Capital market transactions provide an opportunity for investors to acquire ownership of company shares and capital gains, as well as dividends. However, alongside the benefits, there are risks of capital loss and liq...
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This research explores the vital role of penetration testing in cybersecurity, specifically its alignment with ISO 27001:2022, COBIT 2019, and NIST CSF standards in the context of crypto asset exchange management. The...
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An imaging scheme, which uses the frequency- domain reverse time migration (RTM) method to reconstruct two rough surfaces between three dielectric media from the scattered field measurements, is developed. The propose...
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ISBN:
(数字)9798350369908
ISBN:
(纸本)9798350369915
An imaging scheme, which uses the frequency- domain reverse time migration (RTM) method to reconstruct two rough surfaces between three dielectric media from the scattered field measurements, is developed. The proposed method requires two steps for the reconstructions. At the first step, the RTM function that is computed using the scattered field measurements reveals only the upper rough surface. At the second step, it is first assumed that there is only one surface, the upper one. The forward scattering problem is solved for this scenario. Then, to reconstruct the lower surface, the difference between the scattered fields obtained from this forward scattering problem and the scattered field measurements is used to compute the RTM function of the second step. This second RTM function reveals the lower surface. Numerical experiments demonstrate that the proposed two-step scheme is effective and promising.
A nonlinear electromagnetic inversion method that promotes the sparsity in the model gradient is proposed for reconstruction of dielectric profiles. For regularization, the method uses the ratio of $l_{1}$ -norm to ...
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ISBN:
(数字)9781733509671
ISBN:
(纸本)9798350362978
A nonlinear electromagnetic inversion method that promotes the sparsity in the model gradient is proposed for reconstruction of dielectric profiles. For regularization, the method uses the ratio of
$l_{1}$
-norm to
$l_{2}$
-norm (
$l_{1}/l_{2}$
-norm) which is a better approximation to
$l_{0}$
-norm than
$l_{1}$
-norm due to its scale-invariant property. To deal with the non-convexity of the resulting optimization problem, the alternating direction method of mul-tipliers is used to split the quotient structure of the
$l_{1}/l_{2}$
-norm. Consequently, the optimization problem is separated into several sub-steps that are executed iteratively in an alternating fashion. Numerical results show that the proposed method produces more accurate reconstructions of spatially sparse dielectric profiles (by preserving edges and reducing artifacts) compared to the methods relying on Tikhonov and total variation regularization.
A plug-and-play scheme that relies on a deep neural network for image denoising is used to regularize the nonlinear electromagnetic (EM) inversion. It is shown that any state-of-the-art denoiser can be plugged into th...
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ISBN:
(数字)9798350369908
ISBN:
(纸本)9798350369915
A plug-and-play scheme that relies on a deep neural network for image denoising is used to regularize the nonlinear electromagnetic (EM) inversion. It is shown that any state-of-the-art denoiser can be plugged into the conventional inversion framework as an implicit regularization step. Thus, a pretrained Swin-Conv-UNet (SCUNet) is employed in the EM inversion. SCUNet combines the advantages of residual convolutional layers and swin transformer blocks in accounting for different image priors and it is remarkably effective in image denoising. Nu-merical results obtained using this framework clearly shows its benefits over existing inversion algorithms.
Numerical methods that can accurately reconstruct rough surface profiles are used in various fields of engineering such as remote sensing, microwave imaging, optics, nondestructive testing, etc. These methods express ...
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ISBN:
(数字)9781733509671
ISBN:
(纸本)9798350362978
Numerical methods that can accurately reconstruct rough surface profiles are used in various fields of engineering such as remote sensing, microwave imaging, optics, nondestructive testing, etc. These methods express the electromagnetic scattered fields measured away from the surface itself as an integral function of the surface profile. This mapping is highly nonlinear and ill-posed (D. Colton and R. Kress, 1998, SpringerVerlag, Berlin), and therefore its inversion for reconstruction of the surface profile from measured scattered fields is challenging. This inversion can done using semi-analytic asymptotic approaches such as the small perturbation and the Rytov approximation methods (A.G. Voronovich, 2013, Springer-Verlag, Berlin), however the range of applicability of these approaches is rather limited. Fully numerical methods that rely on Newton-type iterative linearization techniques and regularization schemes such as those in (S. Arhab, et al., PIERS, pp. 3495–3500, 2017) and (A. Sefer, A. Yapar, IEEE Trans. Geosci Remote Sens., vol. 59, pp. 1041–1051, 2021) have a wider range of applicability but they suffer from convergence and accuracy issues.
Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms ...
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ISBN:
(数字)9798350360585
ISBN:
(纸本)9798350360592
Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms.
Thin layers with high conductivity values, such as metal sheets, conductive paint, graphene, and other two-dimensional (2D) materials, are commonly used in various electromagnetic applications. One of the fundamental ...
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ISBN:
(数字)9789463968119
ISBN:
(纸本)9798350359497
Thin layers with high conductivity values, such as metal sheets, conductive paint, graphene, and other two-dimensional (2D) materials, are commonly used in various electromagnetic applications. One of the fundamental challenges in numerical modeling of these thin conductive layers is the requirement for an extremely fine mesh that can accurately capture field variations and account for the intricate geometrical features of the structure (H. Chen, A. J. Taylor and N. Yu, Rep. Prog. Phy., 79,10-35,2016). A dense mesh translates into high computational cost since the number of unknowns is increased and the time step size must be reduced for an explicit time marching scheme (to ensure that the Courant-Friedrichs-Lewy (CFL) condition is satisfied). One can replace the thin conductive layer with an infinitesimally thin sheet on which the resistive boundary condition (RBC) is enforced (T. B. A. Senior and J. L. Volakis, London, UK: IET, 1995). This approach completely avoids the dense mesh and the high computational cost that comes with it. However, RBC has to be incorporated into the electromagnetic solver.
A metamaterial-based plasmonic absorber is designed, fabricated, and characterized. The absorber consists of a periodic array of Ti unit-cells at the top, a layer of Al at the bottom, and a thin SiO 2 layer that is s...
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ISBN:
(数字)9798350369908
ISBN:
(纸本)9798350369915
A metamaterial-based plasmonic absorber is designed, fabricated, and characterized. The absorber consists of a periodic array of Ti unit-cells at the top, a layer of Al at the bottom, and a thin SiO
2
layer that is sandwiched between the top and the bottom layers. Simulations and experiments show that the proposed design supports an average absorption of 96% over the wavelength range changing from 280 nm to 1000 nm (frequency range between ultraviolet [UV] and near-infrared [IR]). Furthermore, the absorption spectrum is polarization insensitive and has a large incidence angle tolerance. The proposed absorber has the potential to be used in photovoltaic applications such as solar cells and photo-detectors.
An iterative inversion algorithm to reconstruct the shape of two-dimensional dielectric objects from far-field measurements is formulated and implemented. The proposed method uses an integral operator to map the unkno...
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ISBN:
(数字)9798350369908
ISBN:
(纸本)9798350369915
An iterative inversion algorithm to reconstruct the shape of two-dimensional dielectric objects from far-field measurements is formulated and implemented. The proposed method uses an integral operator to map the unknown boundary of the object onto the far-field pattern of the scattered field. This mapping is inherently ill-posed and nonlinear. Therefore, Newton iterations are used for linearization, and the resulting linear equation at each iteration is regularized using a Tikhonov scheme. Numerical results validate the accuracy and the applicability of the proposed method.
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