There are several ways that can be implemented in a vehicle tracking system such as recognizing a vehicle color, a shape or a vehicle plate itself. In this paper, we will concentrate ourselves on recognizing a vehicle on a highway through vehicle plate recognition. Generally, recognizing a vehicle plate for a toll-gate system or parking system is easier than recognizing a car plate for the highway system. There are many cameras installed on the highway to capture images and every camera has different angles of images. As a result, the images are captured under varied imaging conditions and not focusing on the vehicle itself. Therefore, we need a system that is able to recognize the object first. However, such a system consumes a large amount of time to complete the whole process. To overcome this drawback, we installed this process with grid computing as a solution. At the end of this paper, we will discuss our obtained result from an experiment.
The paper describes a new approach for treatment security issues in reconfigurable grids used for computing or communication, in particular, in the semantic web environment. The proposed strategy combines a convenient mathematical model, efficient combinatorial algorithms which are robust with respect to changes in the grid structure, and an efficient implementation. The mathematical model uses properties of weighted hypergraphs. Model flexibility enables to describe basic security relations between the nodes such that these relations are preserved under frequent changes in connections of the hypergraph nodes. The algorithms support construction of a grid with embedded security concepts on a given set of nodes. The proposed implementation makes use of the techniques developed for time and space-critical applications in numerical linear algebra. Our combination of the mentioned combined building blocks is targeted to the emerging field of the semantic web, where the security seems to be very important. Nevertheless, the ideas can be generalized to other concepts describable by weighted hypergraphs. The paper concentrates on explaining the model and the algorithms for the chosen application. The consistency of the proposed ideas for security management in the changing grid was verified in a couple of tests with our pilot implementation SECGRID.
Job Scheduling in Computational Grids is gaining importance due to the need for efficient large-scale Grid-enabled applications. Among different optimization techniques designed for the problem, Genetic Algorithm (GA) is a popular class of solution methods. As GAs are high level algorithms, specific algorithms can be designed by choosing the genetic operators as well as the evolutionary strategies such as Steady State GAs and Struggle GAs. In this paper we focus on Struggle GAs and their tuning for scheduling of independent jobs in computational grids. Our results showed that a careful hash implementation for computing the similarity of solutions was able to alleviate the computational burden of Struggle GA and perform better than standard similarity measures. This is particularly interesting for the scheduling problem in Grid systems, which due to changeability over time, has demanding time restrictions on the computation of planning the jobs to resources.