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Öğe CNN-Based deep learning architecture for electromagnetic imaging of rough surface profiles(IEEE, 2022-10) Aydın, İzde; Budak, Güven; Sefer, Ahmet; Yapar, AliA convolutional neural network (CNN) based deep learning (DL) technique for electromagnetic imaging of rough surfaces separating two dielectric media is presented. The direct scattering problem is formulated through the conventional integral equations and the synthetic scattered field data is produced by a fast numerical solution technique which is based on Method of Moments (MoM). Two different special CNN architectures are designed and implemented for the solution of the inverse rough surface imaging problem wherein both random and deterministic rough surface profiles can be imaged. It is shown by a comprehensive numerical analysis that the proposed deep-learning (DL) inversion scheme is very effective and robust.Öğe Electromagnetic imaging of rough dielectric surface profiles using a single-frequency reverse time migration method(IEEE, 2023-07) Sefer, Ahmet; Yapar, Ali; Bağcı, HakanAn electromagnetic imaging scheme, which makes use of a single-frequency reverse time migration (RTM) technique to reconstruct two-dimensional (2D) rough surface profiles from the scattered field data, is formulated and implemented. The unknown surface profile, which is expressed as a one-dimensional height function, is the interface between two dielectric media. It is assumed that the profile is illuminated from one side and the scattered fields are “measured” along a line on this same side. RTM is used to construct a cross-correlation imaging functional that is numerically evaluated to yield an image of the investigation domain. The maxima of this functional yields an accurate reconstruction of the rough dielectric surface profile.Öğe Image recovery of inaccessible rough surfaces profiles having impedance boundary condition(IEEE, 2022) Sefer, Ahmet; Yapar, AliThis letter addresses a reconstruction algorithm of locally rough inaccessible surface profiles via the knowledge of the scattered field data under the consideration of the impedance boundary condition (IBC). To this aim, first, the synthetic scattered field data are obtained through the solution of the conventional surface integral equation (SIE) written on the rough surface. Then, the same SIE together with the data equation is solved iteratively via Newton's method to obtain the image of the rough surface profile. In the numerical implementation, the nonlinear ill-posed inverse problem is linearized in an iterative fashion via the Newton method and regularized by Tikhonov in the least-squares sense. The feasibility of the algorithm is provided via numerical examples, which shows that the method is effective and promising.Öğe Imaging of rough surfaces by RTM method(IEEE, 2024) Sefer, Ahmet; Yapar, Ali; Yelkenci, TanjuAn electromagnetic imaging framework is implemented utilizing a single frequency reverse time migration (RTM) technique to accurately reconstruct inaccessible two-dimensional (2D) rough surface profiles from the knowledge of scattered field data. The unknown surface profile, which is expressed as a 1D height function, is either perfectly electric conducting (PEC) or an interface between two penetrable media. For both cases, it is assumed that the surface is illuminated by a number of line sources located in the upper medium. The scattered fields, which should be collected by real measurements in practical applications, are obtained synthetically by solving the associated direct scattering problem through the surface integral equations. RTM is subsequently applied to generate a cross-correlation imaging functional which is evaluated numerically and provides a 2D image of the region of interest. A high correlation is observed by the functional in the regions where the transitions between two media occur. Hence, it results in the acquisition of the unknown surface profile at the sites where the functional attains its highest values. The efficiency of the proposed method is comprehensively tested by numerical examples covering various types of scattering scenarios.Öğe Inverse scattering by perfectly electric conducting (PEC) rough surfaces: an equivalent model with line sources(Institute of Electrical and Electronics Engineers Inc., 2022) Sefer, Ahmet; Yapar, AliThis paper presents a new method for the reconstruction of the perfectly electric conducting (PEC) rough surface profiles by utilizing electromagnetic waves. The inaccessible rough surface is illuminated by a tapered plane electromagnetic wave and the scattered field data are measured on a certain number of points above the surface under test. The method for the inverse electromagnetic imaging problem is based on a special representation of the scattered field in terms of a finite number of fictitious discrete line sources located along a plane below the rough surface. The current densities of these fictitious sources are obtained through the regularized solution of an ill-posed problem. Then, it is shown that the image of the rough surface can be directly retrieved by seeking the points in the space where the tangential component of the total electric field vanishes. Alternatively, a much more rigorous iterative method based on a regularized Newton algorithm is also presented. A comprehensive numerical analysis is provided to demonstrate the feasibility of the presented approach. In this context, the quantitative successes of both approaches are interpreted by considering a very sensitive ?2-norm based error function between the actual and the reconstructed surface profiles. Regarding different scattering scenarios taken into account, the error values obtained for satisfactory reconstructions are generally in the range of 10% - 30% for both methods. It is also shown that the presented algorithms are capable of reconstructing the rough surfaces which oscillate for every ? horizontally and have a peak to peak variation 0.5? at most.Öğe Optimization of inverse problems involving surface reconstruction: least squares application(Institute of Electrical and Electronics Engineers Inc., 2022) Sefer, AhmetThis article addresses the least-squares method, which is vital in inverse scattering problems involving the reconstruction of inaccessible rough surface profiles from the measured scattered field data. The unknown surface profile is retrieved by a regularized recursive Newton algorithm which is regularized by the Tikhonov method. The importance of the least-squares application reveals at this point, where the unknown surface profile is expressed as a linear combination of some appropriate basis functions. Thus, the problem of obtaining the unknown rough surface is reduced to finding the unknown coefficients of these functions. As an optimization problem, the choice of appropriate basis functions, as well as the number of their expansions for rough surface imaging problems are essential for the iterative solutions. The validation limits and the performances of different basis functions are presented via several numerical examples.Öğe Reconstruction algorithm for impenetrable rough surface profile under Neumann boundary condition(Taylor and Francis Ltd., 2022-05-24) Sefer, Ahmet; Yapar, AliIn this paper, an algorithm to reconstruct one-dimensional impenetrable rough surface from the knowledge of scattering field is presented. The rough surface is considered as locally perturbed and the scattering field data are collected above the roughness in a simple non-magnetic medium considering Neumann boundary condition. First, the surface integral equation constituted via the Neumann boundary condition is solved and scattering field data are observed synthetically. Then, the same surface integral equation together with the data equation are solved in an iterative fashion to reconstruct the surface variation. In the numerical implementation, the so-called ill-posed inverse problem is regularized with Tikhonov method and a least-squares solution is obtained by using Gaussian-type basis function. Finally, numerical examples are carried out to illustrate effectiveness of the method.Öğe Recovery of impenetrable rough surface profiles via CNN-based deep learning architecture(Taylor and Francis Ltd., 2022-08-18) Aydın, İzde; Budak, Güven; Sefer, Ahmet; Yapar, AliIn this paper, a convolutional neural network (CNN)-based deep learning (DL) architecture for the solution of an electromagnetic inverse problem related to imaging of the shape of the perfectly electric conducting (PEC) rough surfaces is addressed. The rough surface is illuminated by a plane wave and scattered field data is obtained synthetically through the numerical solution of surface integral equations. An effective CNN-DL architecture is implemented through the modelling of the rough surface variation in terms of convenient spline type base functions. The algorithm is numerically tested with various scenarios including amplitude only data and shown that it is very effective and useful.
