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In this paper, artificial intelligence (AI) technology is applied to the electromagnetic imaging of anisotropic objects. Advances in magnetic anomaly sensing systems and electromagnetic imaging use electromagnetic principles to detect and characterize subsurface or hidden objects. We use measured multifrequency scattered fields to calculate the initial dielectric constant distribution of anisotropic objects through the backpropagation scheme (BPS). Later, the estimated multifrequency permittivity distribution is input to a convolutional neural network (CNN) for the adaptive moment estimation (ADAM) method to reconstruct a more accurate image. In the meantime, we also improve the definition of loss function in the CNN. Numerical results show that the improved loss function unifying the structural similarity index measure (SSIM) and root mean square error (RMSE) can effectively enhance image quality. In our simulation environment, noise interference is considered for both TE (transverse electric) and TM (transverse magnetic) waves to reconstruct anisotropic scatterers. Lastly, we conclude that multifrequency reconstructions are more stable and precise than single-frequency reconstructions. [ABSTRACT FROM AUTHOR]

The integration of electromagnetic imaging technology with the Internet of Things plays an important role in fields as diverse as healthcare, geophysics, and industrial diagnostics. This paper presents a novel two-step neural network architecture to solve the electromagnetic imaging for uniaxial objects which can be used in the Internet of Things. We incident TM and TE waves to unknown objects and receive the scattered fields. In order to reduce the training difficulty, we first input the gathered scattered field information into a deep convolutional neural network (DCNN) to obtain the preliminary guess. In the second step, we feed the guessed image into the convolutional neural network (CNN) to reconstruct high-resolution images. Our numerical results demonstrate the real-time imaging capability of our proposed two-step method in reconstructing high-contrast scatterers. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel artificial intelligence technique with an attention mechanism for half-space electromagnetic imaging. A dielectric object in half-space is illuminated by TM (transverse magnetic) waves. Since measurements can only be made in the upper space, the measurement angle will be limited. As a result, we apply a back-propagation scheme (BPS) to generate an initial guessed image from the measured scattered fields for scatterer buried in the lower half-space. This process can effectively reduce the high nonlinearity of the inverse scattering problem. We further input the guessed images into the generative adversarial network (GAN) and the self-attention generative adversarial network (SAGAN), respectively, to compare the reconstruction performance. Numerical results prove that both SAGAN and GAN can reconstruct dielectric objects and the MNIST dataset under same measurement conditions. Our analysis also reveals that SAGAN is able to reconstruct electromagnetic images more accurately and efficiently than GAN. [ABSTRACT FROM AUTHOR]

Transiliac–transsacral screw fixation is widely used to stabilize unstable posterior pelvic ring injuries. Preoperative radiographic assessment of the safe osseous corridor is necessary because the safe space of sacrum is narrower for transiliac–transsacral screw placement than for traditional iliosacral screw placement. However, the radiographic assessment has rarely been studied in the Taiwanese population. We retrospectively analyzed 100 patients with pelvic computed tomography images and divided them into normal and dysmorphic pelvis groups. To determine the safe osseous space, we recorded cross-section area, cross-sectional diameter of the safe zone (CS-szD), and safe zone width on axial view (Ax-szW) in the S1 to S3 segments. The prevalence of dysmorphic pelvis was 48% among all patients. In the S1 segment, no differences were found in the cross-section area and CS-szD been the two groups. However, the Ax-szW was significantly smaller in the dysmorphic pelvis group. In the S2 segment, the cross-section area, CS-szD, and Ax-szW were all significantly larger in the dysmorphic pelvis group. In the S3 segment, the cross-section area and CS-szD of the normal pelvis group were both significantly smaller. No differences were found in the Ax-szW between the two groups. Based on our findings in a Taiwanese population, S1 was the most suitable segment for transiliac–transsacral screw fixation in a normal pelvis, whereas S2, followed by S3, was most suitable in a dysmorphic pelvis. This study offers surgeons information on identifying the optimal sacral segment for transiliac–transsacral screw placement for each pelvic morphology. [Orthopedics. 2024;47(1):e13–e18.] [ABSTRACT FROM AUTHOR]

Featured Application: Electromagnetic imaging. Featured applications include military measurements, medical imaging, industrial applications, underground gas pipe and electrical high-voltage cable detection, etc. In the past, many conventional algorithms, such as self-adaptive dynamic differential evolution and asynchronous particle swarm optimization, were used to reconstruct buried objects in the frequency domain; these were unfortunately time-consuming during the iterative, repeated computing process of the scattered field. Consequently, we propose an innovative deep convolutional neural network approach to solve the electromagnetic inverse scattering problem for buried conductors in this paper. Different shapes of conductors are buried in one half-space and the electromagnetic wave from the other half-space is incident. The shape of the conductor can be reconstructed promptly by inputting the received scattered fields measured from the upper half-space into the deep convolutional neural network module, which avoids the computational complexity of Green's function for training. Numerical results show that the root mean square error for differently shaped—circular, elliptical, arrow, peanut, four-petal, and three-petal—reconstructed images are, respectively, 2.95%, 3.11%, 17.81%, 15.10%, 14.14%, and 15.24%. Briefly speaking, not only can circular and elliptical buried conductors be reconstructed; some irregular shapes can be reconstructed well. On the contrary, the reconstruction result by U-Net for buried objects is worse since it is not able to obtain a good preliminary image by processing only the upper scattered field—that is, rather than the full space. In other words, our proposed deep convolutional neural network can efficiently solve the electromagnetic inverse scattering problem of buried conductors and provide a novel method for the microwave imaging of the buried conductors. This is the first successful attempt at using deep convolutional neural networks for buried conductors in the frequency domain, which may be useful for practical applications in various fields such as the medical, military, or industrial fields, including magnetic resonance imaging, mine detection and clearance, non-destructive testing, gas or wire pipeline detection, etc. [ABSTRACT FROM AUTHOR]