Ashraf Hussein

The ability to study and understand complex, transient phenomena is critical to the solution of many scientific and engineering problems. A typical time-varying dataset from a computational fluid dynamics (CFD) simulation can contain hundreds of time-steps, and each time-step can have more than millions of data points. Generally, multiple values are stored at each data point. As a result, some

In this paper, a platform for managing and providing remote access to robots was developed and constructed. The system helps to schedule, perform and analyze experiments using minirobots. A solution for recharging the robots automatically has been included in the system in order to save the time needed for manual recharging. The system can automatically interrupt the experiments, charge robots and then resume experiments. To reach this level of autonomy, a positioning system, path planning technique along with video streaming have been developed and implemented.

This paper presents an integrated framework for surface reconstruction capable of handling large scale clouds of points. This framework is based on two proposed methods for implicit surface fitting and polygonization to convert a cloud of unorganized points into an optimized surface. The proposed fitting method employs the partition of unity (POU) method associated with the radial basis functions (RBF)

The mapping algorithms address the problem of acquiring spatial models of physical environments using mobile robots. We will focus on using the indoor exploration algorithms to construct a map for such environment. Therefore, the problem under consideration lies ...

Confidence in a pairwise local sequence alignment is a fundamental problem in bioinformatics. For huge DNA sequences, this problem is highly compute-intensive because it involves evaluating thousands of local alignments to construct an empirical score distribution. Recent parallel solutions support only small sequence sizes and/or are based on sophisticated infrastructures that are not available for most research labs. This paper presents an efficient parallel solution for evaluating the statistical significance for a pair of huge DNA sequences using cloud infrastructures. This solution can receive requests from various researchers via web-portal and allocate resources according to the demand. As it is cloud-based solution, it improves robustness, scalability and performance. The fundamental innovation in this research work is proposing an efficient solution that utilizes both shared and distributed memory architectures using the cloud technology to enhance the performance of evaluating the statistical significance for pair of DNA sequences. In this manner, the condition of the sequence size is released to be in megabyte-scale, which was not supported before. The present solution was verified against other recent parallel solutions, and the performance evaluation was carried out on Microsoft's Cloud. The results show that the performance scales with relatively linear speedup, as the number of instances increases.

... Faculty of Computer and Information Sciences, Ain shams University, Cairo, 11566, Egypt.maha.m.azab@gmail.com, dr_howida@cis.asu.edu.eg, ashrafh@acm.org ABSTRACT ... 0 5000 10000 15000 20000 25000 0 0.2 0.4 0.6 0.8 1 1.2 alpha C alpha E alpha T alpha ...

Mobile visualization allows users to visualize data anywhere, anytime, on various mobile clients connected by wireless networks. In this paper, an efficient framework is proposed for the remote rendering of large point-based 3D models represented by QSplats Level-Of-Detail (LOD) on mobile devices. As client-oblivious data model is used, rendering tasks are performed on mobile devices ranging from powerful workstations to PDAs and cell phones. On the server side, the framework renders the scenes of the 3D models via the effective utilization of multicore processor(s). The high-level requirements that guided the formulation of the parallel rendering are (a) the problem domain is highly irregular, motivating the use of low-overhead dynamic load-balancing to effectively utilize the multicore processor(s) and (b) the hidden delays encountered with the multicore processors, e.g. to maintain cache consistency. In this manner, novel dynamic load balancing schemes are introduced to reach the optimum performance of the parallel rendering. These schemes are evaluated through the processing of several 3D models with different sizes. In addition, the hidden delays encountered with the multicore processors are investigated. The proposed framework exhibits remarkable efficiency in rendering 3D models especially for the large and sophisticated ones.

This paper presents an intelligent model for stock market signal prediction using Multi-Layer Perceptron (MLP) Artificial Neural Networks (ANN). Blind source separation technique, from signal processing, is integrated with the learning phase of the constructed baseline MLP ANN to overcome the problems of prediction accuracy and lack of generalization. Kullback Leibler Divergence (KLD) is used, as a learning algorithm, because it converges fast and provides generalization in the learning mechanism. Both accuracy and efficiency of the proposed model were confirmed through the Microsoft stock, from wall-street market, and various data sets, from different sectors of the Egyptian stock market. In addition, sensitivity analysis was conducted on the various parameters of the model to ensure the coverage of the generalization issue. Finally, statistical significance was examined using ANOVA test.

This paper presents an intelligent model for stock market signal prediction using Multi Layer Perceptron (MLP) Artificial Neural Networks (ANN). Blind source separation technique, from signal processing, is integrated with the learning phase of the constructed baseline MLP ANN to overcome the problems of prediction accuracy and lack of generalization. Kullback Leibler Divergence (KLD) is used, as a learning algorithm, because it converges fast and provides generalization in the learning mechanism. Both accuracy and efficiency of the proposed model were confirmed through the Microsoft stock, from wall-street market, and various data sets, from different sectors of the Egyptian stock market. In addition, sensitivity analysis was conducted on the various parameters of the model to ensure the coverage of the generalization issue.

... {doi:10.1061/(ASCE)0893-1321 ... Working paper, Croucher Advanced Study Institute on WindTunnel Modeling, Hong Kong University of Science and Technology ... Hasager, CB, Paulsen, US, Hansen, OF, Enevoldsen, K., Youssef, LG, Said, US, Moussa, AA, Mahmoud, MA, Yousef ...

Fuzzy clustering is one of the most frequently used methods for identifying homogeneous regions in remote sensing images. In the case of large images, the computational costs of fuzzy clustering can be prohibitive unless high performance computing is used. Therefore, efficient parallel implementations are highly desirable. This paper presents results on the efficiency of a parallelization strategy for the Fuzzy c-Means (FCM) algorithm. In addition, the parallelization strategy has been extended in the case of two FCM variants, which incorporates spatial information (Spatial FCM and Gaussian Kernel-based FCM with spatial bias correction). The high-level requirements that guided the formulation of the proposed parallel implementations are: (i) find appropriate partitioning of large images in order to ensure a balanced load of processors; (ii) use as much as possible the collective computations; (iii) reduce the cost of communications between processors. The parallel implementations were tested through several test cases including multispectral images and images having a large number of pixels. The experiments were conducted on both a computational cluster and a BlueGene/P supercomputer with up to 1024 processors. Generally, good scalability was obtained both with respect to the number of clusters and the number of spectral bands.

... Dina Reda Khattab* Amr Hassan Abdel Aziz † Scientific Computing Department, Faculty of Computers and Information Sciences, Ain Shams University. Ashraf Saad Hussein‡ Abstract ... TOLBA, MF, HUSSIEN, AS, ABDEL AZIZ, AH, and IBRAHIM, HE 2002. ...