A
Ali Awad
Researcher at Al-Azhar University – Gaza
Publications - 37
Citations - 292
Ali Awad is an academic researcher from Al-Azhar University – Gaza. The author has contributed to research in topics: Impulse noise & Pixel. The author has an hindex of 8, co-authored 29 publications receiving 225 citations. Previous affiliations of Ali Awad include Stevens Institute of Technology & Universiti Teknologi Petronas.
Papers
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Journal ArticleDOI
Standard Deviation for Obtaining the Optimal Direction in the Removal of Impulse Noise
TL;DR: This letter proposes a new technique of restoring images distorted by random-valued impulse noise, based on finding the optimum direction, by calculating the standard deviation in different directions in the filtering window.
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Recent advances in cleaner hydrogen productions via thermo-catalytic decomposition of methane: Admixture with hydrocarbon
Anuar Faua’ad Syed Muhammad,Ali Awad,Rahman Saidur,Rahman Saidur,Nurliyana Masiran,Abdus Salam,Bawadi Abdullah +6 more
TL;DR: In this paper, an extensive review has been made on the effectiveness of metallic catalyst in hydrocarbon reforming for COX free hydrogen production via different techniques and the effect of reaction temperature, gas hour space velocity and metal loading on the sustainability of thermocatalytic decomposition TCD of methane.
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High performance detection filter for impulse noise removal in images
TL;DR: In this paper, a high performance detection (HPD) filter is proposed for impulse noise removal in images, where the noisy pixels are detected iteratively through several phases, based on a set of unique similarity criteria.
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Non-oxidative decomposition of methane/methanol mixture over mesoporous Ni-Cu/Al2O3 Co-doped catalysts
TL;DR: In this paper, the authors reported an attempt to increase the catalyst deactivation time by using Cu promoted Ni-based catalyst, and methanol premixed methane gas as a feedstock.
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Impulse noise reduction in audio signal through multi-stage technique
TL;DR: The method adopted has turned out to be more successful in removing noisy samples and keeping the original ones intact and also the method in question highlights low computational complexity and easy to implement.