Infrared Camera Research Articles

A drone-based population survey of Delacour's langur (Trachypithecus delacouri) in the karst forests of northern Vietnam

The Critically Endangered Delacour's langur (Trachypithecus delacouri) is presently found only in a few isolated karst areas in northern Vietnam, where conducting field surveys has proven challenging due to the difficult terrain. Accurate population estimates from a scientifically sound method are needed to inform management of the species. From October to December 2022, we used a drone equipped with optical and thermal cameras to survey the species in Kim Bang Forest, a critical site for the species. We estimated langur abundance from the resulting point count data using N-mixture models including abiotic and biotic variables. We also compared the effectiveness and efficiency of the drone method with two commonly used ground-based methods. The drone survey recorded 16 groups with 104 individuals. We estimated a density of 0.87 groups per km2 and a population of 25 groups and 175 individuals. This estimate is 80–113 % higher than previous ground-based estimates, attributed primarily to the higher area coverage by the drone survey. The estimate reaffirms the conservation importance of Kim Bang Forest for the species. The modelling also indicated that Delacour's langur abundance was correlated negatively with Assamese macaque (Macaca assamensis) presence and positively with vegetation productivity. Other variables (elevation, terrain ruggedness and distance to forest edge) were much less important in explaining langur abundance. Compared to the ground-based methods, the drone approach proved effective and resource-efficient for surveying Delacour's langurs. We recommend the drone method for future Delacour's langur surveys, with potential applicability to other arboreal mammals in difficult-to-access karst forests.

Low-Cost Arduino-Based Counter System

A useful tool for counting the number of persons entering or exiting a room is an automated counter system.The goal of this research is to present a novel approach that can count people automatically. With the aid of ATmega328P internal code, it intelligently determines and counts the number of individuals. The number of obstacles that pass in front of the IR sensor in a single direction is counted in this project. A 16x2 LCD display module shows the count number or the total counts value. The module consists of an IR LED emitter and an IR photodiode detector. In this project, we are utilizing an active infrared sensor. It produces a high output when it detects an object inside its detection range, and a low output otherwise. Rotating the built-in trim pot will also change the sensor's range. When something moves in front of it, the count starts at zero and increases by one.

An IoT-Enabled System for Prioritizing Emergency Vehicles and Detecting Accidents in Smart Traffic Management

Abstract—With the growth of urban areas and the rising number of vehicles, managing traffic congestion has become increasingly difficult, especially when it comes to ensuring prompt clearance for emergency vehicles like ambulances, fire trucks, and police cars. This paper introduces a Smart Traffic Management System powered by IoT, which leverages camera modules and machine learning algorithms to identify and prioritize emergency vehicles. Ambulances receive the highest priority, followed by fire trucks and police vehicles. The system adapts traffic signals dynamically by detecting emergency vehicles in real-time, and also enhances traffic flow using IR sensors to monitor vehicle density. Additionally, the system includes an Accident Detection Module that leverages intelligent accelerometer sensors to detect road accidents, using data on speed, pressure, and distance. This holistic approach ensures that emergency lanes are cleared efficiently and improves traffic mobility in urban environments. The proposed system significantly enhances the efficiency of traffic management compared to conventional methods, offering a comprehensive solution for reducing congestion, improving emergency response times, and minimizing accident fatalities. Index Terms—IoT, smart traffic management, machine learning, emergency vehicle detection, traffic signal optimization, IR sensors, accident detection, accelerometer sensors, real-time data processing, urban mobility, SCATS (Sydney Coordinated Adaptive Traffic System), IoT-Enabled Smart Traffic Management System (ISTMS).

Structural Deterioration and Failure of the Laminated Wooden Roof of a Covered Swimming Pool

A swimming pool in Corrales de Buelna (Cantabria) was demolished in March 2017 due to the loss of mechanical performance of the laminated timber structure. The relevant deterioration was caused by rotting of the wood and corrosion of the metal connecting elements. The structure featured a barrel vault with five large tri-articulated arches enclosed on the sides by inclined facades formed by toral rafters and purlins. The corresponding diagnostic process involved data collection and structural assessments to verify the structure’s bearing capacity and serviceability. Data collection was carried out in December 2015 and consisted of a thermal camera inspection to determine the points of moisture accumulation and sampling openings, conduct environmental and wood hygrothermal measurements, and measure cross-sectional losses and deformations of the structural elements. Verification of the load-bearing capacity was carried out using matrix calculation structure software for both the original and deteriorated structure. The diagnosis indicated that the damage was caused by leaks in the joints of the aluminum composite roof panels and by the insufficient load-bearing capacity of the structure. The severity of the damage compromised the mechanical strength and stability of the building, leading to a recommendation that the use of the facilities be immediately discontinued. The degree of deterioration left the structure unrecoverable, making it very difficult to apply reinforcement measures. These factors led to the structure’s demolition to prevent its collapse.

Symptoms and joint degeneration correlate with the temperature of osteoarthritic knees: an infrared thermography analysis.

This study aim was to analyze the joint temperature of patients affected by bilateral knee osteoarthritis (OA) using infrared thermography to investigate whether thermographic imaging patterns are influenced by the severity of symptoms and joint degeneration. Sixty-sixpatients ranging from 43 to 78 years old (63.3 ± 8.8 years) with bilateral knee OA and one symptomatic knee were enrolled. Thermograms of the two knees were captured using a thermographic camera FLIR T1020 and analyzed with the ResearchIR software to calculate the temperature of the overall knee and the four regions of interest (ROIs): patella, suprapatellar, medial, and lateral areas. The temperature of knees affected by OA was influenced by joint degeneration level and symptoms: patients with higher OA grade in the symptomatic knees presented higher total knee temperatures compared to the asymptomatic ones (p = 0.002), as well as in the patellar (p = 0.005), lateral (p = 0.002), and medial (p = 0.001) areas. On the other hand, patients with the same OA level in the two knees presented a higher temperature in the symptomatic knee only in the medial area (p = 0.037). Symptomatic knees demonstrated a different pattern compared to asymptomatic knees, with the medial area presenting the highest temperature changes (p = 0.020). Patients reporting prevalent pain in the lateral knee area presented higher differences in total knee temperature (0.7 ± 0.7°C) than patients with pain in the medial area (0.1 ± 0.5°C) (p = 0.023). The temperature of knees affected by OA is influenced by the degree of joint degeneration and by the presence of symptoms, with higher temperatures found in symptomatic joints, especially with prevalent lateral knee pain, and in more severe OA. Symptomatic knees demonstrated a different pattern compared to asymptomatic knees, with the medial area presenting the highest temperature changes.

Inverse Design of Aberration-Corrected Hybrid Metalenses for Large Field of View Thermal Imaging Across the Entire Longwave Infrared Atmospheric Window.

Long wave infrared (LWIR) cameras play a pivotal role across diverse applications due to their distinctive features. The growing demand for high-performance thermal imaging optics, characterized by a broad working bandwidth, large field of view (FOV), aberration-free design, lightweight construction, compactness, and cost-effectiveness, poses significant challenges for LWIR lens design. Here, we propose an inverse design method for LWIR hybrid metalenses, specifically aiming to achieve aberration-corrected thermal imaging with both a large FOV and a broad working bandwidth. Our approach involves optimizing phase profiles of metalens' unit cells guided by a loss function that compares the hybrid lens design to diffraction-limited results for various incident angles and wavelengths. As a result, we demonstrate an aberration-corrected thermal camera with a 30° FOV and an achromatic working bandwidth spanning the entire LWIR atmospheric window (8 to 14 μm). Significantly, the total optical path length, the entrance pupil to the sensor plane of the charge-coupled device (CCD), is a mere 13.6 mm. Our work merits advantages in the FOV, working bandwidth, and compactness, which surpass state-of-the-art LWIR hybrid metalens designs and find numerous imaging and sensing applications.

Use of the Thermal Camera to Assess the Perfusion of the Nipple-Areola Complex and Lower Adipoglandular Flap in Post-Explantation Mastopexy.

Thermography is a valuable tool for assessing tissue temperature. In recent years, explantation surgery has increased due to the fact that, despite technological advancements, breast implants have a limited lifespan and eventually require explantation, whether for pathological, aesthetic, or personal reasons. Among the complications that can arise in breast surgery are necrosis of the nipple-areola complex (NAC) and fat necrosis of the lower dermoglandular flap, which may affect the outcomes. Considering that the blood supply and perfusion of the breast were altered during the initial surgical procedure, and the technique previously used is often unknown, it is crucial to ensure adequate vascularization of the NAC and the flap. This study demonstrates the usefulness of the thermal camera for intraoperative and postoperative monitoring of the NAC and the lower adipoglandular flap, with the goal of determining tissue perfusion and identifying hypoperfused areas for immediate management, thus providing a new safety parameter in breast surgery.Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Raman Spectroscopy Analysis of Nitrogen Hyperdoped Cz and Fz Silicon Implanted with Nitrogen at Various Dose Levels for Infrared Absorption

Nitrogen hyperdoping is proposed to enable infrared absorption in silicon via two-photon absorption, which is beneficial for high-efficiency solar cells and IR sensors. Cz-Si and lightly doped FZ-Si were implanted with nitrogen at 200 keV with high fluences to achieve doping levels of 0.02, 0.1, 0.2, and 1 at% %, referred to as Dose 1, 2, 3, and 4, respectively. Confocal Raman spectroscopy with 532 nm excitation provided spectra at multiple locations and depths for hyperdoped and undoped Cz and FZ silicon.Raman spectroscopy revealed several structural features contributing to the spectra of nitrogen hyperdoped silicon. Both non-implanted Cz-Si and FZ-Si exhibit a sharp peak at 520 cm−1, attributed to the first-order TO mode for crystalline silicon (c-Si). In certain areas of non-implanted FZ-Si samples, this peak shifts to 514.9 cm−1, indicating residual tensile strain due to nitrogen incorporation. Additionally, this peak displays a slight asymmetry, suggesting the presence of another phase of silicon in FZ-Si. The refinement process for FZ-Si introduces inhomogeneous strains and swirl defects, as revealed by etching and x-ray topography (XRT). Amorphous regions in non-implanted FZ-Si are indicated by a broad band in the Raman spectra, ranging from 63 cm−1 to 207 cm−1. Non-implanted Cz-Si samples show a small shift in the c-Si line to 524 cm−1, reflecting localized compressive strain. However, the c-Si peak remains symmetric across all measured locations. All as-grown samples display the c-Si 2TA mode at 300 cm−1 and the 2TO mode at 970 cm−1, which shift to 291 cm−1 and 940 cm−1, respectively, due to residual stress in the bulk.Raman spectra of n hyperdoped FZ-Si (NFZ-Si) and Cz-Si (NCz-Si) samples reveal both crystalline and amorphous phases (a-Si) in the near-surface region. All spectra from implanted silicon display the intense first-order TO mode peak located around 520 cm−1, varying slightly with sample type. The broad band from 407 cm−1 to 493 cm−1 (phonon TO mode) in NCz-Si and NFZ-Si exhibits an asymmetric shoulder shape, attributed to scattering from the Si–N bond. This suggests a mixture of amorphous silicon and SiNx nanocrystallites. The band intensity increases with dose; and for a given dose, it is more prominent and wider in NFZ-Si. In this material, SiNx clusters induce strain, shifting the c-Si line to lower frequencies (509–512 cm−1) or higher frequencies (521–525 cm−1) depending on the dose, indicating the buildup of tensile or compressive strain during the implantation. As NCz-Si relaxes at high nitrogen doses, the c-Si peak shifts back to 520 cm−1, with slight asymmetry suggesting the presence of SiNx clusters.For all implanted samples, regardless of the dose, the intensity of the c-Si phonon peak remains unchanged compared to the as-grown samples. The broad peak from 63 cm−1 to 207 cm−1 of the a-Si TA mode is also present in implanted NCz-Si and NFZ-Si. Notably, the intensity of this band increases with dose, rising significantly in NFZ-Si but only slightly in NCz-Si. NFZ-Si samples exhibit a low-intensity Raman peak at 600–620 cm−1, attributed to the a-Si 2LA mode, which appears in NCz-Si at high doses only, suggesting the involvement of oxygen interacting with nitrogen, as shown in our previous work. Additionally, we observed a shift in the c-Si 970 cm−1 2TO mode to 930 cm−1 in NFZ-Si as the dose increases, a shift not seen in NCz-Si. This may result from the combination of SiNx clusters and existing self-interstitial aggregates in NFZ-Si, which creates additional stress in the material as the implant dose rises.Overall, N implanted Cz-Si preserves a good crystalline structure compared to FZ-Si, which undergoes more damage as the dose increases. Ion implantation generates vacancy and self-interstitial bands, leading to a supersaturated level of nitrogen trapped in the crystal. Previous work has shown that in Cz-Si, oxygen and vacancies couple with nitrogen to form N-V-O complexes, which enhance the material's strength. This could explain the relaxation observed at high doses in NCz-Si. We believe this is why N-implanted Cz-Si exhibits higher crystalline quality relative to its NFZ-Si counterpart. NFZ-Si samples sustain more damage from implantation due to their softness, suggesting that oxygen helps heal defects during implantation, up to a certain dose limit. Figure 1

Determination of Flash Point in Gel-Polymer Electrolytes for Their Use in Safe Quasi-Solid Batteries

Gel polymer materials have recently gained significant attention for their use as electrolytes in quasi-solid batteries. A gel polymer electrolyte consists of a polymeric matrix in which a solvent is trapped. This composition offers a compromise between high ionic conductivity (facilitated by the solvent) and enhanced safety (provided by the polymeric matrix), making them a promising technology for short-term high-performance batteries. It is widely accepted that the polymeric matrix plays an important role in improving the overall safety of the electrolyte, notably by increasing its mechanical strength.1,2 However, there is a lack of systematic research on the effect of the polymeric matrix on the overall flammability. In the field of battery research, there is no standardized test to define and quantify the flammability of a gel polymer electrolyte.This work focuses on the development of a flammability test that is adapted for the analysis of gel polymer electrolytes. The flash point temperature represents the lowest temperature at which there is enough vapor to produce a flame with a source of ignition. Flash point measurement is a standardized test extensively studied for liquid chemicals. In our previous work, flash point measurement was adapted for the analysis of gel polymer electrolytes by coupling the flash point instrument with a thermal imaging camera.3 This novel methodology and the subsequent data processing were automated and treated through computer vision to accurately identify the flash point temperature, eliminating potential errors associated with human interpretation on whether the flame is seen or not. The employed deep learning algorithms exhibit high accuracy in detecting the presence of the flash point with a small amount of solvent (15 L), making then suitable for the analysis of gel-polymer electrolytes. Moreover, the effect of intermolecular interactions between the solvent and the salt and between the solvent and the polymeric matrix have been studied by FT-IR and were shown to increase the flash point temperature.References Castillo, A. Santiago, X. Judez, I. Garbayo, J. A. C. Clemente, M. C. Morant-Minana, A. Villaverde, J. A. Gonzalez-Marcos, H. Zhang, M. Armand and C. M. Li, Chem Mater, 2021, 33, 8812-8821. 23. Li, J. Chen, L. Fan, X. Kong and Y. Lu, Green Energy & Environment, 2016, 1, 18-42.St-Antoine,C., et al., Journal of Materials Chemistry A, 2023. 11(20): p. 10984-10992. Figure 1

Insights into tail-belting by wild mice encourages fresh perspectives on physiological mechanisms that safeguard mammal tissues from freezing

We investigated tail-belting (TB), the newly-discovered freeze avoidance behavior among wild rodents. When temperatures dropped to -6 °C, wild mice (Apodemus agrarius and Apodemus flavicollis) were observed curling their tails inward and positioning it on the back. A literature search suggested TB had never been documented, presumably because rodents, especially in the laboratory, are seldomly assayed under cold stress. Due to the infrequent occurrence of the behavior, we used infrared and thermal cameras to confirm observations. We also collected tail-skin samples to investigate whether any physiological mechanisms might co-occur with TB. If such mechanisms were found, they could inform wider debate involving freeze protection among mammals, and could potentially lead to understanding mammal susceptibility or resilience to sudden temperature changes such as those associated with climate change. Lastly, we scored behaviors by bank voles (Myodes glareolus) which unexpectedly visited chambers. Across four winters, we observed TB in both Apodemus species during subzero conditions, but bank voles never performed the behavior. We also confirmed that TB occurs as an adaptive reflex which warms the tail. From tissue samples, we found that free amino acids, peptides, and glycoproteins were significantly higher during cold-stress. Thus, TB may have been accompanied by the expression of cold-protective proteins which ostensibly enable the peripheral body parts of mammals to survive temperatures well below 0 °C. These findings should inspire new dialogue regarding the role of lipids in tissues of peripheral organs in mammals. By extension, our findings may lead to the discovery of a putative cryoprotection mechanism among mammals.

On the Effects of Substrate Temperature on Glass Internal Modification Using Femtosecond Laser Pulses

Abstract The effects of substrate temperature on laser focal position and tear-drop morphology in laser internal modification of glass are investigated. A model is derived to predict the shift of the tear-drop at high substrate temperature. Femtosecond laser pulses are scanned inside borosilicate glass at room temperature, 150 °C, and 200 °C using a pulse energy of 4.5–18 µJ, a scanning speed of 5–20 mm/s, and a distance between lens and glass of 9.56–10.76 mm. Temperature effects are characterized by defining a height (width) gain ratio as the ratio between the tear-drop height (width) measured at high temperature to that measured at room temperature. Thermal expansion is simulated using a profile temperature acquired by a thermal camera and image processing. Results show that substrate temperature has a significant effect on self-focusing, and modifications at 200 °C show a relaxed discoloration compared to 150 °C and room temperature. Analytical predictions match the measurements of focal position in the distance of 9.56–9.96 mm at 200 °C where self-focusing is not significant while underestimating the measurements for the distance of 10.76 mm and 10.36 mm by 30–50 µm. At 200 °C, the tear-drop's gain ratio is increased when the pulse energy is increased in the range of 4.5–18 µJ. Within this pulse energy range and at 200 °C, the maximum width gain is 10–100% higher compared to the maximum height gain.

Comprehensive Systems Review for Accident Detection, Prevention, and Real-Time Alerting Mechanisms

The growth of technology and infrastructure at a rapid pace has made our lives easier. The increased frequency of road accidents and possible traffic dangers brought about by newer technologies has resulted in a significant loss of life and property due to inadequate emergency facilities and preventive measures. The greatest hazard to human life is traffic accidents. High speeds combined with intoxication are the main causes of many accidents. Vehicle safety and accident prevention and detection systems are important because they reduce the number of accidents and the time it takes for an accident to be recognized, allowing rescue personnel to attend to injured victims more quickly. This article presents the MCU-designed Vehicle Tracking and Accident Detection system along with several accident avoidance strategies.A prototype of an accident prevention & detection system using a vibration sensor as the detector and an ultrasonic sensor, or IR sensor, is to detect a signal in case of a near collision or a distance between two vehicles to prevent an accident. Even after this, if an accident occurs, they send a signal to the connected microcontroller via the vibration sensor. When the signal is received, the controller activates the relay, causing the airbag to deploy and the brakes to automatically lock. Meanwhile, a message is sent to the nearest help center, so ambulance service and other aid can come in the quickest time feasible.A black box gathers driving information before, during, and after a crash about the vehicle in which it is installed. The data collected by the black box includes speed, braking, acceleration, steering, and airbag deployment. The help can be provided in areas like vehicle safety, collision victim care, insurance corporations, vehicle crash investigations, and improving road conditions through the automotive black box system, which can reduce death rates to a significant level. The main aim of this paper is to include the overview and basics of certain techniques utilized to prevent, detect accidents, and alert the nearby help centers for the fastest aid in case of an accident.

Multi-Rotor Drone-Based Thermal Target Tracking with Track Segment Association for Search and Rescue Missions

Multi-rotor drones have expanded their range of applications, one of which being search and rescue (SAR) missions using infrared thermal imaging. This paper addresses thermal target tracking with track segment association (TSA) for SAR missions. Three types of associations including TSA are developed with an interacting multiple model (IMM) approach. During multiple-target tracking, tracks are initialized, maintained, and terminated. There are three different associations in track maintenance: measurement–track association, track–track association for tracks that exist at the same time (track association and fusion), and track–track association for tracks that exist at separate times (TSA). Measurement–track association selects the statistically nearest measurement and updates the track with the measurement through the IMM filter. Track association and fusion fuses redundant tracks for the same target that are spatially separated. TSA connects tracks that have become broken and separated over time. This process is accomplished through the selection of candidate track pairs, backward IMM filtering, association testing, and an assignment rule. In the experiments, a drone was equipped with an infrared thermal imaging camera, and two thermal videos were captured of three people in a non-visible environment. These three hikers were located close together and occluded by each other or other obstacles in the mountains. The drone was allowed to move arbitrarily. The tracking results were evaluated by the average total track life, average mean track life, and average track purity. The track segment association improved the average mean track life of each video by 99.8% and 250%, respectively

Multisource data fusion for defect detection in composite additive manufacturing using explainable deep neural network

Composite additive manufacturing has the potential to replace traditional composite manufacturing for aerospace applications. Additive manufacturing provides greater adaptability to complex designs, reduce material waste, and streamline the production process. However, in comparison to conventional composite manufacturing methods, additive manufacturing is inherently more susceptible to processing anomalies and defects, primarily due to the novelty of the process and the absence of applied pressure. To effectively capitalize on the benefits of additive manufacturing, a quality and reliability assurance system is critical. In this study, we have generated multisource data to analyze the inner layer thermography and surface quality, employing a multi-camera system including thermal and charge-coupled device cameras. This multisource data is utilized in an explainable zero bias deep neural network framework to detect manufacturing defects. This deep learning algorithm introduces a new zero bias layer following the regular dense layer. After being trained on normal (defect-free) samples of each data stream, the trained model is transformed into an anomaly detector by extracting low dimension features from the zero-bias layer. This allowed identification various anomalies without having to train the model on any defective inputs. As a result, the model's accuracy to detect multiple types of anomalies is higher with multisource data compared to models based on single data source. Anomaly detection accuracy was 99.72% when using data from multiple sources, 98.28% when using only CCD images, and 95% when using only thermal camera data.

Federated Learning Using Multi-Modal Sensors with Heterogeneous Privacy Sensitivity Levels

Data from multi-modal sensors, such as Red-Green-Blue (RGB) cameras, thermal cameras, microphones, and mmWave radars, have gradually been adopted in various classification problems for better accuracy. Some sensors, like RGB cameras and microphones, however, capture privacy-invasive data, which are less likely to be used in centralized learning. Although the Federated Learning (FL) paradigm frees clients from sharing their sensor data, doing so results in reduced classification accuracy and increased training time. In this article, we introduce a novel Heterogeneous Privacy Federated Learning (HPFL) paradigm to better capitalize on the less privacy-invasive sensor data, such as thermal images and mmWave point clouds, by uploading them to the server for closing the performance gap between FL and centralized learning. HPFL not only allows clients to keep the more privacy-invasive sensor data private, such as RGB images and human voices, but also gives each client total freedom to define the levels of their privacy concern on individual sensor modalities. For example, more sensitive users may prefer to keep their thermal images private, while others do not mind sharing these images. We carry out extensive experiments to evaluate the HPFL paradigm using two representative classification problems: semantic segmentation and emotion recognition. Several key findings demonstrate the merits of HPFL: (i) compared to FedAvg, it improves foreground accuracy by 18.20% in semantic segmentation and boosts the F1-score by 4.20% in emotion recognition, (ii) with heterogeneous privacy concern levels, it achieves an even larger F1-score improvement of 6.17–16.05% in emotion recognition, and (iii) it also outperforms the state-of-the-art FL approaches by 12.04–17.70% in foreground accuracy and 2.54–4.10% in F1-score.

Assessment of Tube–Fin Contact Materials in Heat Exchangers: Guidelines for Simulation and Experiments

This paper describes experiments on finned tube heat exchangers, focusing on reducing the thermal contact resistance at the contact between the pipe and the lamella. Various contact materials, such as solders and adhesives, were investigated. Several methods of establishing contact were tested, including blowtorch soldering, brazing, and furnace soldering. Thermal camera measurements were carried out to assess the performance of the contact materials. Moreover, finite element analysis was performed to evaluate the contact materials and establish guidelines in the fin–tube connection modeling by comparing simplified models with the realistic model. Blowtorch brazing tests were successful while soldering attempts failed. During the thermographic measurements, reflective surfaces could be measured after applying a thin layer of paint with high emissivity. These measurements did not provide valuable results; thus, the contact materials were assessed using a finite element analysis. The results from the finite element analysis showed that all the inspected contact materials provided better heat transfer than not using a contact material. The heat transfer rate of the tight-fit realistic model was found to be 33.65 for air and 34.9 for the Zn-22Al contact material. This finding could be utilized in developing heat exchangers with higher heat transfer with the same size.

Impact of climate change on vegetation growth trends in a citrus farmland in the south-eastern Sicily

Abstract. The environmental repercussions of climate change triggered substantial alterations in existing ecosystem balances leading to a gradual reduction in biodiversity and the increasing degradation of soil and the built environment. This has a direct effect on agricultural production in particular, leading to increased crop water requirements, the spread of new pests and pathogens that are difficult to manage, and, thus, a general reduction in productivity. Through the analysis of multispectral data remotely sensed with latest generation of optical sensors (RGB, thermal, NIR and multi-spectral cameras), it is possible to recognize the health status and growth stages of crops. In fact, in relation to the amount of radiation reflected in the different bands it is possible to calculate spectral indices, or vegetation indices. The main vegetative indices are NDVI, SAVI, MSAVI2, GNDVI and NDRE, which are calculated by differentially combining the ultraviolet spectrum, the visible range, and the near and mid-infrared band.In this paper the outcomes of a multi-year monitoring of a citrus grove field near Rosolini (Syracuse), in south-eastern Sicily-Italy, are presented. This monitoring activity was conducted to highlight areas of productive greenery under stress, on which to act as a priority with specific agronomic interventions. In parallel with the direct monitoring phases of the citrus grove, the trend of average monthly and annual temperature and precipitation values over the past 24 years was also studied to more specifically contextualize the possible presence of significant variations in climatic trends and relate them to the results returned by the calculation of the indices.

Managing Residual Heat Effects in Femtosecond Laser Material Processing by Pulse-on-Demand Operation

Femtosecond laser processing combines highly accurate structuring with low residual heating of materials, low thermal damage, and nonlinear absorption processes, making it suitable for the machining of transparent brittle materials. However, with high average powers and laser pulse repetition rates, residual heating becomes relevant. Here, we present a study of the femtosecond laser pulse-on-demand operation regime, combined with regular scanners, aiming to improve throughput and quality of processing regardless of the scanner’s capabilities. We developed two methods to define the needed pulse-on-demand trigger sequences that compensate for the initial accelerating scanner movements. The effects of pulse-on-demand operation were studied in detail using direct process monitoring with a fast thermal camera and indirect process monitoring with optical and topographical surface imaging of final structures, both showing clear advantages of pulse-on-demand operation in precision, thermal effects, and structure shape control. The ability to compensate for irregular scanner movement is the basis for simplified, cheaper, and faster femtosecond laser processing of brittle and heat-susceptible materials.

Visual search for hazardous items: using virtual reality (VR) in laypersons to test wearable displays for firefighters

In virtual reality (VR), we assessed how untrained participants searched for fire sources with the digital twin of a novel augmented reality (AR) device: a firefighter’s helmet equipped with a heat sensor and an integrated display indicating the heat distribution in its field of view. This was compared to the digital twin of a current state-of-the-art device, a handheld thermal imaging camera. The study had three aims: (i) compare the novel device to the current standard, (ii) demonstrate the usefulness of VR for developing AR devices, (iii) investigate visual search in a complex, realistic task free of visual context. Users detected fire sources faster with the thermal camera than with the helmet display. Responses in target-present trials were faster than in target-absent trials for both devices. Fire localization after detection was numerically faster and more accurate, in particular in the horizontal plane, for the helmet display than for the thermal camera. Search was strongly biased to start on the left-hand side of each room, reminiscent of pseudoneglect in scene viewing. Our study exemplifies how VR can be used to study vision in realistic settings, to foster the development of AR devices, and to obtain results relevant to basic science and applications alike.

Smart Park: Innovating the Future of Vehicle Parking

Effective parking solutions become increasingly important as urban areas increase because of wasted space, time-consuming searches, and traffic congestion. By utilizing cutting edge technologies to improve parking management and user experience, the Smart Vehicle Parking System (SVPS) tackles these issues. To deliver dynamic solutions, SVPS combines sophisticated algorithms, sensor networks, and real-time data. Parking space sensors track occupancy and transmit data to a central system, where machine learning maximizes space distribution and directs vehicles to open spaces. This smart parking system is software and hardware based. Different algorithms are used to find vacant slots using machine learning and deep learning. IR sensors, RFID, Servomotors and ESP 32 microcontrollers and Internet of things are useful to find vacant slots in real time signal processing. In traditional method it is difficult to search free space, and this method is time consuming, Smart parking system overcome this problem with new technology and improve parking management system. The SVPS not only improves urban mobility but also contributes to reducing vehicle emissions and enhanced overall efficiency. Keywords— smart parking, integration, secure payment,advanced technologies, convenient.