Microwave imaging of composite materials using image processing

This paper presents the results of application of a relatively simple microwave continuous wave reflectometer with an open-ended waveguide antenna for the purpose of nondestructive testing and evaluation of composite materials using their images. It is shown that the resulting original images could not reveal a desired amount of information about the interior of the sample under investigation and the proposed image processing techniques can improve the results in particular as it relates to detecting the targets located at different depths. This paper presents the results of this investigation and a discussion of these results

Structural damage identification using millimeter wave imaging and image processing

In the past decades, structural health monitoring (SHM) has received wide attention in preventing the sudden failure of structural components by identifying the damages in its early stages. Basically, to ensure the safety and reducing the serviceabili Otsu's thresholding ty of civil infrastructure it is important to inspect and assess the physical and functional condition of structures. Currently, manual inspection is the main form of assessing the conditions to ensure structure still meets the safety requirements. However, there are still several accidents that are reported as a result of insufficient inspection and conditional assessment of structures. In order to prevent further incidents, it is necessary to continuously inspect and assess the condition of structures with appropriate techniques. This is why the development and application of efficient non-destructive testing and computer vision methods for infrastructure health monitoring are in demand. This paper present the developed smart damage detection system for a local infrastructure health monitoring which complement the image-based damage detection methods in hazardous scenarios. The system is based on a relatively simple millimeter wave continuous wave reflectometer with an open-ended waveguide antenna for the purpose of automatic imaging of flaws such as cracks in a steel plate at different standoff distances, monitoring the crack to avoid further deformation through a sequence of inspections at intervals. However, in some cases the original images based on measured data do not provide desired information, leading to developing the image processing algorithm to enhance the imaging result. The proposed algorithm is based on Otsu's thresholding method and Prewitt approximation in six directions in an image which has been created from the measured data to facilitate the structural damage identification. The proposed algorithm can successfully enhance the quality of images and visualize the crack in a steel plate under dielectric coating at different standoff distances

Statistical features and traditional SA-SVM classification algorithm for crack detection

In recent years, the interest in damage identification of structural components through innovative techniques has grown significantly. Damage identification has always been a crucial concern in quality assessment and load capacity rating of infrastructure. In this regard, researchers focus on proposing efficient tools to identify the damages in early stages to prevent the sudden failure in structural components, ensuring the public safety and reducing the asset management costs. The sensing technologies along with the data analysis through various techniques and machine learning approaches have been the area of interest for these innovative techniques. The purpose of this research is to develop a robust method for automatic condition assessment of real-life concrete structures for the detection of relatively small cracks at early stages. A damage identification algorithm is proposed using the hybrid approaches to analyze the sensors data. The data obtained from transducers mounted on concrete beams under static loading in laboratory. These data are used as the input parameters. The method relies only on the measured time responses. After filtering and normalization of the data, the damage sensitive statistical features are extracted from the signals and used as the inputs of Self-Advising Support Vector Machine (SA-SVM) for the classification purpose in civil Engineering area. Finally, the results are compared with traditional methods to investigate the feasibility of the hybrid proposed algorithm. It is demonstrated that the presented method can reliably detect the crack in the structure and thereby enable the real-time infrastructure health monitoring

Millimeter wave imaging of notches in metal specimens under dielectric coating using image processing

Structural health monitoring is one of the main concerns in infrastructure engineering since it focuses on evaluating the life span of structures and detection of defects and damages, which may lead to sudden failure of structural components. This is why, in recent years, defect and damage detection and evaluation have received a wide attention among researchers. More specifically, a local structural health monitoring based on non-destructive testing techniques is an efficient approach to identify the defects and damages of structures in early stages to evaluate the remaining life span, improving safety and health of structures and increasing their serviceability. Therefore, the development and application of non-destructive testing and evaluation techniques including microwave and millimeter wave imaging of structures are very important. This paper presents the application of a relatively simple millimeter wave continuous wave reflectometer with an open-ended waveguide antenna and the developed image processing algorithms for the purpose of detection and evaluation of flaws such as cracks and notches on metal surface covered by dielectric coating such as rubber coating. It is shown that the original images based on measured data may not provide desired information, leading to developing the image processing algorithms to enhance the imaging results. Three different algorithm-based solutions are proposed and developed in this paper for automatic damage detection in infrastructures which complement the edge-based damage detection methods in hazardous scenarios. The proposed algorithms are based on Sobel, Canny, and fuzzy c-mean thresholding algorithms developed using Otsu's thresholding method to facilitate the structural damage identification. The notches in this paper represent damages in the metal plate such as cracks. The proposed algorithms have been compared together and with the traditional ones to investigate their performance and advantages. The proposed algorithms can successfully enhance the quality of images and visualize the damage on the metal surface under dielectric coating

Structural damage detection of a concrete based on the autoregressive all-pole model parameters and artificial intelligence techniques

Over the past few decades, damage identification in structural components has been the crucial concern in quality assessment and load capacity rating of infrastructure, as well as in the planning of a maintenance schedule. In this regard, structural health monitoring based on efficient tools to identify the damages in early stages has been focused by researchers to prevent sudden failure in structural components, ensure the public safety and reducing the asset management costs. Therefore, the development and application of sensing technologies and data analysis using machine learning approaches to enable the automatic detection of cracks have become very important. The purpose of this research is to develop a robust method for automatic condition assessment of real-life concrete structures for the detection of relatively small cracks at early stages. A damage identification approach is proposed using the parametric modeling and machine learning approaches to analyze the sensors data. The data obtained from transducers mounted on concrete beams under static loading in laboratory. These data are used as the input parameters. The method relies only on the measured time responses. After filtering and normalization of the data, Autoregressive all-pole model parameters (Yule-Walker method) are considered as features and used as the inputs of a newly developed Self-Advising Support Vector Machine (SA-SVM) for the classification purpose in civil Engineering area. Finally, the results are compared with traditional methods to investigate the feasibility of our proposed method. It is demonstrated that the presented method can reliably detect the crack in the structure and thereby enable the real-time infrastructure health monitoring. © 2017 Association for Computing Machinery

Benzophenone-3 exposure alters composition of tumor infiltrating immune cells and increases lung seeding of 4T1 breast cancer cells

Environmental chemicals are a persistent and pervasive part of everyday life. A subset of environmental chemicals are xenoestrogens, compounds that bind to the estrogen receptor (ER) and drive estrogen-related processes. One such chemical, benzophenone-3 (BP3), is a common chemical in sunscreen. It is a potent UV protectant but also is quickly absorbed through the skin. While it has been approved by the FDA, there is a renewed interest in the safety of BP3, particularly in relation to breast cancer. The focus of this study was to examine the impact that BP3 has on triple negative breast cancer (TNBC) through alterations to cells in the immune microenvironment. In this study, we exposed female mice to one of two doses of BP3 before injecting them with a TNBC cell line. Several immune endpoints were examined both in the primary tissues and from in vitro studies of T cell behavior. Our studies revealed that in the lung tumor microenvironment, exposure to BP3 not only increased the number of metastases, but also the total area of tumor coverage. We also found that BP3 caused alterations in immune populations in a tissue-dependent manner, particularly in T cells. Taken together, our data suggest that while BP3 may not directly affect the proliferation of TNBC, growth and metastasis of TNBC-derived tumors can be altered by BP3 exposures via the alterations in the immune populations of the tumor microenvironment