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The use of electromagnetic waves at microwave and millimeter-wave (mm-wave) frequencies in imaging has been growing rapidly in the last two decades with applications in security screening, biomedical imaging, nondestructive testing, and the inspection of goods and packages. The nonionizing nature of the radiation renders microwave and mm-wave imaging (MMI) safe for humans and, thus, attractive, especially for frequent imaging of living tissue and humans. At the same time, the radiation penetrates many materials, which are optically opaque: e.g., fog and foliage, soil and living tissue, brick and drywall, wood, fabrics, and plastics. Importantly, modern MMI systems offer compact and relatively low-cost hardware due to advancements in high-frequency microelectronics. 

The demand for biomedical devices that can collect high-fidelity data from patients in various environments, inside and outside medical clinics, and can provide high-precision diagnosis and long-term monitoring, is high in the healthcare industry. Among different sensing technologies, this study specifically focuses on providing a review of inductive sensing-based devices used in healthcare. Although the concept of inductive sensing has been used in other fields aside from healthcare, we believe that there is a high potential for a wide range of use of this sensing method across various biomedical devices due to the low cost, flexibility in the sensor design, mature fabrication technologies, and high sensitivity. This review first summarizes the mechanisms of inductive sensing-based devices and design considerations. Then, three main groups of applications that are used in healthcare and rely mainly on inductive sensing for their diagnosis and treatments are presented, including motion tracking and continuous monitoring systems, bio-signal detection systems, and imaging biological tissues. We conclude with a summary of the current status of inductive sensing devices used in healthcare, highlighting the promising capabilities and barriers to overcome. 

Microwave imaging has been a popular high resolution, non-invasive, and non-contact nondestructive testing (NDT) method for detecting defects and objects in non-metallic media with applications toward testing dielectric slabs, printed circuit board testing, biomedical diagnosis, etc. In this paper, we employ an array of microwave sensors designed based on the complementary split ring resonators (CSRR) along with nearfield holographic microwave imaging (NH-MWI) to assess the hidden features in the dielectric media. In this array, each element resonates at a different frequency in the range of 1 GHz to 10 GHz. Performance of the proposed method is demonstrated via simulation and experimental results. 

In current near-field holographic imaging, a circular region is scanned using an array of transmitting and receiving antennas over a narrow frequency band. This makes the data acquisition system slow, complex, bulky, and costly. Reducing the number of receiver antennas and using a narrower frequency band can significantly reduce the cost and complexity of the data acquisition system. To do so, we propose a method that uses prior knowledge about the object position, obtained by applying a neural network algorithm, called convolutional neural network (CNN), to the scattered field responses. This prior knowledge is then used to add a new regularization term to the cost function that is minimized in near-field holography. 

Microwave imaging is a high-resolution, noninvasive, and noncontact method for detecting hidden defects, cracks, and objects with applications for testing nonmetallic components such as printed circuit boards, biomedical diagnosis, aerospace components inspection, etc. In this paper, an array of microwave sensors designed based on complementary split ring resonators (CSRR) are used to evaluate the hidden features in dielectric media with applications in nondestructive testing and biomedical diagnosis. In this array, each element resonates at a different frequency in the range of 1 GHz to 10 GHz. Even though the operating frequencies are not that high, the acquisition of evanescent waves in extreme proximity to the imaged object and processing them using near-field holographic imaging allows for obtaining high-resolution images. The performance of the proposed method is demonstrated through simulation and experimental results. 

There is a rapid trend in various industries to replace the metallic pipes by nonmetallic ones. This is due to the certain properties, such as high strength, lightweight, resilience to corrosion, and low cost of maintenance for nonmetallic pipes. Despite the abovementioned advantages, nonmetallic pipes are still affected by issues, such as erosion, defects, damages, cracks, holes, delamination, and changes in the thickness. These issues are typically caused due to the manufacturing process, type of carried fluid composition, and flow rate. If not examined well, these issues could lead to disastrous failures caused by leakages and bursting of the pipes. To prevent such major failures, it is extremely important to test the pipes periodically for an accurate estimation of their thickness profile. In this article, we propose a nondestructive testing (NDT) technique, based on near-field microwave holography, for identifying the fluid carried by a nonmetallic pipe and estimating the pipe's thickness profile. Identifying the carried fluid helps improve the thickness profile estimation. The performance of the proposed techniques will be demonstrated via simulations and experiments. 

In oil, gas, transportation, and construction industries non-metallic pipes are rapidly replacing metallic pipes. Thus, it is crucial to have a reliable and robust nondestructive testing (NDT) technique to monitor the variation in the thickness profile of such pipes. Here, we propose a technique based on the near-field microwave holography and standardized minimum norm (SMN) to reconstruct the thickness profile of these pipes. We will also identify the fluid carried within the pipe which improves the thickness profile estimation process. The proposed methods are validated via simulation and experimental results. 

In this paper, a unique approach to the imaging of non-metallic media using capacitive sensing is presented. By using customized sensor plates in single-ended and differential configurations, responses to hidden objects can be captured over a cylindrical aperture surrounding the inspected medium. Then, by processing the acquired data using a novel imaging technique based on the convolution theory, Fourier and inverse Fourier transforms, and exact low resolution electromagnetic tomography (eLORETA), images are reconstructed over multiple radial depths using the acquired sensor data. Imaging hidden objects over multiple depths has wide range of applications, from biomedical imaging to nondestructive testing of the materials. Performance of the proposed imaging technique is demonstrated via experimental results.