Every year, metallurgical plants emit hundreds of thousands of tons of harmful substances into the atmosphere. The remote sensing of flue gases from the chimneys of metallurgical plants is an urgent task for both industrial enterprises themselves and the enviromental control systems of nearby settlements. In this study, based on the results of the remote optical monitoring of emissions from chimneys of metallurgical plants of the PJSC “MMC ‘Norilsk Nickel’s’,” Polar Division, the concentration of sulfur dioxide in flue gases is estimated. The measurements are carried out using infrared (IR) Fourier transform spectrometers operating in the 7–13 µm range with a spectral resolution of 4 cm–1. A new technology for remote optical sensing in the passive mode of flue gases from metallurgical plants is proposed, including measurements both on cross sections of chimneys and plumes.

Early diagnosis is crucial for effective treatment of socially significant diseases, such as type 1 diabetes mellitus (T1DM), pneumonia, and asthma. This study employs a diagnostic method based on infrared laser spectroscopy of human exhaled breath. The experimental setup comprises a quantum cascade laser, which emits in a pulsed mode with a peak power of up to 150 mW in the spectral range of 5.3-12.8 μm (780-1890 cm-1), and a Herriott multipass gas cell with a specific optical path length of 76 m. Using this setup, spectra of exhaled breath in the mid-infrared range were obtained from 165 volunteers, including healthy individuals, patients with T1DM, asthma, and pneumonia. The study proposes a hybrid approach for classifying these spectra, utilizing a variational autoencoder for dimensionality reduction and a support vector machine method for classification. The results demonstrate that the proposed hybrid approach outperforms other machine learning method combinations.

В настоящий момент 6 % людей от всего населения планеты больны сахарным диабетом обоих типов, а 4 % – бронхиальной астмой. Прогнозируется, что количество людей с этими заболеваниями будет расти с каждым годом. Большой процент от всех страдающих вышеупомянутыми заболеваниями – дети. Актуальной задачей является разработка неинвазивного метода диагностирования диабета первого и второго типов, астмы и других болезней. Разработан подход для подготовки проб выдыхаемого человеком воздуха для их последующего анализа с помощью метода, основанного на инфракрасной лазерной спектроскопии. Применяемый метод подробно описан в данной работе. С помощью установки, основанной на инфракрасном квантово-каскадном лазере, проводится анализ спектров пропускания выдыхаемого человеком воздуха. По полученным спектрам можно рассчитать концентрации веществ-биомаркеров, отклонение от нормы которых связано с развитием у пациента определенных заболеваний или патологий. В данной работе проведен анализ таких существующих типов осушителей воздуха, как например, капиллярная колонка, криоловушка, адсорбционные осушители и др. В качестве наиболее оптимального решения для использования в экспериментальной установке с инфракрасным квантово-каскадным лазером был выбран нафионовый осушитель. По результатам исследований спектров выдыхаемого воздуха пациентов, с заранее известными поставленными диагнозами, был разработан и описан метод осушения пробы выдыхаемого человеком воздуха, а также была рассчитана абсолютная влажность осушенной пробы выдыхаемого воздуха.

The infrared spectra of the air exhaled by several groups of volunteers were studied: those suffering from type 1 diabetes, bronchial asthma, and pneumonia. To record infrared spectra, a tunable quantum-cascade laser (QCL) was used. QCL emits in the wavelength range from 5.3 to 12.8 μm in a pulsed mode with a pulse width of 50 ns, a power of up to 150 mW, and a tuning step of 1 cm-1. The laser is optically coupled to an astigmatic gas cell of the Herriot type with an optical path length of 76 m. A difference was found in the intensity of selective lines of biomarker molecules in the spectra of exhaled air of healthy volunteers compared to similar indicators of volunteers suffering from a certain disease. For an example of methods such as the support vector machine (SVM), the k-nearest neighbors (k-NN) and the random forest algorithm (RandomForest), the possibility of classifying volunteers by the infrared spectra of their exhaled air is shown. The use of dimensionality reduction methods (PCA and t-SNE) made it possible to increase the accuracy of disease classification up to 98% in terms of the accuracy metric.

The problem of global climate change has become one of the most important challenges to humanity in the 21 st century. The main reason is the appearance in the atmosphere of an excessive concentration of greenhouse gases, which absorb the thermal radiation of the Earth and partially return it to the Earth’s surface. The accumulation of greenhouse gases in the atmosphere leads to a rapid increase in the global average air temperature and, as a result, climate change. It is well known that greenhouse gases have a high transparency in the visible spectral range and high absorption in the infrared range. In this paper, we propose a new technique for recording the CO 2 and CH 4 spectra. An experimental setup based on dynamic Fourier spectrometer is developed. It allows to record IR absorption spectra in the wavelength range of 1.0 to 1.7 μm with a 10 cm –1 spectral resolution. Long-term recording of the atmospheric transmittance in the conditions of urban development is carried out. Based on the obtained data, the CO 2 and CH 4 integral and volumetric concentrations are monitored. It is shown that the carbon dioxide and methane volumetric concentrations time dependences accurately reflects the traffic congestion degree on that day. Reduction of volume concentrations in the evening hours is explained by the increase of the optical path and the additional capture of air masses outside the heavy traffic area.

Появление в атмосфере избыточной концентрации парниковых газов, которые, накапливаясь в ней, поглощают тепловое излучение Земли и частично возвращают его на земную поверхность, приводит к стремительному росту глобальной средней температуры воздуха и, как следствие, изменению климата. К парниковым относятся газы с высокой прозрачностью в видимом диапазоне и активным поглощением в тепловом инфракрасном диапазоне. В настоящей работе предложена новая методика регистрации спектров парниковых газов CO 2 и CH 4 . Представлен макет, разработанный на базе динамического фурье-спектрометра, который регистрировал спектры ИК-поглощения в диапазоне длин волн 1.0–1.7 мкм со спектральным разрешением 10 см –1 . Проведена долговременная запись коэффициента пропускания атмосферы в условиях городской застройки. По полученным данным осуществлялся контроль интегральной и объемной концентраций CO 2 и CH 4 . Показано, что поведение временны́х зависимостей объемных концентраций углекислого газа и метана хорошо отражает степень загруженности дорог. Уменьшение объемной концентрации в вечернее время объясняется увеличением оптической трассы и дополнительным захватом массы воздуха, находящегося за пределами области интенсивного движения.

Greenhouse gases absorb the Earth’s thermal radiation and partially return it to the Earth’s surface. When accumulated in the atmosphere, greenhouse gases lead to an increase in the average global air temperature and, as a result, climate change. In this paper, an approach to measuring CO2 and CH4 concentrations using Fourier transform infrared spectroscopy (FTIR) is proposed. An FTIR spectrometer mockup, operating in the wavelength range from 1.0 to 1.7 μm with a spectral resolution of 10 cm−1, is described. The results of CO2 and CH4 observations throughout a day in urban conditions are presented. A low-resolution FTIR spectrometer for the 16U CubeSat spacecraft is described. The FTIR spectrometer has a 2.0–2.4 μm spectral range for CO2 and CH4 bands, a 0.75–0.80 μm range for reference O2 bands, an input field of view of 10−2 rad and a spectral resolution of 2 cm−1. The capabilities of the 16U CubeSat spacecraft for remote sensing of greenhouse gas emissions using a developed FTIR spectrometer are discussed. The design of a 16U CubeSat spacecraft equipped with a compact, low-resolution FTIR spectrometer is presented.

The development of modern technologies in the field of image formation leads to an increase in the size of the generated images, as a result the question of reducing the processing computational costs arises, and this is an important factor in the creation of real-time systems. The study provides a description of high-speed recursive-separable filters for improving the quality of images, which, due to the peculiarities of their implementation, can reduce the number of computational operations required for the image processing process. This type of filters is obtained from two-dimensional linear digital filters, which are modified by applying recursive and separable properties to them. The MATLAB environment computing method for implementation of these filters is described. An extensive performance research of the developed filters has been carried out at various sizes of the test image and on various experimental installations. The comparison with the classical two-dimensional convolution method of the developed filters is demonstrated, and it shows the time gain required for the image processing. The results obtained can be applied in biomedical image processing systems or in vision systems working in heavy weather conditions.

An estimated 10.5% of the world’s population aged 20–79 years are currently living with diabetes in 2021. An urgent task is to develop a non-invasive express-diagnostics of diabetes with high accuracy. Type 1 diabetes mellitus (T1DM) diagnostic method based on infrared laser spectroscopy of human exhaled breath is described. A quantum cascade laser emitting in a pulsed mode with a peak power of up to 150 mW in the spectral range of 5.3–12.8 μm and Herriot multipass gas cell with an optical path length of 76 m were used. We propose a method for collecting and drying an exhaled human air sample and have measured 1200 infrared exhaled breath spectra from 60 healthy volunteers (the control group) and 60 volunteers with confirmed T1DM (the target group). A 1-D convolutional neural network for the classification of healthy and T1DM volunteers with an accuracy of 99.7%, recall 99.6% and AUC score 99.9% was used. The demonstrated results require clarification on a larger dataset and series of clinical studies and, further, the method can be implemented in routine medical practice.

A mathematical model of damped harmonic oscillators based on the Lorentz equations for calculating the optical characteristics of a medium is considered. Using an experimental setup built on the basis of an infrared quantum-cascade laser in the wavelength range of 5.3–12.8 μm with a peak power of up to 150 mW, diffuse scattering spectra of individual potassium perchlorate crystals are recorded. The transmission spectra are calculated using the Kramers–Kronig relations and the recorded scattering spectra. The model parameters are obtained based on the Lorentz equations for the scattering spectra of potassium perchlorate, which makes it possible to calculate the transmission spectra. The latter can be used to detect substances, including those in trace amounts, on various surfaces.

Progress in the development of infrared (IR) laser diodes, photodetector elements of matrices in the visible and IR ranges, lidar systems allows the use of optical location methods to detect and track moving objects in the atmosphere. This is primarily related to unmanned aerial vehicles (UAVs), which are widely used in many areas of human activity. This paper describes an experimental setup that makes it possible to detect a moving object in the atmosphere at a distance of more than 1 km, determine the distance to it, and automatically track it. The installation consists of a matrix photodetector of the visible and IR ranges, an active illumination source in the form of an IR laser diode emitting at a wavelength of λ = 808 nm with an output power of 30 W, and an IR lidar module with an energy per pulse of up to 15 mJ, emitting at a wavelength of λ = 1540 nm. It is shown that a combination of passive and active optical methods makes it possible to detect moving objects in the atmosphere, such as aerosol clouds or UAVs. For the automatic detection of moving objects of various types in the process of image processing in the visible and IR ranges, deep learning methods (convolutional neural networks) are used. With the help of the described installation, the linear dimensions of UAVs were estimated on routes of up to 1 km.

This work is devoted to the capabilities analysis of constellation and small spacecraft developed using CubeSat technology to solve promising problems of the Earth remote sensing in the area of greenhouse gases emissions. This paper presents the scientific needs for such tasks, followed by descriptions and discussions of the micro-technology application both in the small satellite platform design and in the payload design. The overview of analogical spacecraft is carried out. The design of a new spacecraft for determination the oxygen and carbon dioxide concentration in the air column along the line of sight of the spacecraft when it illuminated by reflected sunlight is introduced. A mock-up of the device was made for greenhouse gases remote sensing a Fourier Transform Infrared (FTIR) spectroradiometer is placed in the small spacecraft design. The results of long-term measurements of greenhouse gas concentrations using the developed Fourier spectrometer mock-up is presented.

In this paper, the application of machine learning and deep learning in the spectral analysis of multicomponent gas mixtures is considered. The experimental setup consists of a quantum cascade laser with a tuning range of 5.3–12.8 µm, a peak power of up to 150 mW, and an astigmatic Herriott gas cell with an optical path length of up to 76 m. Acetone, ethanol, methanol, and their mixtures are used as test substances. For the detection and clustering of substances, including molecular biomarkers, methods of machine learning, such as stochastic embedding of neighbors with a t-distribution, principal component analysis and classification methods, such as random forest, gradient boosting, and logistic regression, are proposed. A shallow convolutional neural network based on TensorFlow (Google) and Keras is used for the spectral analysis of gas mixtures. Model spectra of substances are used as a training sample, and model and experimental spectra are used as a test sample. It is shown that neural networks trained on model spectra (NIST database) can recognize substances in experimental gas mixtures. We propose using machine learning methods for clustering and classification of pure substances and gas mixtures and neural networks for the identification of gas mixture components. Using the experimental setup described, the experimentally obtained concentration limits are 80 ppb for acetone and 100–120 ppb for ethanol and methanol. The possibility of using the proposed methods for analyzing spectra of human exhaled air is shown, which is significant for biomedical applications.

An experimental setup and a method for analyzing multicomponent gas mixtures, including human-exhaled air, have been presented. The installation consists of a quantum cascade laser that is tunable in the wavelength range of 5.3–12.8 µm and has a peak power of 150 mW and a multi-pass Herriot gas cell that allows obtaining an optical path of up to 76 m. The registration time of a single spectrum is about 50 ms. For acetone and ethanol which are potential biomarkers of some human diseases the sensitivity threshold at the sub-ppm level has been experimentally determined. A system of sample preparation and pre-drying that allows analyzing both multicomponent gas mixtures and the air exhaled by a person has been proposed. The variants of application of the described installation in biomedical applications has been proposed.

We present the technique and experimental laboratory setup for measuring and analysis of diffuse reflectance spectra. The Experimental spectra obtained by tunable infrared quantum cascade laser with average power of 15 mW. Using causality relations for real and imaginary parts of reflectivity we can calculate the extinction coefficient. We use dumped harmonic oscillator (DHO) model to calculate synthetic spectra and test Kramers-Kronig relations for spectra calculations. Using experimental setup and numerical methods of spectra analysis we could identify the diethyl phthalate (DEP). The proposed method can be used in routine laboratory analysis to complement the ATR and DRIFTS methods of IR spectroscopy.

We consider a setup designed to study infrared radiation reflected from liquid and solid substances located on various substrates in this work. We describe the optical and principal scheme for the experimental setup. We use a laser setup that contains one Alpes Lasers quantum-cascade laser chip with a tuning range 1000 - 1300 1/cm with peak power up to 480 mW and 1 MHz repetition rate. Setup includes two HgCdTe thermoelectrically cooled sensors as reference and signal detector. Each sensor is equipped TEC controller, and the amplifier is mounted. The signal is digitized using a 24-bit ADC with a frequency of 1.5 MHz. Measurement time is about 1 sec. Recently experimentally reached sensitivity is less than one microgram per square cm on various substrates. We estimate the device's possible weight to about 5 kg and the sensitivity of about microgram per square cm. This work presents the results of processing the diffuse reflectance spectra. The diffuse reflectance spectra have low selectivity. So, we use calculational algorithms based on Kramers-Kronig transformations with extrapolation of spectra and phase correction. For substance identification using diffuse reflectance spectra, we use database consists of about 20 substances.

In this paper, we consider an integrated optical system designed to analyze the composition of ambient air. The optical system consists of a dynamic Michelson interferometer and a multi-pass White gas cell. We use steady state IR radiation source, that is integrated in the system. IR Radiation is modulated by passing through the interferometer, and then enters a multi-pass gas cell. We use two MCT TE cooled photodetector as reference and signal detectors. To digitize registered signal, we use a 24-bit ADC. For the described system, the sensitivity limit is about ppb levels, that allows detecting volatile substances at the maximum permissible concentrations. Described FTIR spectroscopy systems can be used for ambient air analysis and for breathomics applications.

We consider the possibility of using a combination of machine and deep learning in the spectral analysis for multicomponent gas mixtures. The experimental setup consists of a quantum cascade laser with a tuning range of 5.3 to 12.8 mu m, a peak power up to 150 mW, and a Herriot astigmatic gas cell with an optical path of up to 76 m. We used acetone, ethanol, methanol, acetaldehyde, and ethylene as test substances for the described techniques. For detection and clustering biomarkers, we used machine learning methods such as t-distributed stochastic neighbor embedding, principal component analysis, and classification methods such as decision tree, k-nearest neighbors, logistic regression, and support vector machines. We used a shallow convolutional neural network (CNN) based on TensorFlow (Google) and Keras for spectral analysis of gas mixtures. We modeled IR spectra of pure substances using an NIST database as training and validation sets. Then we used experimental spectra as a test set. We showed that logistic regression gives us the best result for pure substances' classification. Next, we modeled gas mixtures from synthetic IR spectra for CNN as training and validation sets. We showed that neural networks trained on synthetic spectra can recognize synthetic gas mixtures and experimental individual gaseous substances. We suggest using machine learning methods for pure substances clustering and classification and CNN for gas mixture components identification. We experimentally obtained minimum detectable concentration at ppm level for pure substances. Finally, we estimated that detection limits for the described experimental setup and numerical techniques occurs at levels around 10 to 50 ppb. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)

The paper presents a schematic diagram of the experimental setup human breath analysis using a broadband infrared quantum-cascade laser and a multipass Herriot gas cell. The sensitivity of described experimental setup allows to detect most common biomarkers of various diseases. The experimental spectra for several test substances are measured, threshold concentrations are detected. Statistical numerical methods and deep learning for spectra analysis are considered.