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    Efficient and Accurate Numerical Techniques for Sparse Electromagnetic Imaging

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    Name:
    Thesis.pdf
    Size:
    14.22Mb
    Format:
    PDF
    Description:
    Final Thesis
    Embargo End Date:
    2021-04-22
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    Type
    Dissertation
    Authors
    Sandhu, Ali Imran cc
    Advisors
    Bagci, Hakan cc
    Committee members
    Ooi, Boon S. cc
    Hoteit, Ibrahim cc
    Dorn, Oliver
    Program
    Electrical Engineering
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-04
    Embargo End Date
    2021-04-22
    Permanent link to this record
    http://hdl.handle.net/10754/662627
    
    Metadata
    Show full item record
    Access Restrictions
    At the time of archiving, the student author of this dissertation opted to temporarily restrict access to it. The full text of this dissertation will become available to the public after the expiration of the embargo on 2021-04-22.
    Abstract
    Electromagnetic (EM) imaging schemes are inherently non-linear and ill-posed. Albeit there exist remedies to these fundamental problems, more efficient solutions are still being sought. To this end, in this thesis, the non-linearity is tackled in- corporating a multitude of techniques (ranging from Born approximation (linear), inexact Newton (linearized) to complete nonlinear 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. More specifically, four novel in- verse scattering schemes are formulated and implemented. (i) A greedy algorithm is used together with a simple artificial neural network (ANN) for efficient and accu- rate EM imaging of weak scatterers. The ANN is used to predict the sparsity level of the investigation domain which is then used as the L0 - constraint parameter for the greedy algorithm. (ii) An inexact Newton scheme that enforces the sparsity con- straint on the derivative of the unknown material properties (not necessarily sparse) is proposed. The inverse scattering problem is formulated as a nonlinear function of the derivative of the material properties. This approach results in significant spar- sification where any sparsity regularization method could be efficiently applied. (iii) A sparsity regularized nonlinear contrast source (CS) framework is developed to di- rectly solve the nonlinear minimization problem using Landweber iterations where the convergence is accelerated using a self-adaptive projected accelerated steepest descent algorithm. (iv) A 2.5D finite difference frequency domain (FDFD) based in- verse scattering scheme is developed for imaging scatterers embedded in lossy and inhomogeneous media. The FDFD based inversion algorithm does not require the Green’s function of the background medium and appears a promising technique for biomedical and subsurface imaging with a reasonable computational time. Numerical experiments, which are carried out using synthetically generated mea- surements, 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.
    Citation
    Sandhu, A. I. (2020). Efficient and Accurate Numerical Techniques for Sparse Electromagnetic Imaging. KAUST Research Repository. https://doi.org/10.25781/KAUST-9G4K6
    DOI
    10.25781/KAUST-9G4K6
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-9G4K6
    Scopus Count
    Collections
    Dissertations; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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