Infrared Thermography Imaging Research Articles

Selective Heat Input for Low Temperature Metallic Wafer Level Bonding

Introduction Wafer level packaging (WLP) is a key technology for material integration in Micro-Electro-Mechanical-Systems (MEMS). To guarantee their reliable functionality, these MEMS often require a hermetically sealed and mechanically stable package. On industrial scale, MEMS packaging is done with wafer bonding technologies such as anodic, glass-frit, eutectic or solid-liquid inter diffusion bonding. These bonding technologies require process temperatures of 200 °C to more than 400 °C to form a stable bond between two substrates. To integrate heterogeneous substrates and fully processed ASICs in one package, the thermal stress has to be reduced. These requirements can be met by new innovative technologies such as reactive and inductive wafer bonding, enabling a selective heating of the bond interface in a very short time only. Reactive wafer bonding Reactive bonding utilizes a self-propagating exothermic reaction for selective (SER) heating of the bond interface only. The SER occurs in sputtered nanometer-scaled multilayer stack, consisting of Pd/Al, Ti/Si or CuO/Al with a total thickness between 1 µm and 4 µm. Upon ignition, the SER propagates through the multilayer stack (Fig. 1) with a reaction velocity of 1 m/s to 80 m/s, while melting the bond metallization adjacent to the reactive multilayers in the reaction zone. During solidification of the metallization, a mechanical strong, hermetic bond is formed between both substrates. To ensure close contact between the multilayers and the bond metallization, a bond pressure of 3 MPa to 5 MPa is applied to the substrates.The reactive properties such as reaction temperature and energy level strongly depend on the reactive material system. In comparison to medium energetic Ti/Si, high energetic systems such as Pd/Al and CuO/Al generate more energy at a higher reaction temperature. The energy level of the integrated reactive material system (iRMS) can be tuned by total multilayer thickness. By varying the multilayer design, the iRMS can be adapted to the application, bond metallization and substrate material. Inductive wafer bonding During inductive bonding, the necessary heat input into the interface is generated by an external electromagnetic field. This field induces a voltage in the electrically conductive bond material. The resulting eddy current causes a heat flow mainly by Joule heating. To ensure an encapsulation of all MEMS devices at wafer level, the most important requirements are homogeneous heat distribution and rapid heat generation in each bond layer or structure. Considering these demands, the here presented method includes four innovative claims. The most important point includes the inductor coil as well as the conception of the inductor geometry, which has to be adapted to the bond layout for homogeneous heat generation in a single process step (I). The heating system, consisting of an induction generator and an L-C resonant circuit, is adjusted to the bonding application (II). Especially, an appropriate tuning of frequency and input power is necessary to achieve fast heating rates with a high efficiency factor and low energy losses. To ensure wafer level bonding, the inductor is integrated in a bond module, which is compatible with an industrial wafer bonder (III). With an implementation of all these components in one process, an induction-assisted wafer level bonding is accomplished. Scope In this paper, two innovative techniques for bonding with selective heating are presented. The focus for reactive bonding lies on the new cost efficient CuO/Al material system. Starting with the multilayer design, deposition and patterning as well as the bonding process will be explained, including the influence of the layer design on the bond strength (Fig. 2). The detailed analysis of the bond interfaces enables a comparison of the reactive bonded interface with standard wafer level packaging technologies such as thermal compression or glass-frit bonding (Tab. 1).The focus for inductive bonding in this paper is the support and enhancement of the solid-liquid inter diffusion (SLID) bonding process at wafer level by using selective and energy-efficient induction heating of Cu-Sn layers (Fig. 3). To predict the heat distribution within the bond structures in dependence of the inductor geometry and operating frequency, Finite Element Analysis (FEA) with the electromagnetic AC/DC module of COMSOL Multiphysics is performed (Fig. 4 left). Infrared thermography imaging was used to investigate the heating distribution as well as the achieved heating rate of more than ∆T = 150 K/s (Fig. 4 right). Finally, the inductive SLID bonding is carried out in 120 s by applying a bond pressure of approx. 2 MPa and characterized by determination the bond strength. Figure 1

Robust Detection, Classification and Localization of Defects in Large Photovoltaic Plants Based on Unmanned Aerial Vehicles and Infrared Thermography

The efficiency and profitability of photovoltaic (PV) plants are highly controlled by their operation and maintenance (O&M) procedures. Today, the effective diagnosis of any possible fault of PV plants remains a technical and economic challenge, especially when dealing with large-scale PV plants. Currently, PV plant monitoring is carried out by either electrical performance measurements or image processing. The first approach presents limited fault detection ability, it is costly and time-consuming, and it is incapable of fast identification of the physical location of the fault. In the second approach, Infrared Thermography (IRT) imaging has been used for the characterization of PV module failures, but their setup and processing are rather complex and an experienced technician is required. The use of Unmanned Aerial Vehicles (UAVs) for IRT imaging of PV plants for health status monitoring of PV modules has been identified as a cost-effective approach that offers 10–-15 fold lower inspection times than conventional techniques. However, previous works have not performed a comprehensive approach in the context of automated UAV inspection using IRT. This work provides a fully automated approach for the: (a) detection, (b) classification, and (c) geopositioning of the thermal defects in the PV modules. The system has been tested on a real PV plant in Spain. The obtained results indicate that an autonomous solution can be implemented for a full characterization of the thermal defects.

Infrared thermal imaging associated with pain in laboratory animals.

The science of animal welfare has evolved over the years, and recent scientific advances have enhanced our comprehension of the neurological, physiological, and ethological mechanisms of diverse animal species. Currently, the study of the affective states (emotions) of nonhuman animals is attracting great scientific interest focused primarily on negative experiences such as pain, fear, and suffering, which animals experience in different stages of their lives or during scientific research. Studies underway today seek to establish methods of evaluation that can accurately measure pain and then develop effective treatments for it, because the techniques available up to now are not sufficiently precise. One innovative technology that has recently been incorporated into veterinary medicine for the specific purpose of studying pain in animals is called infrared thermography (IRT), a technique that works by detecting and measuring levels of thermal radiation at different points on the body’s surface with high sensitivity. Changes in IRT images are associated mainly with blood perfusion, which is modulated by the mechanisms of vasodilatation and vasoconstriction. IRT is an efficient, noninvasive method for evaluating and controlling pain, two critical aspects of animal welfare in biomedical research. The aim of the present review is to compile and analyze studies of infrared thermographic changes associated with pain in laboratory research involving animals.

A robust optical flow motion estimation and correction method for IRT imaging in brain surgery

ABSTRACT In brain surgery, respiration motion, outliers, and occlusions create artefacts in Infrared Thermography (IRT) imaging. In this paper, we propose a robust method to handle multiple motion, outliers, and occlusions in IRT images which consists of two phases: preprocessing and image motion estimation. In the preprocessing phase, the Region of Interest (RoI) segmentation is employed to extract the brain cortex only. Thereafter, the Phase Correlation method is employed to compensate for large motion followed by occlusion masking based on an approach applying Cellular Nonlinear Networks (CNN). Next, intensity adjustment is made with respect to the reference image. Then, a Gaussian filter is applied. In the following phase, the image motion is estimated by employing Combined Local-Global (CLG) optical flow method. In order to find the best regularization coefficient for the spatial coherency term and the number of iterations for recursive optical flow estimation, we generated ground truth and evaluated the accuracy of the estimated motion vectors based on Average Angular Error (AAE) and Average Magnitude Error (AME). The efficiency improvement of the proposed method was tested on 1024 IRT images based on different comparisons. Thereby, the proposed method shows promising results for motion estimation and correction application in brain surgery.

Methodology to evaluate temperature changes in multiple sclerosis patients by calculating texture features from infrared thermography images

ABSTRACT Multiple sclerosis (MS) is a progressive and degenerative disease that causes nerve conduction blocks due to demyelination in the central nervous system. Most MS patients experience a worsening of clinical signs and neurological symptoms when they are exposed to heat due to a thermoregulatory dysfunction. This paper proposes a novel methodology to understand temperature changes in MS patients by obtaining and evaluating texture features from infrared thermography (IRT) images. For that purpose, images of the legs of a MS patient and a healthy control subject with similar physical characteristics (while at rest and in a standing position) were recorded using a FLIR A655SC infrared camera. In the quantitative analysis of the resulting IRT images, three texture features (average, entropy, and uniformity) were computed, and the results were compared using statistical techniques. The statistical analysis showed that temperatures in the MS patient were not normally distributed, while those in the healthy control subject were normally distributed. In addition, significant differences in average, entropy, and uniformity were found between subjects. This methodology enables a quantitative evaluation of thermal distributions over different regions of the body and can be used in further studies into temperature changes in MS patients.

A building unit decomposition model for energy leakage by infrared thermography image analysis

A quantitative energy leakage model was developed based on the thermography image data measured for both external and internal building surfaces. The infrared thermography images of both surfaces of doors, windows, and walls of an office building in the Hengqin Campus of University of Macao were taken at various times in a day for four seasons. The transient heat flux for sample units were obtained based on measurements of the seasonal transient local temperature differences and calculations of the effective thermal conductivity from the multiple-layer porous medium conduction model. Effects of construction unit types, orientations, and seasons were quantitatively investigated with unit transient orientation index factors. The corresponding electric energy consumption was calculated based on the air conditioning system coefficient of performance of heat pump and refrigerator cycles for different seasons. The model was validated by comparing to the electric meter records of energy consumption of the air conditioning system. The uncertainties of the predicted total building energy leakage are about 14.7%, 12.8%, 12.4%, and 15.8% for the four seasons, respectively. The differences between the predicted electric consumption and meter values are less than 13.4% and 5.4% for summer and winter, respectively. The typical daily thermal energy leakage value in winter is the highest among the four seasons. However, the daily electric energy consumption by the air conditioning system in summer and autumn is higher than that in winter. The present decomposition model for energy leakage is expected to provide a practical tool for quantitative analysis of energy leakage of buildings.

Use of infrared thermography imaging for assessing heat tolerance in high and low iron pearl millet lines

In the arid regions of Asia and Africa, pearl millet serves as a staple source of dietary energy and mineral micronutrients for millions of people. These regions are more vulnerable to increased temperature. The availability of rapid and efficient screening tools based on the relevant non-destructive quantifiable traits would facilitate pearl millet improvement for heat tolerance. The objective of this study was to evaluate pearl millet lines with contrast micronutrients for heat tolerance using infrared thermal imaging, a rapid proxy-canopy (panicle and flag leaf) temperature measurement. Results showed the highly significant genotypic differences between high-Fe and low-Fe genotypes for grain Fe and Zn densities and agronomic traits. Both high-Fe and low-Fe group genotypes differed significantly for panicle temperature depression (PTD) during high- vapor deficit (VPD) at stigma stage (3.0 to 6.73°C). PTD values were positive across all genotypes during stigma stage and were very low or negative during the low-VPD. Cooler canopy temperature (high-PTD) was observed during stigma stage rather than seed-set stage at higher-VPD in both high-Fe and low-Fe genotypes. The cooler temperature achieved by panicle might be helpful in maintaining stigma receptivity for longer periods in the female parents, whereas in male parents it might be helpful in maintaining pollen viability for longer periods. Flag leaf temperature (FTD) was cooler than PTD at both high-VPD and low-VPD as well in both stigma (less by 2.1°C) and grain-filling stage (less by 2.7°C), again signifying that the reproductive parts are more prone to heat stress as compared to vegetative parts. Since, thermal imaging discriminates the heat stress and non-stress canopies, this can serve as a proxy canopy temperature tool for heat stress tolerance screening in pearl millet.

Hygrothermal and energy retrofit planning of masonry façade historic building used as museum and office: A cultural properties case study

The optimization of building materials and energy technologies is imperative to reduce building energy consumption and thereby minimize greenhouse gas emissions. Common energy retrofits include the addition of walls or roof insulation, window renovations, heating upgrades, installation of ventilating and air conditioning (HVAC) systems and managing the building’s operational schedules. Historic buildings differ from other general buildings in two major aspects that affect energy retrofits. This study analyzed the effect of) Energy Efficiency Measure (EEMs) packages, which improve the hygrothermal and energy performances using infrared thermography images and building simulation. The hygrothermal and energy performances were analyzed separately for the office and museum. EEM1 is a package that adds 150 mm PUR insulation only to roof and wall. EEM1 significantly reduced the risk of mold growth after three years, as mold index values approached zero. The results of EEM1 show that additional application of insulation could reduce the risk of mold growth. Results of the energy retrofit analysis indicate that improvements in the window performance and airtightness reduce the heating energy consumption. In particular, the improvement in airtightness drastically reduced heating energy consumption in winter. Whereas all EEMs packages applied to buildings reduced heating energy consumption, EEM5 achieved a heating energy reduction of 72% and a total energy consumption reduction of 60%. EEM5 is a package that integrates all the technologies for maximum energy savings including adding insulation, improving airtightness, replacing window systems, installing internal blinds, and replacing LED lighting. Building performance retrofits will be the subject of further studies, enabling the continued use and conservation of historic buildings.

Broiler stress responses to light intensity, flooring type, and leg weakness as assessed by heterophil-to-lymphocyte ratios, serum corticosterone, infrared thermography, and latency to lie

Stress and leg weakness are detrimental to broiler production, health, and welfare. Traditional methods to evaluate stress may be stressful to the bird because they are invasive and require handling and restraint. Two studies examined the effects of light intensity and flooring on the following in broilers: 1) traditional methods for assessing stress using heterophil-to-lymphocyte ratios and serum corticosterone (CORT) concentrations, 2) noninvasive measures of stress from infrared thermography (IRT) eye and beak surface temperatures, and 3) latency-to-lie (LTL) test times of birds tested individually and in groups of 5. Day-of-hatch male broiler chicks were placed into 6 pens (N = 120 chicks/pen). At 1 wk, pens were allocated to 3 light intensity treatments (2, 5, or 10 lux). At 4 wk, half of the birds from each pen were moved to a pen with wire flooring and the same light intensity. At 1, 4, 5, and 8 wk, blood samples were collected and IRT images of the heads of 5 clinically healthy broilers from each pen were captured. In study 2, IRT images of the heads of birds that became lame in the wire flooring pens were taken. There were no treatment effects on the LTL times of birds tested in groups or individually (P > 0.05). On day 56 in study 1, birds on wire flooring had elevated heterophil-to-lymphocyte ratios and CORT concentrations (P ≤ 0.002) and depressed IRT eye and beak temperatures (P < 0.0001). In both studies, there were negative correlations between CORT concentrations and IRT beak surface temperatures (P < 0.05). Lame birds had lower IRT eye and beak surface temperatures than sound birds (P ≤ 0.004), and the IRT beak surface temperatures of lame birds were lower than their eye surface temperatures (P = 0.004) in study 2. These studies indicate that the IRT surface temperatures of the eye, and more distinctly of the beak, can be used as sensitive noninvasive indicators of stress.

Infrared thermography for evaluation of the environmental thermal comfort for livestock.

The objective of this study was to assess the use of infrared thermography as a microclimate-evaluating tool and an estimate of the thermal comfort provided by four types of tree to cattle under grazing conditions in the central region of Brazil. The experiment was conducted at the Embrapa Beef Cattle Company, in Campo Grande, MS, Brazil, from June to August 2015. Evaluations were carried out over four consecutive days, at 1-hour intervals, from 8:00 a.m. to 4:00 p.m. (local time; GMT - 4:00). Infrared thermography images of tree crowns and soil surface underneath them from the shadow projection of four tree species native to the Brazilian cerrado (savannah-like) biome were obtained. The microclimate was assessed by estimation of thermal indices: temperature and humidity index, black globe, and radiation thermal load. The previous was calculated based on records of air temperature, dew point temperature, black globe temperature, air relative humidity, wind speed, and solar radiation. The geometrical settings of the trees were assessed for each tree component. Five thematic groups were formed based on multiple factor analysis that summarizes three synthetic analytical dimensions to explain the total variance among the studied elements and the existing correlations between groups. Positive linear correlations were found between thermography and the temperature measurements, thermal comfort indices, and radiation, suggesting that infrared thermography can be used as a tool for estimating and monitoring the microclimate and thermal comfort, presenting a potential use of measurement in agroforestry systems.

Valve regulated lead acid battery diagnostic system based on infrared thermal imaging and fuzzy algorithm

In recent times, advanced inspection technique like infrared thermography (IRT) has been used widely for fault diagnosis of electrical equipment in non-contact, non-destructive and non-invasive manner. Manual classification of faults from the IRT images requires more time and effort. In this work, an intelligent scheme for predictive fault diagnosis in VRLA battery is presented for scheduling its preventive maintenance. IR images of pristine and aged VRLA battery in uninterrupted power supply application are acquired using IR camera at different discharging cycles. Image processing of IR images is performed for detection of faults. In order to intelligently classify the faults a fuzzy inference system is developed. Proposed scheme for automatic diagnosis and classification of faults in VRLA battery is implemented using LabVIEW 2015 software. The output information defining the condition of VRLA battery is displayed on front panel of LabVIEW and stored in MS Excel file with the time stamp at hard disk of host computer for further reliability analysis. Based on occurrence of major faults in VRLA battery, alert signal is send to intended users at both onsite and remote locations. To facilitate remote condition monitoring of VRLA battery, front panel information is continuously provided to remote user using web publishing tool of LabVIEW. Using proposed technique, the fault diagnosis of lead acid batteries in different battery applications can similarly be performed in non-invasive manner.

Distribution of Superficial Body Temperature in Horses Ridden by Two Riders with Varied Body Weights

Simple SummaryLeisure horse riding is becoming an increasingly popular activity, but often the regard for the welfare of recreational horses is insufficient. Most often, this situation is associated with workload or insufficient knowledge of the owners. Another problem may be overloading the animals with excessive weight of the rider, because the problem of obesity affects more and more societies. Carrying heavy loads significantly reduces horse comfort and affects movement mechanics. Prolonged use of the horse in this way may translate into health and lameness issues in the back and limbs. There are currently no strict weight limits for riders. However, we believe that horse users should pay more attention to this problem. As shown in this study, a horse’s load above 20% of his body weight, even with little effort, affects changes in surface temperature and the activity of the autonomic nervous system.It was assumed that a horse with its rider body weight found in the upper limit may negatively impact the horse’s welfare. The objective of this paper was to analyze the differences in body temperature and selected heart rate parameters in horses in response to physical exercise accompanied by various rider’s body weight loads. The study was carried out on 12 leisure, 10–15-year-old warmblood geldings. The horses were ridden by two equally qualified riders whose body weights were about 20% and 10% of the average body weight (BW) of the animals (about 470 kg). Each rider rode each of the 12 horses for 13 min walking and 20 min of trotting. Images of the horse at rest, after physical exercise directly after unsaddling, and during the recovery phase (10 min after unsaddling) were taken with an infrared thermography camera. For analysis, the temperatures of selected body parts were measured on the surface of the head, neck, front, middle, and back (croup) parts of the trunk, forelimb, and hind limb. Immediately after the infrared thermography images were taken, the rectal temperature of the horse was measured. The heart rate parameters were measured at rest for 10 min directly before, during, and 10 min following the end of a training session. A multivariate analysis of variance (ANOVA) for repeated measurements was performed. Statistical significance was accepted for p < 0.05. A rider BW load on a horse of approximately 20% of the horse’s BW led to a substantial increase in the superficial temperatures of the neck, front, middle, and back parts of the trunk in relation to these body parts’ average temperatures when the load was about 10% BW. The head and limb average temperatures were not significantly affected by the load of the exercised horse. A horse’s load above 20% of his body weight, even with little effort, affects changes in surface temperature and the activity of the autonomic nervous system.

On Field Infrared Thermography Sensing for PV System Efficiency Assessment: Results and Comparison with Electrical Models.

The evaluation of photovoltaic (PV) system’s efficiency loss, due to the onset of faults that reduce the output power, is crucial. The challenge is to speed up the evaluation of electric efficiency by coupling the electric characterization of panels with information gathered from module diagnosis, amongst which the most commonly employed technique is thermographic inspection. The aim of this work is to correlate panels’ thermal images with their efficiency: a “thermal signature” of panels can be of help in identifying the fault typology and, moreover, for assessing efficiency loss. This allows to identify electrical power output losses without interrupting the PV system operation thanks to an advanced PV thermography characterization. In this paper, 12 faulted working panels were investigated. Their electrical models were implemented in MATLAB environment and developed to retrieve the ideal I-V characteristic (from ratings), the actual (operative) I-V characteristics and electric efficiency. Given the curves shape and relative difference, based on three reference points (namely, open circuit, short circuit, and maximum power points), faults’ typology has been evidenced. Information gathered from infrared thermography imaging, simultaneously carried out on panels during operation, were matched with those from electrical characterization. Panels’ “thermal signature” has been coupled with the “electrical signature”, to obtain an overall depiction of panels’ health status.

Implementation of artificial intelligence and non-contact infrared thermography for prediction and personalized automatic identification of different stages of cellulite

BackgroundCellulite is a common physiological condition of dermis, epidermis, and subcutaneous tissues experienced by 85 to 98% of the post-pubertal females in developed countries. Infrared (IR) thermography combined with artificial intelligence (AI)-based automated image processing can detect both early and advanced cellulite stages and open up the possibility of reliable diagnosis. Although the cellulite lesions may have various levels of severity, the quality of life of every woman, both in the physical and emotional sphere, is always an individual concern and therefore requires patient-oriented approach.ObjectivesThe purpose of this work was to elaborate an objective, fast, and cost-effective method for automatic identification of different stages of cellulite based on IR imaging that may be used for prescreening and personalization of the therapy.Materials and methodsIn this study, we use custom-developed image preprocessing algorithms to automatically select cellulite regions and combine a total of 9 feature extraction methods with 9 different classification algorithms to determine the efficacy of cellulite stage recognition based on thermographic images taken from 212 female volunteers aged between 19 and 22.ResultsA combination of histogram of oriented gradients (HOG) and artificial neural network (ANN) enables determination of all stages of cellulite with an average accuracy higher than 80%. For primary stages of cellulite, the average accuracy achieved was more than 90%.ConclusionsThe implementation of computer-aided, automatic identification of cellulite severity using infrared imaging is feasible for reliable diagnosis. Such a combination can be used for early diagnosis, as well as monitoring of cellulite progress or therapeutic outcomes in an objective way. IR thermography coupled to AI sets the vision towards their use as an effective tool for complex assessment of cellulite pathogenesis and stratification, which are critical in the implementation of IR thermographic imaging in predictive, preventive, and personalized medicine (PPPM).

Optimal region growing and multi-kernel SVM for fault detection in electrical equipments using infrared thermography images

Infrared thermography (IRT) has played an essential part in observing and examining thermal defects of electrical equipment without ending, which has vital enormity for the dependability of electrical recorded. This paper dissected the electrical parts are faulted or non-faulted with the help of segmentation and classification model. The features are calculated from the input thermal images and regions of interest (ROI) is segmented by utilising optimal region growing (ORG) technique and faults are classified using multi kernel support vector machine (MKSVM). In the tests, the classification performances from different input features are assessed. For enhancing the performance of the segmentation investigation optimisation procedure that is whale optimisation (WO) is used. Before classifying, the extracted electrical components are fused by using feature level fusion (FLF) procedure to fused vector in all images. These multi kernel classification performance indices, including sensitivity, specificity and accuracy are utilised to recognise the most appropriate input feature and the best arrangement of classifiers. The performance of SVM is contrasted with a neural network. The correlation comes about demonstrating that our technique can accomplish a superior performance with accuracy at 98.21%.

PSIII-4 Infrared thermography as a method for detecting pain in dairy calves in response to disbudding on-farm

Abstract Measured using infrared thermography (IRT), changes in eye temperature, can be used to non-invasively measure pain in response to husbandry procedures such as disbudding in calves. Previous studies have manually recorded IRT in controlled studies, however, it would be of interest to determine if IRT could be used to measure changes in eye temperature in response to pain in an automated on-farm system. Therefore, the objective of this study was to determine if IRT could be used to detect pain in dairy calves in response to disbudding on-farm. At 3 wk of age, 51 Friesian calves were allocated to 1 of 5 treatment groups: 1) sham handling (SHAM, n = 10), 2) cautery disbudding (DB, n = 11), 3) administration of local anesthetic (LA) and DB (LA+DB, n = 11), 4) administration of a non-steroidal anti-inflammatory drug (NSAID) and DB (NSAID+DB, n = 9) and 5) administration of LA, NSAID and DB (LA+NSAID+DB, n = 10). Eye temperature was measured using an IRT camera located next to an automatic calf milk feeder for 3 days before and after disbudding. During each calf’s visit to the feeder, IRT images were automatically recorded for the duration of the feeding bout. A mixed model analysis with splines was used to determine the effect of treatment on eye temperature over time. There was no overall effect of disbudding treatment on eye temperature (P = 0.22), but temperature appeared to increase in calves after disbudding or sham handling (P = 0.036). On average, calves visited the calf feeder 4 times/day, which may have been too infrequent to collect sufficient IRT images to detect a difference in eye temperature in response to disbudding. However, the increase in eye temperature after disbudding and handling may reflect a general stress response. Therefore, the automated use of IRT to measure eye temperature may be a useful non-invasive method to measure stress in calves.

Performance Evaluation of the Developed Diagnostic Multi-Leaf Collimator and Implementation of Fusion Image of X-ray Image and Infrared Thermography Image

Performance Evaluation of the Developed Diagnostic Multi-Leaf Collimator and Implementation of Fusion Image of X-ray Image and Infrared Thermography Image

High-throughput, outdoor characterization of photovoltaic modules by moving electroluminescence measurements

Fast and nondestructive quality control tools are important to assess the reliability of photovoltaic plants. On-site inspection is essential to minimize the risk of further damage and electrical yield losses. The most effective way of achieving this is with highly sensitive imaging techniques such as luminescence or infrared thermography imaging. Nowadays, electroluminescence (EL) is used at nighttime to detect defects such as local cell changes, series resistances, and shunts. However, the drawback of this method is low measurement throughput. To increase the throughput, indium gallium arsenide detectors with a resolution of 640 × 512 pixels are used, for which short integration times are possible to acquire EL images. For such short integration times, even moving image acquisition and movie recording are feasible to detect the mentioned defects. An outdoor EL setup is presented for mobile handheld recording, which can even be used under low-light conditions, below 100 W / m2, at daytime. Experiments show that 5 ms integration time is a good compromise between low contrasts for lower integration times and motion blur for higher integration times. The camera prototype has an onboard computer to avoid image transmission losses and an external lithium polymer battery power supply for improved mobility.

A framework for automated time-lapse thermography data processing

The current research develops a framework to automatically extract sub-surface defects from time-lapse thermography (TLT) images of reinforced concrete bridge components. Traditional approaches for processing TLT data typically require manual interventions that are not easily scaled to a large network of concrete bridges. A backbone of robust algorithms for detecting and analyzing deep sub-surface defects in concrete is needed to support condition assessment of concrete structures such as bridges. The current paper leverages advances in adaptive signal and image processing to develop a fully automated TLT data processing pipeline that is capable of efficiently detecting defects at different depths in concrete. The methodology decomposes raw TLT datasets into narrow band time-frequency domains via a multiscale data analysis approach called a wavelet transform. The resulting decomposed modes are mined to extract defect information using thermal contrast enhancement routines. An objective measure of effectiveness based on signal-to-noise ratio was developed and used to compare the current framework with traditional approaches for processing TLT data. Active contour models were also designed to automatically extract the boundary location and geometric properties of the sub-surface defects. The results of this study show that the detection of deeper defects (3 in. and beyond) can be improved by analyzing the time-frequency response of surface temperature variations over a period of time. Compared to traditional lock-in algorithms and conventional infrared thermography images, the proposed framework is more effective at removing noisy information and produces images with greater contrast between intact and defective areas of concrete.

INTEGRATIVE IRT FOR DOCUMENTATION AND INTERPRETATION OF ARCHAEOLOGICAL STRUCTURES

Abstract. The documentation of built heritage involves tangible and intangible features. Several morphological and metric aspects of architectural structures are acquired throughout a massive data capture system, such as the Terrestrial Laser Scanner (TLS) and the Structure from Motion (SfM) technique. They produce models that give information about the skin of architectural organism. Infrared Thermography (IRT) is one of the techniques used to investigate what is beyond the external layer. This technology is particularly significant in the diagnostics and conservation of the built heritage. In archaeology, the integration of data acquired through different sensors improves the analysis and the interpretation of findings that are incomplete or transformed. Starting from a topographic and photogrammetric survey, the procedure here proposed aims to combine the bidimensional IRT data together with the 3D point cloud. This system helps to overcome the Field of View (FoV) of each IRT image and provides a three-dimensional reading of the thermal behaviour of the object. This approach is based on the geometric constraints of the pair of RGB-IR images coming from two different sensors mounted inside a bi-camera commercial device. Knowing the approximate distance between the two sensors, and making the necessary simplifications allowed by the low resolution of the thermal sensor, we projected the colour of the IR images to the RGB point cloud. The procedure was applied is the so-called Nymphaeum of Egeria, an archaeological structure in the Caffarella Park (Rome, Italy), which is currently part of the Appia Antica Regional Park.