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Cairo, Egypt

Nile University is a not-for-profit institution of higher education. The University was established in Egypt in July 2006 by the Egyptian Foundation for Technological Education Development .The Egyptian Foundation for Technological Education is a not-for-profit organization, dedicated to improving technology-related education.NU is a research university, offering graduate and undergraduate studies in Engineering, Technology, Management and Management of Technology . NU's emphasis in research and development activities is in the area of Communications and Information Technology. Wikipedia.

Deif A.M.,Nile University
Journal of Cleaner Production | Year: 2011

Manufacturing systems evolution is afunction in multiple external and internal factors. With today's global awareness of environmental risks as well as the pressing needs to compete through efficiency, manufacturing systems are evolving into a new paradigm. This paper presents a system model for the new green manufacturing paradigm. The model captures various planning activities to migrate from a less green into a greener and more eco-efficient manufacturing. The various planning stages are accompanied by the required control metrics as well as various green tools in an open mixed architecture. The system model is demonstrated by an industrial case study. The proposed model is a comprehensive qualitative answer to the question of how to design and/or improve green manufacturing systems as well as a roadmap for future quantitative research to better evaluate this new paradigm. © 2011 Elsevier Ltd. All rights reserved.

Bioinformatics services have been traditionally provided in the form of a web-server that is hosted at institutional infrastructure and serves multiple users. This model, however, is not flexible enough to cope with the increasing number of users, increasing data size, and new requirements in terms of speed and availability of service. The advent of cloud computing suggests a new service model that provides an efficient solution to these problems, based on the concepts of "resources-on-demand" and "pay-as-you-go". However, cloud computing has not yet been introduced within bioinformatics servers due to the lack of usage scenarios and software layers that address the requirements of the bioinformatics domain. In this paper, we provide different use case scenarios for providing cloud computing based services, considering both the technical and financial aspects of the cloud computing service model. These scenarios are for individual users seeking computational power as well as bioinformatics service providers aiming at provision of personalized bioinformatics services to their users. We also present elasticHPC, a software package and a library that facilitates the use of high performance cloud computing resources in general and the implementation of the suggested bioinformatics scenarios in particular. Concrete examples that demonstrate the suggested use case scenarios with whole bioinformatics servers and major sequence analysis tools like BLAST are presented. Experimental results with large datasets are also included to show the advantages of the cloud model. Our use case scenarios and the elasticHPC package are steps towards the provision of cloud based bioinformatics services, which would help in overcoming the data challenge of recent biological research. All resources related to elasticHPC and its web-interface are available at http://www.elasticHPC.org.

Ibrahim M.,Nile University | Ibrahim M.,University of Technology of Troyes | Youssef M.,Alexandria University
IEEE Transactions on Vehicular Technology | Year: 2012

Context-aware applications have been gaining huge interest in the last few years. With cell phones becoming ubiquitous computing devices, cell phone localization has become an important research problem. In this paper, we present CellSense, which is a probabilistic received signal strength indicator (RSSI)-based fingerprinting location determination system for Global System for Mobile Communications (GSM) phones. We discuss the challenges of implementing a probabilistic fingerprinting localization technique in GSM networks and present the details of the CellSense system and how it addresses these challenges. We then extend the proposed system using a hybrid technique that combines probabilistic and deterministic estimations to achieve both high accuracy and low computational overhead. Moreover, the accuracy of the hybrid technique is robust to changes in its parameter values. To evaluate our proposed system, we implemented CellSense on Android-based phones. Results from two different testbeds, representing urban and rural environments, for three different cellular providers show that CellSense provides at least 108.57% enhancement in accuracy in rural areas and at least 89.03% in urban areas compared with current state-of-the-art RSSI-based GSM localization systems. In additional, the proposed hybrid technique provides more than 6 and 5.4 times reduction in computational requirements compared with state-of-the-art RSSI-based GSM localization systems for rural and urban testbeds, respectively. We also evaluate the effect of changing the different system parameters on the accuracy-complexity tradeoff and how the cell tower and fingerprint densities affect system performance. © 2011 IEEE.

Deif A.M.,Nile University
International Journal of Production Research | Year: 2012

One of the ultimate targets of lean manufacturing paradigm is to balance production and produce at takt time in production cells. This paper investigates the performance of a lean cell that implements the previous lean goals under uncertainty. The investigation is based on a system dynamics approach to model a dynamic lean cell. Backlog is used as a performance metric that reflects the cell's responsiveness. The cell performance is compared under certain and uncertain external (demand) and internal (machine availability) conditions. Results showed that although lean cell is expected to be responsive to external demand with minimum waste, however, this was not the case under the considered uncertain conditions. The paper proposes an approach to mitigate this problem through employing dynamic capacity policy. Furthermore, the paper explores the effect of the delay associated with the proposed capacity policies and how they affect the lean cell performance. Finally, various recommendations are presented to better manage the dynamics of lean manufacturing systems. © 2012 Taylor & Francis.

ElBaz M.S.,Nile University
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention | Year: 2012

In this work, a novel active shape model (ASM) paradigm is proposed to segment the right ventricle (RV) in cardiac magnetic resonance image sequences. The proposed paradigm includes modifications to two fundamental steps in the ASM algorithm. The first modification includes employing the 2D-principal component analysis (PCA) to capture the inter-profile relations among shape's neighboring landmarks and then model the inter-profile variations between the training set. The second modification is based on using a multi-stage searching algorithm to find the best profile match based on the best maintained profile's relations and thus the best shape fitting in an iterative manner. The developed methods are validated using a database of short axis cine bright blood MRI images for 30 subjects with total of 90 images. Our results show that the segmentation error can be reduced by about 0.4 mm and contour overlap increased by about 4% compared to the classical ASM technique with paired Student's t-test indicates statistical significance to a high degree for our results. Furthermore, comparison with literature shows that the proposed method decreases the RV segmentation error significantly.

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