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The current traffic signal light timing scheme cannot effectively solve the problem of traffic congestion. This paper studied the intelligent control of signal lights based on infrared sensors. Firstly, the traffic flow was detected by infrared sensors; then, the future traffic flow was predicted using a back-propagation neural network; an intelligent control algorithm was designed based on the prediction results, and data were collected at the actual intersection for simulation experiments. It was found that the maximum and minimum root-mean-square error (RMSE) value of the BPNN prediction were 72.33 and 45.77, respectively, i.e., BPNN could predict the traffic flow accurately. Then, in the comparison of timing schemes, the maximum average queuing delay time of the BPNN was 0.55 s, and the maximum average queuing length was 92 m in the case of large traffic flow, both of which were better than the traditional fixed timing scheme. The results prove the reliability of the proposed signal light intelligent control algorithm. The algorithm can be further applied in practice. [ABSTRACT FROM AUTHOR]

The "Transit Guard" project represents a cutting-edge endeavour aimed at revolutionizing public transportation by implementing ticketless travel on buses and trains and tracking the number of passengers traveling. This project introduces a comprehensive system designed to enhance fare compliance, improve passenger convenience, and to avoid revenue losses due to ticketless traveling. Key components of the project include the development of a user-friendly mobile app for ticket purchase and validation, giving the number of passengers traveling in the bus and at the same time keeping track of how many passengers purchased the ticket through mobile app or offline ticket purchasing for each stop. "Transit Guard" not only curbs revenue losses for transit agencies but also fosters a culture of responsible travel and adherence to transit policies. By offering multiple ticketing options and ensuring accessibility, the project caters to all passengers, regardless of smartphone ownership. The result is an equitable, efficient, and secure public transportation experience the benefits both computers and transit providers, ultimately advancing the vision of fair and reliable transit systems. [ABSTRACT FROM AUTHOR]

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Nanocrystals (NCs) are now established building blocks for photonic applications. However, their integration for optoelectronics has not yet reached the same level of maturity, in part due to the perceived bottleneck that is the inherent limited mobility resulting from hopping conduction. Significant efforts are made to improve this mobility, notably by tuning the particle surface chemistry to enable larger interparticle electronic coupling, and values of mobility of ≈10 cm2 V−1 s−1 have been achieved. It is acknowledged that this value remains significantly lower than those obtained in 2D electron gases but is on par with the mobility reported for vertical transport in epitaxially grown heterostructures with similar confinement energies. Since there appears to be limited perspectives for further increasing mobility values, a suggestion is made that efforts should instead be directed toward exploring the potential benefits offered by hopping conduction. One of these benefits is the bias dependence of the diffusion length, which plays a key role in designing bias‐reconfigurable optical responses for NC‐based devices. Some recent achievements in building bias‐activated devices will be reviewed and the essential criteria for designing future structures will be discussed. Ultimately, hopping conduction is an opportunity to generate new functionalities that low‐disorder materials would be unable to provide. [ABSTRACT FROM AUTHOR]

The van der Waals (vdW) heterojunctions of transition metal dichalcogenides (TMDCs) provide an advanced platform for interlayer exciton generation to detect the exceeding cutoff wavelengths of individual TMDCs. Herein, the first demonstration of high‐performance infrared (IR) photodetectors driven by interlayer excitons and based on HfS2/MoS2 vdW heterojunctions grown by chemical vapor deposition is presented. HfS2 exhibits selective growth only on MoS2, establishing a vertical heterojunction that effectively generates interlayer excitons. The synthesized HfS2/MoS2 vertical heterojunction with type‐II band alignment exhibits a low interlayer bandgap and a significantly large interface area, enabling highly efficient IR detection. Moreover, the built‐in potential in HfS2/MoS2 plays a pivotal role in the outstanding photoresponse by suppressing the dark current and providing gradient band bending for the interlayer exciton‐induced photocarriers to drift toward each electrode. The HfS2/MoS2 photodetector exhibits remarkable performance, achieving a detectivity (D*) of ≈7 × 1013 Jones at 1550 nm, D* of ≈2 × 1014 Jones at 980 nm, and fast response time of 60 µs, surpassing previously reported 2D photodetectors. Overall, the successful demonstration of a photodetector based on vdW epitaxial HfS2/MoS2 paves the way for the advancement of large‐scale high‐performance IR sensors. [ABSTRACT FROM AUTHOR]

The “Transit Guard” project represents a cutting-edge endeavour aimed at revolutionizing public transportation by implementing ticketless travel on buses and trains and tracking the number of passengers traveling. This project introduces a comprehensive system designed to enhance fare compliance, improve passenger convenience, and to avoid revenue losses due to ticketless traveling. Key components of the project include the development of a user-friendly mobile app for ticket purchase and validation, giving the number of passengers traveling in the bus and at the same time keeping track of how many passengers purchased the ticket through mobile app or offline ticket purchasing for each stop. “Transit Guard” not only curbs revenue losses for transit agencies but also fosters a culture of responsible travel and adherence to transit policies. By offering multiple ticketing options and ensuring accessibility, the project caters to all passengers, regardless of smartphone ownership. The result is an equitable, efficient, and secure public transportation experience the benefits both computers and transit providers, ultimately advancing the vision of fair and reliable transit systems.

This paper presents a density-based traffic light signal system that performs timing signal that changes automatically based on the amount of traffic at each of its intersections. However, as traffic congestion is a pertinent problem on all of Ahmadu Bello University's (ABU) gates, it is time to advance from the traditional technique to an automated system that has self-decision capabilities. The current technique used on the traffic system is based on the traditional technique, which works based on time scheduling; this system is inefficient if one lane is operational while the others are not operational. The intelligent traffic control was prototyped in order to solve this perennial problem of ABU's gate. When there is a high density on one lane of the intersection, it causes a longer waiting time on the other lanes than the regular permitted time. As a result, a process was designed through which the time periods for the green and red lights were assigned based on the traffic densities on each of the lanes at that time. Infrared (IR) sensors were used to perform this task. The Arduino Uno Microcontroller was used for allocating the glowing period of green lights once density had been calculated. Sensors were used for monitoring the presence of vehicles and communicating information to the microcontroller, which determines the duration for which a signal will change or a flank will remain open. Also displayed is the operating principle of the density-based traffic signal control system, which shows the prototype's efficiency. [ABSTRACT FROM AUTHOR]

Currently, there is an increasing need for reliable mechanisms for automatically detecting and localizing people—from performing a people-flow analysis in museums and controlling smart homes to guarding hazardous areas like railway platforms. A method for detecting people using FLIR Lepton 3.5 thermal cameras and Raspberry Pi 3B+ computers was developed. The method creates a control and capture library for the Lepton 3.5 and a new person-detection technique that uses the state-of-the-art YOLO (You Only Look Once) real-time object detector based on deep neural networks. A thermal unit with an automated configuration using Ansible encapsulated in a custom 3D-printed enclosure was used. The unit has applications in simple thermal detection based on the modeling of complex scenes with polygonal boundaries and multiple thermal camera monitoring. An easily deployable person-detection and -localization system based on thermal imaging that supports multiple cameras and can serve as an input for other systems that take actions by knowing the positions of people in monitored environments was created. The thermal detection system was tested on a people-flow analysis performed in the Czech National Museum in Prague. The contribution of the presented method is the development of a small and simple detection system that is easily mountable with wide indoor as well as outdoor applications. The novelty of the system is in the utilization of the YOLO model for thermal data. [ABSTRACT FROM AUTHOR]

The paper is a continuation of the authors' work intended for infrared navigation for blind people and mobile robots. This concerns the detection of obstacles in the person's or mobile robot's trajectory, in particular, the detection of corners. The temperature distribution of a building's internal wall near a corner has been investigated. Due to geometry, more heat will be transferred by conduction so that inside the building, the temperature on the wall will be decreasing towards a corner. The problem will be investigated theoretically and numerically, and the results are confirmed by experimental measurements. The purpose of this research is to help blind people by equipping them with a small infrared camera that warns them when they are approaching a corner inside a building. The same aim is addressed to mobile robots. [ABSTRACT FROM AUTHOR]

With a growing focus on properties of softness, miniaturization, and intelligence, extensive research has been focusing on constructing wearable electronic devices facilitating comfort, wearable health monitoring and diagnosis. Among recent progress in the development of wearable bioelectronics, wearable near-infrared spectroscopy (NIRS) devices demonstrate wide implementation possibilities in multiple health monitoring scenarios. Throughout the years, multiple design strategies have assisted in developing wearable NIRS devices with high wearing comfortability and miniaturized size. This review summarizes the principle of NIRS technology, recent advances in design strategies towards soft, wearable, miniaturized NIRS devices, and the future potential development directions. Based on the discussion of different design strategies, including modular device design, flexible hybrid electronics, and materials innovation, we also pinpoint some development directions for wearable NIRS. The reviewed and proposed research efforts may enhance the applicability and capability of NIRS as an important technology for digital health.

An overview of the developmental history, characteristics, and applications of infrared sensors are firstly provided in the paper. Subsequently, it delves into the sensing mechanisms of commonly employed infrared sensors in substations, namely the thermistor sensors, pyroelectric sensors, and photoconductive sensors. Thermistor infrared sensors gauge infrared radiation by tracking changes in material resistance with temperature. Pyroelectric sensors detect thermal changes in objects based on the thermal effects induced by infrared radiation. Photoconductive-type sensors leverage the photoconductive effect of semiconductors to convert optical signals into electrical signals, adapting to various light conditions. In the process of measurements, variables like temperature, distance between the target object and the infrared sensor, and electromagnetic interference impact accuracy. In extreme temperatures, the cooling precision of infrared thermal imaging systems may decline, affecting measurement accuracy. The distance between the target and sensor also influences results due to energy attenuation in the infrared radiation path through the atmosphere. Faraday’s law of electromagnetic induction warns of potential electromagnetic interference signals in substations, causing device breakdowns, output image halo, or increased internal noise. Finally, the paper envisions the future development of infrared sensors.

Diagnostic abdominal ultrasound screening and monitoring protocols are based around gathering a set of standard cross sectional images that ensure the coverage of relevant anatomical structures during the collection procedure. This allows clinicians to make diagnostic decisions with the best picture available from that modality. Currently, there is very little assistance provided to sonographers to ensure adherence to collection protocols, with previous studies suggesting that traditional image only machine learning classification can provide only limited assistance in supporting this task, for example it can be difficult to differentiate between multiple liver cross sections or those of the left and right kidney from image post collection. In this proof of concept, positional tracking information was added to the image input of a neural network to provide the additional context required to recognize six otherwise difficult to identify edge cases. In this paper optical and sensor based infrared tracking (IR) was used to track the position of an ultrasound probe during the collection of clinical cross sections on an abdominal phantom. Convolutional neural networks were then trained using both image-only and image with positional data, the classification accuracy results were then compared. The addition of positional information significantly improved average classification results from ∼90% for image-only to 95% for optical IR position tracking and 93% for Sensor-based IR in common abdominal cross sections. While there is further work to be done, the addition of low-cost positional tracking to machine learning ultrasound classification will allow for significantly increased accuracy for identifying important diagnostic cross sections, with the potential to not only provide validation of adherence to protocol but also could provide navigation prompts to assist in user training and in ensuring adherence in capturing cross sections in future.

The Cross-track Infrared Sounder (CrIS) is an infrared Fourier-transform spectrometer that measures the Earth's infrared radiance at high spectral resolution and high accuracy. The potential for polarization errors contributing significantly to the radiometric uncertainty of infrared remote sounders has been well recognized and documented, particularly due to polarization-dependent scene select mirrors operated in conjunction with grating-based instruments. The issue is equally applicable to FTS-based sensors. While the CrIS sensor utilizes an unprotected gold scene select mirror which has extremely low polarization in the infrared and the angle of incidence at the mirror is maintained for all calibration and Earth scene views, the radiometric bias due to polarization effects was determined to be non-negligible for cold scenes. A model for the polarization-induced calibration bias and the associated correction is presented for the CrIS instrument, along with details of the model parameter determination, and the impact of the correction on the calibrated radiances for a range of scene temperatures and types. [ABSTRACT FROM AUTHOR]

This paper explores the feasibility of using low-resolution infrared (LRIR) image streams for human activity recognition (HAR) with potential application in e-healthcare. Two datasets based on synchronized multichannel LRIR sensors systems are considered for a comprehensive study about optimal data acquisition. A novel noise reduction technique is proposed for alleviating the effects of horizontal and vertical periodic noise in the 2D spatiotemporal activity profiles created by vectorizing and concatenating the LRIR frames. Two main analysis strategies are explored for HAR, including (1) manual feature extraction using texture-based and orthogonal-transformation-based techniques, followed by classification using support vector machine (SVM), random forest (RF), k-nearest neighbor (k-NN), and logistic regression (LR), and (2) deep neural network (DNN) strategy based on a convolutional long short-term memory (LSTM). The proposed periodic noise reduction technique showcases an increase of up to 14.15% using different models. In addition, for the first time, the optimum number of sensors, sensor layout, and distance to subjects are studied, indicating the optimum results based on a single side sensor at a close distance. Reasonable accuracies are achieved in the case of sensor displacement and robustness in detection of multiple subjects. Furthermore, the models show suitability for data collected in different environments. [ABSTRACT FROM AUTHOR]

This article presents the development of a prototype computational system designed for the detection of vital signs and the location of people in structures, such as in earthquake situations. The system uses sensor technologies, signal processing algorithms and neural networks to identify the presence of trapped people and monitor their vital signs. The system architecture, component integration, and initial test results in simulated environments are detailed. Preliminary results demonstrate the effectiveness of the system in accurate and real-time detection, proposing a valuable tool for rescue operations. [ABSTRACT FROM AUTHOR]

This paper presents a density-based traffic light signal system that performs timing signal that changes automatically based on the amount of traffic at each of its intersections. However, as traffic congestion is a pertinent problem on all of Ahmadu Bello University’s (ABU) gates, it is time to advance from the traditional technique to an automated system that has self-decision capabilities. The current technique used on the traffic system is based on the traditional technique, which works based on time scheduling; this system is inefficient if one lane is operational while the others are not operational. The intelligent traffic control was prototyped in order to solve this perennial problem of ABU’s gate. When there is a high density on one lane of the intersection, it causes a longer waiting time on the other lanes than the regular permitted time. As a result, a process was designed through which the time periods for the green and red lights were assigned based on the traffic densities on each of the lanes at that time. Infrared (IR) sensors were used to perform this task. The Arduino Uno Microcontroller was used for allocating the glowing period of green lights once density had been calculated. Sensors were used for monitoring the presence of vehicles and communicating information to the microcontroller, which determines the duration for which a signal will change or a flank will remain open. Also displayed is the operating principle of the density-based traffic signal control system, which shows the prototype’s efficiency.

A key challenge in further improving infrared (IR) sensor capabilities is the development of efficient data pre-processing algorithms. This paper addresses this challenge by providing a mathematical model and synthetic data generation framework for an uncooled IR sensor. The developed model is capable of generating synthetic data for the design of data pre-processing algorithms of uncooled IR sensors. The mathematical model accounts for the physical characteristics of the focal plane array, bolometer readout, optics and the environment. The framework permits the sensor simulation with a range of sensor configurations, pixel defectiveness, non-uniformity and noise parameters. [ABSTRACT FROM AUTHOR]

Metamaterial-based infrared sensors have attracted a lot of attention due to their ease of fabrication and various applications. In this paper, sensing capability of various metamaterial architectures, with different geometrical designs, is investigated. It turns out that some of these designs represent sensitivity values comparable to those of the best metamaterial sensors ever reported in literature. Some of these designs represent perfect absorption and some others represent electromagnetically induced reflectance (EIR). Between them, the best structure in sensing capability feature has a relatively simple architecture as well. Its unit cell contains two metallic nano-plates within which two horizontal nano-bars are emptied out. These sheets are located at the two ends of the unit cell. Moreover, a π-shaped metallic element is also inserted between the two nano-plates. In addition to representing the interesting behavior of EIR, this structure provides a sensitivity value of 4077.2 nm/RIU. This is among the highest values reported in literature for metamaterial sensors operating in the near infrared region. The high value of sensitivity, along with the simplicity of the structure design, makes the idea of suitability of the proposed sensor for practical applications viable. Finally, effects of geometrical parameters on the sensitivity and resonance frequency of the sensors are investigated. It is found that the resonance frequency can be tuned in the range of 250–350 THz. This tunability of the resonance frequency is another promising point associated with the proposed sensors for practical applications. [ABSTRACT FROM AUTHOR]

Currently, there is an increasing need for reliable mechanisms for automatically detecting and localizing people—from performing a people-flow analysis in museums and controlling smart homes to guarding hazardous areas like railway platforms. A method for detecting people using FLIR Lepton 3.5 thermal cameras and Raspberry Pi 3B+ computers was developed. The method creates a control and capture library for the Lepton 3.5 and a new person-detection technique that uses the state-of-the-art YOLO (You Only Look Once) real-time object detector based on deep neural networks. A thermal unit with an automated configuration using Ansible encapsulated in a custom 3D-printed enclosure was used. The unit has applications in simple thermal detection based on the modeling of complex scenes with polygonal boundaries and multiple thermal camera monitoring. An easily deployable person-detection and -localization system based on thermal imaging that supports multiple cameras and can serve as an input for other systems that take actions by knowing the positions of people in monitored environments was created. The thermal detection system was tested on a people-flow analysis performed in the Czech National Museum in Prague. The contribution of the presented method is the development of a small and simple detection system that is easily mountable with wide indoor as well as outdoor applications. The novelty of the system is in the utilization of the YOLO model for thermal data.

The paper is a continuation of the authors’ work intended for infrared navigation for blind people and mobile robots. This concerns the detection of obstacles in the person’s or mobile robot’s trajectory, in particular, the detection of corners. The temperature distribution of a building’s internal wall near a corner has been investigated. Due to geometry, more heat will be transferred by conduction so that inside the building, the temperature on the wall will be decreasing towards a corner. The problem will be investigated theoretically and numerically, and the results are confirmed by experimental measurements. The purpose of this research is to help blind people by equipping them with a small infrared camera that warns them when they are approaching a corner inside a building. The same aim is addressed to mobile robots.

Light interaction physical properties of MXenes are fundamentally important and promising for developing new broadband photodetection applications. In this work, the significant influence of intercalants on the photo‐electronic properties of MXenes is studied. It is found that the intercalated H2O‐induced inter‐flake resistance greatly restricts the photon excitation responsivity of Ti3C2Tx, resulting in a slow and irreversible resistance change under laser irradiation. After deintercalation of H2O by in situ laser writing, the resistance of Ti3C2Tx responds fast and reversibly to laser excitation and the variation range is proportional to laser power. By extending the findings to another MXene member, Mo2TiC2, the intercalant induced resistance is successfully developed for optoelectronic detection. By selectively deintercalating H2O in the as‐prepared Mo2TiC2 which is co‐intercalated by H2O and TMAOH, bare TMAOH intercalated Mo2TiC2 shows fast and reversible resistance changes with laser. Moreover, the resistance of bare TMAOH intercalated Mo2TiC2 shows a negative temperature coefficient behavior while the fully deintercalated Ti3C2Tx shows a positive temperature behavior. These findings elucidate the understanding of the optoelectronic properties of MXenes and provide insights into the engineering of interfacial chemistry of MXenes for performance enhancement toward various detectors/sensors beyond optoelectronic applications. [ABSTRACT FROM AUTHOR]

An infrared sensor with Au/SiO2/Au metamaterial perfect absorber (MPA) structure and Crystal ion slicing (CIS) fabricated LiTaO3 single crystal film to realize wavelength-selective function was proposed and fabricated in this paper. The Au/SiO2/Au multilayer MPA exhibited outstanding wavelength selectivity at $4.04\mu \text{m}$ with a maximum absorbance of 98% and the spectral voltage response of sensor highly agreed with it. Meanwhile, the sensor performed stable angle independency with radiation incident angle from 0° to 50°. After integration, this sensor showed maximum voltage responsivity of ${1.7} \times {10}^{{4}}\text{V}$ /W, maximum detectivity of ${1.7} \times {10}^{{8}}$ cmHz1/2/W, and 5.8ms response time. These results demonstrate the promising application prospect of high specific and fast response pyroelectric infrared sensor achieved by integrating MPA with LiTaO3 thin film. [ABSTRACT FROM AUTHOR]

Light management is one of the main challenges to address when designing a sensor from a nanocrystal (NC) array. Indeed, the carrier diffusion length, limited by hopping mechanism, is much shorter than the absorption depth. Several types of resonators (plasmon, Bragg mirror, guided mode, Fabry-Perot cavity) have been proposed to reduce the volume where light is absorbed. All of them are inherently narrow bands, while imaging applications focus on broadband sensing. Here, an infrared sensor in the short and mid-wave infrared (SWIR and MWIR) that combines three different photonic modes is proposed to achieve broadband enhancement of the light absorption. Moreover, it is shown that these three modes can be obtained from a simple structure where the NC film is coupled only to a grating and a top metallic layer. The obtained device achieves a high responsivity of >700 mA W-1, a detectivity up to 2 × 1010 Jones at 80 K, and a short response time of 11 µs. [ABSTRACT FROM AUTHOR]

Garbage detection and disposal have become one of the major hassles in urban planning. Due to population influx in urban areas, the rate of garbage generation has increased exponentially along with garbage diversity. In this paper, we propose a hardware solution for garbage segregation at the base level based on deep learning architecture. The proposed deep-learning-based hardware solution SmartBin can segregate the garbage into biodegradable and non-biodegradable using Image classification through a Convolutional Neural Network System Architecture using a Real-time embedded system. Garbage detection via image classification aims for quick and efficient categorization of garbage present in the bin. However, this is an arduous task as garbage can be of any dimension, object, color, texture, unlike object detection of a particular entity where images of objects of that entity do share some similar characteristics and traits. The objective of this work is to compare the performance of various pre-trained Convolution Neural Network namely AlexNet, ResNet, VGG-16, and InceptionNet for garbage classification and test their working along with hardware components (PiCam, raspberry pi, infrared sensors, etc.) used for garbage detection in the bin. The InceptionNet Neural Network showed the best performance measure for the proposed model with an accuracy of 98.15% and a loss of 0.10 for the training set while it was 96.23% and 0.13 for the validation set. [ABSTRACT FROM AUTHOR]

In this paper, we report a method of increasing the sensitivity of a silicon near-infrared sensor. The sensor is realized by forming multiple trench-type photodiodes in a silicon chip. The trench photodiodes can be formed using conventional semiconductor fabrication equipment. The device structure allows the depletion layer to be spread over the entire sensor chip even at a bias voltage of 10 V or less. The sensor chip can thereby extend the collection area of photoelectrons to the maximum. At a chip thickness of 540 $\mu \text{m}$ , the conversion efficiency for near-infrared wavelengths between 940 and 1020 nm is more than 80% at room temperature. In addition, the electrical characteristics and response performance of the fabricated 2.4 mm $\times2.4$ mm test chips are reported. Since the proposed method can achieve a high conversion efficiency at low voltage without cooling in silicon semiconductors, it is expected to provide a low-cost and compact solution for various near-infrared receiver devices such as these for Internet of Things (IoT) applications.

Hand exoskeleton pose monitoring is of great importance in the rehabilitation training of stroke patients to ensure precise robotic trajectory control and provide a patient recovery assessment mechanism. In this paper, a low-cost pose sensor unit based on the principle of photoelectric reflection is proposed to measure the pose of a multi-segment continuous structure in a hand rehabilitation exoskeleton. The sensor unit consists of five photosensitive elements that measure the rotation angle of an arrangement of adjacent segments, each integrated with a sensing element, to estimate the actuator’s motion. An accurate device with a user-friendly interface is then designed for calibration of the sensing elements. The experimental results indicate that the sensitivity exceeds 0.047 V/° for the sensing elements, and hysteresis and repeatability errors are less than 1.1% and 1.8%, respectively. A comparison between the proposed sensor output and the results benchmarked by a VICON motion capture system demonstrates that the sensor can measure the bending angle of the multi-segment structure with a mean error of 3.23 degrees. [ABSTRACT FROM AUTHOR]

The main objective of this paper was to demonstrate the capability of dedicated small satellite infrared sensors with cooled quantum detectors, such as those successfully utilized three times in Germany's pioneering BIRD and FireBIRD small satellite infrared missions, in the quantitative characterization of high-temperature events such as wildfires. The Bi-spectral Infrared Detection (BIRD) mission was launched in October 2001. The space segment of FireBIRD consists of the small satellites Technologie Erprobungs-Träger (TET-1), launched in July 2012, and Bi-spectral InfraRed Optical System (BIROS), launched in June 2016. These missions also significantly improved the scientific understanding of space-borne fire monitoring with regard to climate change. The selected examples compare the evaluation of quantitative characteristics using data from BIRD or FireBIRD and from the operational polar orbiting IR sensor systems MODIS, SLSTR and VIIRS. Data from the geostationary satellite "Himawari-8" were compared with FireBIRD data, obtained simultaneously. The geostationary Meteosat Third Generation-Imager (MTG-I) is foreseen to be launched at the end of 2022. In its application to fire, the MTG-I's Flexible Combined Imager (FCI) will provide related spectral bands at ground sampling distances (GSD) of 3.8 µm and 10.5 µm at the sub-satellite point (SSP) of 1 km or 2 km, depending on the used FCI imaging mode. BIRD wildfire data, obtained over Africa and Portugal, were used to simulate the fire detection and monitoring capability of MTG-I/FCI. A new quality of fire monitoring is predicted, if the 1 km resolution wildfire data from MTG-1/FCI are used together with the co-located fire data acquired by the polar orbiting Visible Infrared Imaging Radiometer Suite (VIIRS), and possibly prospective FireBIRD-type compact IR sensors flying on several small satellites in various low Earth orbits (LEOs). [ABSTRACT FROM AUTHOR]

Position sensitive detectors (PSDs) offer possibility to track single active marker’s two (or three) degrees of freedom (DoF) position with a high accuracy, while having a fast response time with high update frequency and low latency, all using a very simple signal processing circuit. However they are not particularly suitable for 6-DoF object pose tracking system due to lack of orientation measurement, limited tracking range, and sensitivity to environmental variation. We propose a novel 6-DoF pose tracking system for a rigid object tracking requiring a single active marker. The proposed system uses a stereo-based PSD pair and multiple Inertial Measurement Units (IMUs). This is done based on a practical approach to identify and control the power of Infrared-Light Emitting Diode (IR-LED) active markers, with an aim to increase the tracking work space and reduce the power consumption. Our proposed tracking system is validated with three different work space sizes and for static and dynamic positional accuracy using robotic arm manipulator with three different dynamic motion patterns. Results demonstrate that the static position accuracy root-mean-square (RMS) error is $0.6~ {{mm}}$ ($0.22~ {{mm}}$ precision RMS error) at the mid range of the workspace and $1.3 ~{{mm}}$ ($0.44~ {{mm}}$ precision RMS error) at the largest workspace. The dynamic position RMS error is 0.7- $0.9 ~{{mm}}$. The orientation RMS error is between 0.04° and 0.9° at varied dynamic motion. Overall, our proposed tracking system is capable of tracking a rigid object pose with sub-millimeter accuracy at the mid range of the work space and sub-degree accuracy for all work space under a lab setting. [ABSTRACT FROM AUTHOR]

In the context of the pandemic caused by the COVID-19 virus, the transition to online education was forced. Also, the laboratory works from the engineering disciplines must be adapted to the online environment. This paper presents the study of infrared proximity sensors, raising the operating characteristic and comparing it with that provided by the sensor manufacturer. The transfer function of the sensor will be determined annalistically and will be compared with the one obtained from the simulation. At the end, the conclusions we can draw will be presented. [ABSTRACT FROM AUTHOR]

In this investigation,the problem of moving object detection-without any knowledge-is classified. It describes a technique that will allowreal-timelocalizationwith usage of IR sensors. The proposed algorithm is simplistic, and in the future,it might be implemented into any vehicle, premium or entry level. It is guidedbyAI that must calculate its nextmoves in the blink of an eyewithout user noticingany delays. The main problem ofmoving object recognition wasextraction ofproper features,description ofthe events, and choice ofonly the crucial ones. The presentednovel approach does not followanystandard algorithms. It is a practical hardware implementation of custom solution, based onprocessing system, which can be well situated in the safety modules of futurecars.

The main topic of the article is the choice of the direction and implementation of the use of electronic measuring instruments and monitoring of the working process occurring in the diesel internal combustion engine. For this purpose, an analysis is performed and a device is created that allows controlling the uneven rotation of the engine crankshaft containing photoelectron sensors, one of which allows reading the moment of passing the upper dead point, the other is designed to control the angular speed of the flywheel. The device converts the signals of these sensors into the form necessary for visualization of the working periodic process on the oscilloscope screen. The article presents a block diagram of the device and its amplitude-frequency characteristic. This device can be used for research and development work to improve fuel supply systems in internal combustion engines.

Vehicle accidents on mountain roads have been a major concern for many years. The narrow roads, acute turns, and steep slopes of these areas can make it difficult for drivers to see approaching vehicles, leading to a high incidence of collisions. This research paper aims to propose a solution to this problem using emerging modern technology to prevent and reduce the number of accidents on mountain roads. The proposed model consists of techniques such as image processing to detect the type of vehicle present at the other end of a blind spot, alerting the driver and thus allowing them to make safe decisions while taking turns also there is a system to detect vehicle using sensors and also to manipulate the signal so proper instruction can be conveyed. Through this research, we aim to demonstrate the effectiveness of this system and show how it can be used to reduce the loss of life and property on mountain roads.

The Cross-track Infrared Sounder (CrIS) is an infrared Fourier-transform spectrometer that measures the Earth’s infrared radiance at high spectral resolution and high accuracy. The potential for polarization errors contributing significantly to the radiometric uncertainty of infrared remote sounders has been well recognized and documented, particularly due to polarization-dependent scene select mirrors operated in conjunction with grating-based instruments. The issue is equally applicable to FTS-based sensors. While the CrIS sensor utilizes an unprotected gold scene select mirror which has extremely low polarization in the infrared and the angle of incidence at the mirror is maintained for all calibration and Earth scene views, the radiometric bias due to polarization effects was determined to be non-negligible for cold scenes. A model for the polarization-induced calibration bias and the associated correction is presented for the CrIS instrument, along with details of the model parameter determination, and the impact of the correction on the calibrated radiances for a range of scene temperatures and types.