Q.E. Elgamily

Work place: Communication and Electronics, Faculty of Engineering, Mansoura University, Egypt

E-mail: engqasim2@gamil.com


Research Interests: Systems Architecture, Embedded System, Computer systems and computational processes


Qasim Elgamily received B.Sc. Degree in Communication and Electronics from MET Academy with Total Grade Excellent with honor. He works as Demonstrator in MET Academy and Embedded System Instructor from 2016 till now in CIE Program at Faculty of Engineering Mansoura University, also working as Embedded System Instructor for several Training Companies and Embedded System developer. he had a diploma from NTL

Author Articles
Evaluation of Reconstructed Radio Images Techniques of CLEAN De-convolution Methods

By M.A. Mohamed A.H. Samrah Q.E. Elgamily

DOI: https://doi.org/10.5815/ijigsp.2018.10.03, Pub. Date: 8 Oct. 2018

In Modern Radio Interferometry Various Techniques have been developed for the Reconstruction of the high-dimensional Data scalability Radio Images. CLEAN Variants are widely used in Radio Astronomy because of its computationally efficiency and easiness to understand. CLEAN deconvolves different polarization component images independently and nonlinearly from the point source response by removing the dirty beam pattern form the images. CLEAN Algorithms have been evaluated in this paper for both single field "Deconvolution" (Hogbom, Clark, Clark Stokes, and Cotton Schwab) and multi-field "Deconvolution" (Multi Scale, Multi Frequency and Multi Scale Multi frequency). Based upon simulation results,  it is clear that more updated techniques are needed for Large radio telescopes to face big data, extended sources emissions and fast imaging issues which are using dimensionality reduction from the perspective of the compressed sensing theory and to study its interplay with imaging algorithms which are designed in the context of convex optimization combined with sparse representations. 

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