Mohamed E. Khalifa

A Preemptive Version of the Min-Min Heuristic for Dynamically Mapping Meta-Tasks on a Distributed Heterogeneous Environment

Mapping and scheduling of Meta-tasks in distributed heterogeneous computing systems are complex computational problems. They are known to be NP-Complete except under a few special situations. Solving the mapping problem is basically deciding on which task should be moved to where and when, to improve the overall performance. There is a wide variety of approaches to the problem of mapping and scheduling in HC systems that are either static or dynamic. In this paper, we propose a preemptive (migratory) of the Min- min heuristic for mapping a set of independent tasks to machines in a HC suite. The proposed algorithm works dynamically to assign tasks in a batch-mode fashion. In our approach, we revise the decision taken by the Min- min heuristic and adjust its allocation strategy in order to improve machine (processor) utilization and hence achieve better mapping performance by minimizing the makespan.

Moving Shadow Removal for Multi-Objects Tracking in Outdoor Environments

Shadow detection and removal has had great interest in computer vision especially in outdoor environments. It is an important task for visual tracking, object recognition, and many other important applications. One of the fundamental challenges for accurate tracking is achieving invariance to shadows. Two or more separate objects can appear to be connected through shadows. Many algorithms have been proposed in the literature that deal with shadows. However, the problem remains largely unsolved and needs further research effort. This paper proposes a method for removing cast shadows from vehicles in outdoor environments. The proposed method employs the estimated background model of the video sequence and applies a Gamma decoding followed by a thresholding operation. Experimental results show the success of the proposed method in detecting and removing shadows robustly and leads to considerable improvements in multiple object tracking.

Medical Image Registration Using Mutual Information Similarity Measure

Nowadays Medical Imaging has an increasing need in patient treatment, not only it aids physicians in determining the correct diagnosis but also it reduces overall cost and speedup treatment plans. Medical Image Registration is a vital medical imaging application it aligns one image to another, to obtain a registered image has the information content of both images.

Randomized Algorithms for Mapping Clustered Object-Oriented Software onto Distributed Architectures

Distributed Object Oriented (DOO) applications have been developed for solving complex problems in various scientific fields. One of the most important aspects of the DOO systems is the efficient distribution of software classes among different nodes in order to solve the mismatch problem that may appear when the software structure does not match up the available hardware organization. We have proposed a multistep approach for restructuring DOO software. According to this approach, the OO system is partitioned into clusters that are then merged into larger groups forming what we call Merged Cluster Graph. The last step in this approach is concerned by mapping these merged clusters onto the target distributed architecture. Generally, the mapping problem is intractable thus allowing only for efficient heuristics. This paper presents two algorithms to solve the mapping problem using a randomized approach. The proposed algorithms has proved to be efficient, Simple and easy to understand and implement. Furthermore, the performance of the proposed algorithms was tested against some existing deterministic techniques. The experimental results showed an outstanding performance of the proposed algorithms in minimizing the overall mapping cost of the produced assignments.