Bilal A. Shehada

Work place: Department of Computer Engineering Islamic University of Gaza



Research Interests: Computational Engineering, Network Architecture, Network Security


Bilal A. Shehada was born in Saudi Arabia, in 1988. He received the B.Sc. degree from Islamic University of Gaza, in 2010. In 2011, he joined the Graduate Studies Program of Faculty of Engineering at Islamic University of Gaza at Gaza Strip, in Palestine, as a M.Sc. Student. From 2011 until now, he is working as Network Engineer at Ministry of Health (MOH) in Gaza, Palestine.

Author Articles
Enhancing Leakage Power in CPU Cache Using Inverted Architecture

By Bilal A. Shehada Ahmed M. Serdah Aiman Abu Samra

DOI:, Pub. Date: 8 Feb. 2013

Power consumption is an increasingly pressing problem in modern processor design. Since the on-chip caches usually consume a significant amount of power so power and energy consumption parameters have become one of the most important design constraint. It is one of the most attractive targets for power reduction. This paper presents an approach to enhance the dynamic power consumption of CPU cache using inverted cache architecture. Our assumption tries to reduce dynamic write power dissipation based on number of ones and zeros in the in-coming cache block data using bit to indicate is the block is mostly one or zero. This architecture reduces the dynamic write power by 17 %. We use Proteus Simulator to test that proposed circuit and performed the experiments on a modified version of the cacti6.0 simulator.

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Data Clustering Using Wave Atom

By Bilal A. Shehada Mahmoud Z.Alkurdi Wesam M. Ashour

DOI:, Pub. Date: 8 Aug. 2012

Clustering of huge spatial databases is an important issue which tries to track the densely regions in the feature space to be used in data mining, knowledge discovery, or efficient information retrieval. Clustering approach should be efficient and can detect clusters of arbitrary shapes because spatial objects cannot be simply abstracted as isolated points they have different boundary, size, volume, and location. In this paper we use discrete wave atom transformation technique in clustering to achieve more accurate result .By using multi-resolution transformation like wavelet and wave atom we can effectively identify arbitrary shape clusters at different degrees of accuracy. Experimental results on very large data sets show the efficiency and effectiveness of the proposed wave atom bases clustering approach compared to other recent clustering methods. Experimental result shows that we get more accurate result and denoised output than others.

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