• Login
    View Item 
    •   Home
    • Research
    • Preprints
    • View Item
    •   Home
    • Research
    • Preprints
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguidePlumX LibguideSubmit an Item

    Statistics

    Display statistics

    A Neural Network Assisted Greedy Algorithm For Sparse Electromagnetic Imaging

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Preprintfile1.pdf
    Size:
    3.996Mb
    Format:
    PDF
    Description:
    Pre-print
    Download
    Type
    Preprint
    Authors
    Sandhu, Ali Imran
    Shaukat, Salman Ali
    Desmal, A.
    Bagci, Hakan cc
    KAUST Department
    Electrical Engineering Program
    Investment Fund
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019-11-15
    Permanent link to this record
    http://hdl.handle.net/10754/660752
    
    Metadata
    Show full item record
    Abstract
    Greedy pursuit algorithms (GPAs), are well appreciated candidates for accurate and efficient reconstruction of sparse signal and image processing applications. Even though many electromagnetic (EM) imaging applications are naturally sparse, GPAs have rarely been explored for this purpose. This is because, for accurate reconstruction, GPAs require (i) the exact number of non-zeros, 'k', in the unknown to be reconstructed. This information is not available a-priori for EM imaging applications, and (ii) the measurement matrix to satisfy the restricted isometric property (RIP), whereas the EM scattering matrix which is obtained by sampling the Green's function between measurement locations and the unknowns does not satisfy the RIP. To address the aforementioned limitations, two solutions are proposed. First, an artificial neural network (ANN) is trained on synthetic measurements, such that given a set of measurements, the ANN produces an estimate of 'k'. Second, Tikhonov second norm regularization term is added to the diagonal elements of the scattering matrix, which scales the eigenvalues of the scattering matrix such that it satisfies the RIP. The CoSaMP algorithm, which is at the heart of GPAs, is then applied, to accurately and efficiently reconstruct the unknown. The proposed scheme implicitly imposes the sparsity constraint, as the regularization parameter is specified by the ANN, hence no additional tuning is required from the user. Numerical results demonstrate the efficiency and superiority of the proposed scheme.
    Publisher
    arXiv
    arXiv
    1911.06514
    Additional Links
    https://arxiv.org/pdf/1911.06514
    Collections
    Preprints; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2021  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.