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George Baravdish

Researcher at Linköping University

Publications -  27
Citations -  181

George Baravdish is an academic researcher from Linköping University. The author has contributed to research in topics: Inverse problem & Flow (mathematics). The author has an hindex of 7, co-authored 25 publications receiving 146 citations.

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Proceedings ArticleDOI

Analysis of vehicular wireless channel communication via queueing theory model

TL;DR: A feasibility analysis for performing vehicular communication via a queueing theory approach based on a multi-server queue using real LTE traffic is presented and a M/M/m model is employed to evaluate the probability that a vehicle finds all channels busy, as well as to derive the expected waiting times and the expected number of channel switches.
Journal ArticleDOI

On Backward p(x)-Parabolic Equations for Image Enhancement

TL;DR: In this article, the backward p(x)-parabolic equation was investigated as a new methodology to enhance images and a novel iterative regularization procedure for the backward ρ-parabolic equa was proposed.
Journal ArticleDOI

Numerical reconstruction of brain tumours

TL;DR: A nonlinear Landweber method based on well-established models of reaction–diffusion type for brain tumour growth that recovers the initial density of the tumour cells starting from a later state by running the model backwards is proposed.
Journal ArticleDOI

Coefficient Identification in PDEs applied to Image Inpainting

TL;DR: The concept of parameter identification problems, which are inverse problems, as a methodology to inpainting is introduced and an error analysis shows that this approach is promising and the numerical experiments produces better results than the harmonic inPainting.
Book ChapterDOI

On tensor-based PDEs and their corresponding variational formulations with application to color image denoising

TL;DR: The necessary conditions for a PDE to be the E-L equation for a corresponding functional of an energy functional are shown and it is shown that the method compares favorably to current state-of-the-art color image denoising techniques.