About Ali Imran Sandhu Ali Imran Sandhu Ph.D. Student, Electrical and Computer Engineering computational electromagnetics inverse scattering Education Ph.D. Student, Electrical and Computer Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, 2010-present M.S., Communication Engineering, Chalmers University of Technology Sweden, Sweden, 2008-2010 B.S., Electronics Engineering, Comsats University Islamabad, Pakistan, 2003-2007 Professional Appointments Research Engineer, European Project FLEXWIN, University of Calabria, Italy, September 2011 Lecturer, Electrical Engineering Department, Comsats University Islamabad (CUI), Lahore, Pakistan, October 2007 Research Interests Computational Events Presented Events Mar 29 - Apr 4, 2020 Efficient and Accurate Numerical Techniques for Sparse Electromagnetic Imaging Ali Imran Sandhu, Ph.D. Student, Electrical and Computer Engineering Apr 1, 15:30 - 17:30 KAUST computational electromagnetics inverse scattering In this thesis, efficient solutions are sought out to fundamental problems in Electromagnetic (EM) imaging that determines the shape, location, and material properties of an (unknown) object of interest in an investigation domain from the scattered field measured away from it. The solution of an EM inverse scattering problem inherently poses two main challenges: (i) non-linearity, since the scattered field is a non-linear function of the material properties and (ii) ill-posedness, since the integral operator has a smoothing effect and the number of measurements is finite in dimension and they are contaminated with noise. The non-linearity is tackled incorporating a multitude of techniques (ranging from Born approximation (linear), inexact Newton (linearized) to complete non-linear iterative Landweber schemes) that can account for weak to strong scattering problems. The ill-posedness of the EM inverse scattering problem is circumvented by formulating the above methods into a minimization problem with a sparsity constraint, which assumes that the dimension of the unknown object relative to the investigation domain is much smaller. Numerical experiments, which are carried out using synthetically generated measurements, show that the images recovered by these sparsity-regularized methods are sharper and more accurate than those produced by existing methods. The methods developed in this work have potential application areas ranging from oil/gas reservoir engineering to biological imaging where sparse domains naturally exist.
Efficient and Accurate Numerical Techniques for Sparse Electromagnetic Imaging Ali Imran Sandhu, Ph.D. Student, Electrical and Computer Engineering Apr 1, 15:30 - 17:30 KAUST computational electromagnetics inverse scattering In this thesis, efficient solutions are sought out to fundamental problems in Electromagnetic (EM) imaging that determines the shape, location, and material properties of an (unknown) object of interest in an investigation domain from the scattered field measured away from it. The solution of an EM inverse scattering problem inherently poses two main challenges: (i) non-linearity, since the scattered field is a non-linear function of the material properties and (ii) ill-posedness, since the integral operator has a smoothing effect and the number of measurements is finite in dimension and they are contaminated with noise. The non-linearity is tackled incorporating a multitude of techniques (ranging from Born approximation (linear), inexact Newton (linearized) to complete non-linear iterative Landweber schemes) that can account for weak to strong scattering problems. The ill-posedness of the EM inverse scattering problem is circumvented by formulating the above methods into a minimization problem with a sparsity constraint, which assumes that the dimension of the unknown object relative to the investigation domain is much smaller. Numerical experiments, which are carried out using synthetically generated measurements, show that the images recovered by these sparsity-regularized methods are sharper and more accurate than those produced by existing methods. The methods developed in this work have potential application areas ranging from oil/gas reservoir engineering to biological imaging where sparse domains naturally exist.
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