Search

This study introduces and evaluates a new approach to reconstruct image sequences acquired during non-destructive testing by pulsed thermography. The proposed method consists of applying two linear support vector regressions to model the evolution of the data from both a spatial and temporal point of view. Each regression vectors will map the data with the number of pixels and the number of frames using convex optimisation. Then the regression vectors are used to predict a more robust representation of the data, thus reconstructing the sequence. The proposed method has been applied to data related to a reference sample of carbon reinforced fibre with known defects. This approach was evaluated on a sequence with severe non-uniform heating and was compared with state-of-the-art methods. Despite being sensitive to nonuniform heating, the proposed method provided a higher CNR score on smaller defects, compared with state-of-the-art methods. For the shallowest defects it shows better performance in term of contrast reconstruction compared to partial least-squares thermography (PLST). It also outperforms principal component thermography (PCT), and thermographic signal reconstruction-PCT (TSR-PCT) for defects located at a depth of 0.6 mm and 0.8 mm below the surface.

New composite materials are always subjected to non-destructive evaluation (NDE) prior to being placed on the market. This is to fully understand the reactions (i.e., development of defects) at the interface between two subsequent layers. Active infrared thermography (aIRT) can help in this regard, especially if anticipated by a simulation of the heat transfer from the exterior (lamp) to the interior (multilayer). Comsol Multiphysics® was used in this work as a tool by developing an innovative approach, which is designed – on the one hand – to minimize the computational cost and – on the other hand – to optimize the radiation to be delivered. The innovation produced by our work also concerns the pre-processing step of the thermal images; in fact, the 2D Fast Iterative Filtering (FIF2) is here introduced, discussing its benefits in comparison to previously developed techniques. Pre-processed data were further analyzed during the post-processing step demonstrating the reliability of FIF2 in enhancing thermal imprints, which leads to an improved detection of subsurface features. In particular, enhanced thermal imprints highlight the shape of the grid of glass fibres present beneath an external coating of hemp fibres (and, in general, added to the whole specimen along the x-y vectors). This grid of glass fibres was recently introduced as an insulation material for buildings. A brief review of the use of the preprocessing step in aIRT allows the reader to better understand the decisive step forward provided by FIF2 combined with a clever numerical simulation in the applied thermal engineering field. Qualitative and quantitative IRT results are shown and discussed thoroughly. Finally, a validation among numerical and experimental (thermographic) data is provided thanks to the Parker (laser flash) method.

Spot welds have a significant part in the creation of automotive vehicles. Since the integrity of, for example, a car, is dependent on the performance of multiple welds, it is important to ensure the quality of each spot weld. Several attempts have been made in order to determine the quality of spot welds, but most of them do not focus on the applicability in the manufacturing process. Spot weld inspections are often performed using back heating. However, during manufacturing, robotic inspections are desired, and since the bodywork of a car is a complex shape, the accessibility from the inside of the vehicle is minor. Therefore, inspections using front heating are more suitable. In this manuscript, multiple excitation methods are compared as well as different post-processing techniques. The used excitation techniques can be divided into light heating and inductive heating. Light heating is further divided in lock-in thermography and pulse thermography. The used post-processing techniques are principle component analysis and fast Fourier transform. Inductive heating turns out to be the most suitable measurement technique since it is fast and can be performed as front and back heating. Both investigated post-processing techniques deliver suitable information, such as relief images and information of the internal structure of the spot weld.

Automated medical data analysis demonstrated a significant role in modern medicine, and cancer diagnosis/prognosis to achieve highly reliable and generalizable systems. In this study, an automated breast cancer screening method in ultrasound imaging is proposed. A convolutional deep autoencoder model is presented for simultaneous segmentation and radiomic extraction. The model segments the breast lesions while concurrently extracting radiomic features. With our deep model, we perform breast lesion segmentation, which is linked to low-dimensional deep-radiomic extraction (four features). Similarly, we used high dimensional conventional imaging throughputs and applied spectral embedding techniques to reduce its size from 354 to 12 radiomics. A total of 780 ultrasound images—437 benign, 210, malignant, and 133 normal—were used to train and validate the models in this study. To diagnose malignant lesions, we have performed training, hyperparameter tuning, cross-validation, and testing with a random forest model. This resulted in a binary classification accuracy of 78.5% (65.1–84.1%) for the maximal (full multivariate) cross-validated model for a combination of radiomic groups.

This work aims to address the effectiveness and challenges of using active infrared thermography (IRT) onboard an unmanned aerial vehicle (UAV) platform. The work seeks to assess the performance of small low-powered forms of excitation which are suitable for active thermography and the ability to locate subsurface defects on composites. An excitation source in multiple 250 W lamps is mounted onto a UAV and is solely battery powered with a remote trigger to power cycle them. Multiple experiments address the interference from the UAV whilst performing an active IRT inspection. The optimal distances and time required for a UAV inspection using IRT are calculated. Multiple signal processing techniques are used to analyse the composites which help locate the sub-surface defects. It was observed that a UAV can successfully carry the required sensors and equipment for an Active thermographic NDT inspection which can provide access to difficult areas. Most active thermographic inspection equipment is large, heavy, and expensive. Furthermore, using such equipment for the inspection of complex structures is time-consuming. For example, a cherry picker would be required to inspect the tail of an aircraft. This solution looks to assist engineers in inspecting complex composite structures and could potentially significantly reduce the time and cost of a routine inspection.

New composite materials are always subjected to non-destructive evaluation (NDE) prior to being placed on the market. This is to fully understand the reactions (i.e., development of defects) at the interface between two subsequent layers. Active infrared thermography (aIRT) can help in this regard, especially if anticipated by a simulation of the heat transfer from the exterior (lamp) to the interior (multilayer). Comsol Multiphysics® was used in this work as a tool by developing an innovative approach, which is designed – on the one hand – to minimize the computational cost and – on the other hand – to optimize the radiation to be delivered. The innovation produced by our work also concerns the pre-processing step of the thermal images; in fact, the 2D Fast Iterative Filtering (FIF2) is here introduced, discussing its benefits in comparison to previously developed techniques. Pre-processed data were further analyzed during the post-processing step demonstrating the reliability of FIF2 in enhancing thermal imprints, which leads to an improved detection of subsurface features. In particular, enhanced thermal imprints highlight the shape of the grid of glass fibres present beneath an external coating of hemp fibres (and, in general, added to the whole specimen along the x-y vectors). This grid of glass fibres was recently introduced as an insulation material for buildings. A brief review of the use of the preprocessing step in aIRT allows the reader to better understand the decisive step forward provided by FIF2 combined with a clever numerical simulation in the applied thermal engineering field. Qualitative and quantitative IRT results are shown and discussed thoroughly. Finally, a validation among numerical and experimental (thermographic) data is provided thanks to the Parker (laser flash) method.

Detection of subsurface defects has hitherto been regarded as an important element in the course of preserving cultural heritage. To do so, non-destructive imaging approaches for viewing and determining the location of splitting inside the sample under test are required, which constitute the subject of the present study. Both active thermography and terahertz imaging have demonstrated their potential in providing non-destructive inspection on cultural heritage objects. Conventionally, active thermography has been used to retrieve details on the defects as well as morphological data from the surface and subsurface, whereas pulsed terahertz imaging has been applied to record the internal material distribution. Here, the feature extraction, selection and fusion framework is extended to design a fusion process to merge the information obtained by both active thermography and terahertz imaging; in this way, the technique naturally inherits the strengths of both aforementioned imaging technologies. The fusion technique is able to produce images with high-contrast defect information located at different depths. To demonstrate the efficacy of the suggested technique, an experiment has been conducted on an ancient marquetry.

The reinforcement of polymer composites using plant fibres has received vast and growing attention for investigation owing to their capability to serve as semi-structural materials, especially as a substitute for fiberglass. However, in this type of research, the structural complexity, due to geometrical mismatches and inherent variability to the properties of vegetable fibres and plant fibre composites would benefit from more effective noninvasive inspection techniques. In this work, a novel infrared-terahertz fused approach was proposed, and a new unsupervised deep residual fusion algorithm was developed in order to combine the outputs from two different physical mechanisms. Specifically, infrared imaging provided the details of the defects and the morphological information from the surface and subsurface, while terahertz imaging recorded the distribution of the fibres and the resin. Finally, the fused infrared-terahertz slices were reconstructed into a form of 3D exhibition for inspection and evaluation of two plant fibre-reinforced polymer composites in first time using multiphysical imaging and fusion.

Camera trapping is widely used in different ecological studies and is particularly important for remote locations and extreme environments. However, the application of camera traps in arctic regions remains very limited. One of the challenges is the formation of hoar on the lens of cameras. In this article we propose a solution to address this problem by changing the camera parameters and its position in order to optimize the camera trap for long-term subnivean deployment in the Canadian Arctic. Preliminary field tests show that this approach allows tracking lemmings in the frozen environment without natural light nor external electrical power supply, where the direct observations are impossible for the most part of the year. We obtained the first videos of lemmings under the snow during the arctic winter. Extending the observational network of the newly designed camera traps will help to better understand lemming population dynamics. The demonstrated approach is also promising for other polar applications

Subnivean life is an important part of the Arctic ecosystem but it has been little explored. Long, harsh winters in addition to remoteness have made direct studies in these hardly accessible areas very expensive and extremely difficult. To tackle this problem, a low-power autonomous camera system (called ArcÇav) is developed for monitoring small mammals beneath the snow in the Canadian Arctic. ArcÇav is composed of several components, including a digital camera, a single board computer, a microcontroller board, and a motion detection sensor. A limited energy source, very cold temperatures, darkness, and a very long recording period (several months) are major challenges that ArcÇav is designed to deal with. The performance of the developed system is evaluated in a real situation in the High Arctic. The field results show that ArcÇav can function well for an extended period of time on a battery at very low temperatures during the arctic winters. To the best of our knowledge, this is the first time that life under snow has been filmed by a camera trap in the Arctic during winter. ArcÇav equips ecologists with a new means to explore and study subnivean life remotely. These observations can provide a foundation to answer some of questions that have puzzled animal ecologists for decades.

Coplanar capacitive sensors are employed in Non-destructive Testing (NDT) methods to measure the difference in dielectric properties of the materials. The most important design parameters for a coplanar capacitive sensor include the shape, size, and separation distance of the electrodes which affect the sensor performance. In addition, the impact of the shielding plate and guard electrode should be considered. In the framework of this paper, numerical simulations and physical experiments are studied for two shapes of electrodes, triangular and rectangular, by examining different sizes and different separation distances between electrodes to assess and analyze the important features of the coplanar capacitive electrodes, such as the penetration and strength of the electric field as a function of sensor geometrical properties. Therefore, a detailed analysis of numerical simulation using Finite Element Modelling (FEM) is provided to study these geometric parameters. In addition, the influence of the different frequencies, lift-off, and the presence or absence of a metal shielding plate and guard electrode on the output result is analyzed. Finally, sensors were manufactured and several experiments were carried out under different configurations. Comparison of the numerical simulation results and physical experiments illustrate that they are in good qualitative agreement.

Subnivean life is an important part of the Arctic ecosystem but it has been little explored. Long, harsh winters in addition to remoteness have made direct studies in these hardly accessible areas very expensive and extremely difficult. To tackle this problem, a low-power autonomous camera system (called ArcÇav) is developed for monitoring small mammals beneath the snow in the Canadian Arctic. ArcÇav is composed of several components, including a digital camera, a single board computer, a microcontroller board, and a motion detection sensor. A limited energy source, very cold temperatures, darkness, and a very long recording period (several months) are major challenges that ArcÇav is designed to deal with. The performance of the developed system is evaluated in a real situation in the High Arctic. The field results show that ArcÇav can function well for an extended period of time on a battery at very low temperatures during the arctic winters. To the best of our knowledge, this is the first time that life under snow has been filmed by a camera trap in the Arctic during winter. ArcÇav equips ecologists with a new means to explore and study subnivean life remotely. These observations can provide a foundation to answer some of questions that have puzzled animal ecologists for decades.

Camera trapping is widely used in different ecological studies and is particularly important for remote locations and extreme environments. However, the application of camera traps in Arctic regions remains very limited. One of the challenges is the formation of hoar on the lens of cameras. In this article, we propose a solution to address this problem by changing the camera parameters and its position in order to optimize the camera trap for long-term subnivean deployment in the Canadian Arctic. Preliminary field tests show that this approach allows tracking lemmings in the frozen environment without natural light or external electrical power supply, where the direct observations are impossible for the most part of the year. We obtained the first videos of lemmings under the snow during the Arctic winter. Extending the observational network of the newly designed camera traps will help to better understand lemming population dynamics. The demonstrated approach is also promising for other polar applications., Le piégeage photographique est largement utilisé dans différentes études écologiques et il est particulièrement important pour les endroits éloignés et les environnements extrêmes. Toutefois, l’utilisation de pièges photographiques dans les régions arctiques reste très limitée. L’un des défis à relever consiste en la formation de givre sur l’objectif des caméras. Dans cet article, les auteurs proposent une solution pour résoudre ce problème en modifiant les paramètres de la caméra et sa position afin d’optimiser le piège photographique pour un déploiement subnival à long terme dans l’Arctique canadien. Des tests préliminaires sur le terrain montrent que cette approche permet de suivre des lemmings dans un environnement gelé, sans lumière naturelle ni alimentation électrique externe, où les observations directes sont impossibles pendant la majeure partie de l’année. Ils ont obtenu les premières vidéos de lemmings sous la neige pendant l’hiver arctique. L’extension du réseau d’observation des nouveaux pièges photographiques permettra de mieux comprendre la dynamique des populations de lemmings. L’approche démontrée est également prometteuse pour d’autres applications polaires.

Coplanar capacitive sensors are employed in Non-destructive Testing (NDT) methods to measure the difference in dielectric properties of the materials. The most important design parameters for a coplanar capacitive sensor include the shape, size, and separation distance of the electrodes which affect the sensor performance. In addition, the impact of the shielding plate and guard electrode should be considered. In the framework of this paper, numerical simulations and physical experiments are studied for two shapes of electrodes, triangular and rectangular, by examining different sizes and different separation distances between electrodes to assess and analyze the important features of the coplanar capacitive electrodes, such as the penetration and strength of the electric field as a function of sensor geometrical properties. Therefore, a detailed analysis of numerical simulation using Finite Element Modelling (FEM) is provided to study these geometric parameters. In addition, the influence of the different frequencies, lift-off, and the presence or absence of a metal shielding plate and guard electrode on the output result is analyzed. Finally, sensors were manufactured and several experiments were carried out under different configurations. Comparison of the numerical simulation results and physical experiments illustrate that they are in good qualitative agreement.