that NTL generally dimmed by 5-8% in U.S. cities caused by the lockdown of cities. There are 6 cities where the luminous brightness has dropped by more than 10%: Chicago, Dallas, Denver, Detroit, Minneapolis, and St. Louis. Among them, Minneapolis has the largest decrease in luminous brightness, with a decrease of about 40% in March. The spatial change of NTL shows obvious "core-periphery" pattern that the reduction of NTL declines with the distance from the city center. This is mainly because the central area of the city is a concentrated commercial area. After the closure of the city, commercial activities have dropped significantly, resulting in an obvious reduction in NTL around city centers. The reduction of NTL varies among diverse urban land use types. In New York, NTL decreased the most on land for residence and aviation facilities by 12% and 11%, respectively. In Chicago, NTL generally decreased by 20% in all types of urban land, and NTL recovered after one month of the lockdown of cities in other urban land except sports facilities land. This study only analyzes the spatio-temporal changes of NTL. In the future, it can be combined with multi-source data to explain the driving force of NTL changes. Nighttime light remote sensing effectively reflects urban socio-economic dynamics with an important application in monitoring and assessing socio-economic impacts of emergencies. © 2022 National Remote Sensing Bulletin. All rights reserved.

Thermal Screening Solution Using Infrared Thermography and Closed-Circuit Television

Closed-circuit television camera (CCTV) and thermal imaging devices are used to detect febrile individuals entering establishments for Coronavirus 2019 (COVID-19) containment. Real-time tracking in post-COVID is manually checked by security personnel, which has risks of less efficiency due to human errors, as advance thermal cameras are unaffordable for some business owners. The main goal is converting an installed CCTV interfaced with infrared sensor to develop an economical thermal screening system with acoustic alarm. In this project, the colored and heatmap images transmitted from the thermal camera were processed through OpenCV. A calibration method was also performed to validate the temperature reading from the thermal camera. The project comes with graphical user interface (GUI) connected into a database, which visually tracks individuals exhibits elevated body temperature. The performance of the system shows above 95% accuracy upon conducting an inexpensive calibration check. The significance of this project is highlighting the effective mitigation of virus spread which offers safe and contactless analysis of potential individuals showing early symptoms of COVID-19. Additional features can be added for future work such as facemask detector, multiple thermal camera setup, and Login Options making the device and application exclusively for business owners. © 2022 IEEE.

Analysis of the influence of external conditions on temperature readings in thermograms and adaptive adjustment of the measured temperature value

Measuring human temperature is a crucial step in preventing the spread of diseases such as COVID-19. For the proper operation of an automatic body temperature measurement system throughout the year, it is necessary to consider outdoor conditions. In this paper, the effect of atmospheric factors on facial temperature readings using infrared thermography is investigated. A thorough analysis of the variation of facial temperature with the prevailing atmospheric conditions was carried out using recordings collected over two years and compared with air temperature values at 1 hour accuracy. A method that takes account of outdoor conditions on temperature readings was proposed. We developed a correction curve with coefficients values based on an analysis of the recordings of people entering the building. Such a method will allow an effective real-time fever screening in public places. © 2022 IEEE.

Quantifying Nighttime Light Changes in KL, Putrajaya, and Selangor from Human Activities Impact of Pandemic COVID-19

The Malaysian government has implemented extensive physical distancing measures to prevent and control virus transmission in response to the pandemic COVID-19. Particularly in the Kuala Lumpur, Putrajaya, and Selangor regions, quantitative, spatially disaggregated information about the population-scale shifts in an activity caused by these measures is extremely rare. A next-generation space-borne low-light imager called the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS-DNB) can monitor changes in human activities. However, a cross-country examination of COVID-19 replies has not yet utilized the potential. To understand how communities have complied with COVID-19 measures in the two years since the pandemic. This study aims to quantify nighttime light (NTL) before and during COVID-19 using multi-year (2019-2021) monthly time series data derived from VIIRS nighttime light (NTL) products covering urban areas in Selangor, Putrajaya, and Kuala Lumpur. The NTL was processed in the Google Earth Engine (GEE) platform. NTL data has documented the link between curfew orders, nationwide closures, and the uneven response to control measures between and within the areas. Our findings demonstrate satellite images from VIIRS DNB can examine public opinion regarding national curfews and lockdowns, laws, and the sociocultural elements that influence their effectiveness, particularly in unstable and sparsely populated areas. Statistical T-test analysis revealed that the p-value for Kuala Lumpur was 0.01687, and less than 0.05 meant a significant difference between NTL reduction before and during COVID-19. Petaling showed a p-value of 0.0034 and less than 0.05, indicating a significant difference between NTL reduction before and during COVID-19. However, for area Putrajaya, the p-value is 0.0957, and more than 0.05 means there is no significant difference between the reduction of NTL before and during COVID-19. © 2022 IEEE.

AN ANALYSIS FOR THE WORK RESUMPTION UNDER THE COVID-19 EPIDEMIC BASED ON VIIRS-DNB NIGHTTIME LIGHTS IN CHINA

Timely and effective quantitative measurement of enterprises' offline resumption of work after public emergencies is conducive to the formulation and implementation of relevant policies. In this paper, we analyze the level of work resumption after the coronavirus disease 2019 (COVID-19)-influenced Chinese Spring Festival in 2020 with National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) daily data. The results demonstrate that COVID-19 has seriously affected the resumption of work after the Spring Festival holiday. Since February 10th, work has been resuming in localities. By late March, the work resumption indexes of most cities exceeded 50%, and Shanghai and Nanjing even had achieved complete resumption of work. Our method effectively estimates the resumption of work, which provides a scientific basis for local governments to formulate subsequent resumption policies. © 2021 IEEE

Detecting Essential Landmarks Directly in Thermal Images for Remote Body Temperature and Respiratory Rate Measurement With a Two-Phase System

Infrared thermographs (IRTs, also called thermal cameras) have been used to remotely measure elevated body temperature (BT) and respiratory rate (RR) during infectious disease outbreaks, such as COVID-19. To facilitate the fast measurement of BT and RR using IRTs in densely populated venues, it is desirable to have IRT algorithms that can automatically identify the best facial locations in thermal images to extract these vital signs. The IEC 80601-2-59:2017 standard suggests that the regions medially adjacent to the inner canthi of the eyes are robust BT measurement sites. The nostril regions, on the other hand, are often used for RR estimation. However, it is more difficult to automatically identify inner canthi and nostrils in thermal images than in visible-light images, which are rich with exploitable features. In this paper, a unique system that can detect inner canthi and outer nostril edges directly in thermal images in two phases is introduced. In Phase I, original thermal images were processed in four different ways to enhance facial features to facilitate inner canthus and nostril detection. In Phase II, landmarks of the inner canthi and outer nostril edges were detected in two steps: (1) face detection using the Single Shot Multibox Detector (SSD) and (2) facial landmark detection to locate the inner canthi and outer nostril edges. The face detection, facial landmark detection, and overall system accuracies were evaluated using the intersection over union, normalized Euclidean distance, and success detection rate metrics on a set of 36 thermal images collected from 12 subjects using three different IRTs. Additional validation was performed on a subset of 40 random thermal images from the publicly available Tufts Face Database. The results revealed that the processed images - referred to as ICLIP images - yielded the highest landmark localization accuracy from the four types of processed thermal images, verifying that the system can automatically and accurately estimate the inner canthus and nostril locations in thermal images. The proposed system can be applied in IRT algorithms to provide reliable temperature measurements and RR estimates during infectious disease outbreaks. © 2013 IEEE.

Studying the accuracy of infrared thermography for measuring core body temperature

The purpose of this study was to investigate the accuracy of infrared thermography for measuring body temperature. We compared a commercially available infrared thermal imaging camera (FLIR One) with a medical-grade oral thermometer (Welch-Allyn) as a gold standard. Measurements using the thermal imaging camera were taken from both a short distance (10cm) and long distance (50cm) from the subject. Thirty young healthy adults participated in a study that manipulated body temperature. After establishing a baseline, participants lowered their body temperature by placing their feet in a cold-water bath for 30 minutes while consuming cold water. Feet were then removed and covered with a blanket for 30 minutes as body temperature returned to baseline. During the course of the 70-minute experiment, body temperature was recorded at a 10-minute interval. The thermal imaging camera demonstrated a significant temperature difference from the gold standard from both close range (mean error: +0.433°C) and long range (mean error: +0.522°C). Despite demonstrating potential as a fast and non-invasive method for temperature screening, our results indicate that infrared thermography does not provide an accurate measurement of body temperature. As a result, infrared thermography is not recommended for use as a fever screening device. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

The temperature difference method for screening patients with COVID-19 fever symptoms

Coronavirus disease (COVID-19), caused by the SARS-CoV-2 virus, is a potentially fatal disease of global public health concern. Fever has been reported to be a common clinical symptom in COVID-19 and current CDC recommendations for mitigation of community COVID-19 transmission include temperature screening, so prompting widespread temperature screening across multiple sectors, including hospitals, office buildings and airports. The need for no-contact and rapid measurement of body temperature during the COVID-19 pandemic emergency has led to the widespread use of thermal imaging cameras. However, the body temperature measurement is also disturbed by the environment factors, including ambient temperature, background light etc. When the ambient temperature is low, the temperature of the patient will also be low. It was difficult to screen the fever patients by using the absolute temperature criteria, and it often result in missing detection. In order to solve this problem, this paper proposed a method of screening COVID-19 symptom fever patients by the body temperature difference detection. The temperature difference detection method combined the temperature measurement of the infrared imaging camera and the visible camera face recognition. This method will eliminate environmental interference and equipment errors, to reduce the probability of the fever missed detection. © Published under licence by IOP Publishing Ltd.

GMPPS Based Face Mask Detection of Infrared Thermal Image for Ultra-Small Training Data Set

Wearing face masks is an effective measure to prevent COVID-19. Infrared thermal image based temperature measurement and identity recognition system has been widely used in many large enterprises and universities in China, so it is totally necessary to research the face mask detection of thermal infrared imaging efficiently. A novel method named as GMPPS was proposed in this paper, which Gray Histogram Equalization and Gaussian Filter was adopted to pre-processing the image, then Max-pooling and fast PCA was used to extract the feature, and SVM was employed to classify the data finally. The experiment was performed on 3 different scales of training data set, the results showed that the average face mask recognition of the method can be up to 99.638% for 800 test samples only with 32 training samples, and the average test time overhead for one sample is 1. 0749s, and the method's performance is very robust. © 2021 IEEE.

The Influence of High Temperature Weather on Human Body Temperature Measurement by Infrared Thermal Imaging Thermometer

Infrared thermography thermometer is a non-contact temperature measuring equipment, which is widely used in the stage of large-scale epidemic of the covid-19 pandemic. It is used for rapid screening of human body temperature in crowded places at the entrance and exit of airports, docks, shopping malls, stations and schools. But when the outdoor temperature approaches or exceeds the body temperature in summer, can this method of measuring body surface temperature by infrared thermal imager be used as a standard for screening fever? Under the condition of high temperature in summer, the field experiment of measuring body temperature by infrared thermal imager is carried out, the experimental results are analyzed. We recommend the use of relative temperature difference for screening patients with fever. © 2021 Institute of Physics Publishing. All rights reserved.

Research on Intelligent Fingertip Blood Collection Robot

At present, fingertip blood sampling is mainly done manually by medical workers. Under the COVID-19 epidemic, medical workers are easily infected, in addition, the finger needs to be squeezed to increase the amount of bleeding during the blood collection process, which will cause the cell fluid to enter the blood and cause the test results to be inaccurate. This paper presents a kind of design about an intelligent fingertip blood sampling robot. We get the finger vein image through the near-infrared imaging module, and select the vein intersection area as the blood collection point after image segmentation, which will be helpful in improving the amount of bleeding. We use the laser to guide the end of the blood collection robot puncture needle and blood collection vessels to achieve rapid and accurate blood puncture and blood collection operation. The experimental results show that the maximum deviation between the blood sampling needle and the blood sampling point does not exceed 0.15mm and the longest time from fingertip blood sampling point selection to guide the blood sampling needle to the blood sampling point is less than 9.8 seconds. © 2021 ACM.