Infrared thermography (IRT) is a promising nondestructive evaluation technique that has been widely used for bridge deck inspection. It is a quick and fairly simple method for providing preliminary information about defected areas in bridge decks. However, detection capabilities and sensitivity to environmental conditions and material properties impose certain challenges for the application of IRT. In this paper, the effect of various concrete mixtures on passive IRT was investigated. Four concrete mixtures (i.e., conventional, high-strength, self-consolidated, and lightweight concrete) were considered in the evaluation. Various defects with relatively small sizes placed at different depths were simulated and planted in cast-in-place slabs. The casting and testing took place in the United Arab Emirates (UAE), which is considered a hot-weather region. The defected slabs were imaged, and the existence of defects was judged visually. A minimum of 0.8°C was used for successful detection to make a mor...

Microwave nondestructive testing (MNDT) has shown great potential in detecting defects in various materials. This is due to it being safe and noninvasive. Safety is essential for the operators as well as the specimens being tested. Being noninvasive is important in maintaining the health of critical structures and components across various industries. In this paper, a review of MNDT methods is given with a comparison against other NDT techniques. First, the latter techniques are described, namely testing using a dye penetrant, ultrasound, eddy currents, magnetic particles, or radiography. Next, an overview of various microwave NDT methods is provided through a review of the applications, advantages, and limitations of each technique. Further, a detailed review of emerging MNDT techniques like microwave microscopy, active microwave thermography, and chipless radio frequency identification is presented. Next, a brief description of current and emerging algorithms employed in MNDT is discussed, with emphasis on those using artificial intelligence. By providing a comprehensive review, this article aims to shed light on the current state of MNDT, thus serving as a reference for subsequent innovations in this rapidly evolving domain.

Deep convolutional neural networks (CNNs) are investigated to solve inverse scattering problems for microwave imaging (MWI). The conventional approaches for solving inverse problems encounter challenges such as noisy data and high computational costs. Thus, various deep-learning techniques have been proposed recently to tackle these issues. In this article, the attention-Unet (ATTN-Unet) architecture with attention gates (AGs) is implemented for MWI applications. Further, it is compared against the performance of other CNN-based architectures with similar configurations, namely, DCEDnet, Unet, and Unet-Lite. In addition, the Unet-Lite is implemented with AGs, mainly to evaluate the consistency of performance improvement due to AGs. All the networks have been implemented and tested with complex—real and imaginary—inputs and outputs. The inputs are the backpropagation (BP) of the measured scattered fields onto the imaging domain. The outputs are the reconstructed real and imaginary relative complex permittivity values of an object-of-interest (OI). The results from different networks are compared against each other and against the conventional contrast source inversion (CSI) algorithm. The proposed ATTN-Unet is then tested with experimental data from the University of Manitoba (UM) repository. The results show that the implemented deep-learning method produces image reconstructions of better quality with much lesser computational time.

A major cause of bone mass loss worldwide is osteoporosis. X-ray is considered to be the gold-standard technique to diagnose this disease. However, there is currently a need for an alternative modality due to the ionizing radiations used in X-rays. In this vein, we conducted a numerical study herein to investigate the feasibility of using microwave tomography (MWT) to detect bone density variations that are correlated to variations in the complex relative permittivity within the reconstructed images. This study was performed using an in-house finite-element method contrast source inversion algorithm (FEM-CSI). Three anatomically-realistic human leg models based on magnetic resonance imaging reconstructions were created. Each model represents a leg with a distinct fat layer thickness; thus, the three models are for legs with thin, medium, and thick fat layers. In addition to using conventional matching media in the numerical study, the use of commercially available and cheap ultrasound gel was evaluated prior to bone image analysis. The inversion algorithm successfully localized bones in the thin and medium fat scenarios. In addition, bone volume variations were found to be inversely proportional to their relative permittivity in the reconstructed images with the root mean square error as low as 2.54. The observations found in this study suggest MWT as a promising bone imaging modality owing to its safe and non-ionizing radiations used in imaging objects with high quality.

In this paper, a novel, cost-efficient 2-by-32 switch matrix for microwave imaging systems is designed and implemented. The switch’s operation was verified, with its performance assessed across different ports through testing and measurements. The microwave switch demonstrated versatility as it is able to handle up to 32 antennas, functioning as both transmitters and receivers. The switch is designed as part of a microwave imaging system for biomedical applications operating at 1 GHz. Nevertheless, the switch’s operability was beyond the required 1 GHz range, suggesting its potential use for a broad range of applications. Further, the microwave switch operation is automated, enabling computerized control with different modes of operation. The compact and portable design, automated control, and compatibility features make the microwave switch matrix a valuable component for microwave imaging applications.