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Grid computing is an enhanced technology consisting of a pool of connected machines that belong to multiple organizations in different sites to form a distributed system. This system can be used to deal with complex scientific or business problems. It is developed to help share distributed resources that may be diverted in nature and solve many computing problems. The typical decentralized grid computing model faces many challenges, such as; different systems and software architectures, quickly handling the enormous amount of grid requesters, and finding the appropriate resources for the grid users. Some of the grid requests might need to get significant attention and fast response to the other requests. Usually, the Grid Broker (GB) works as a third party or mediator between grid service providers and grid service requesters. This paper introduced a new automated system that can help exploit the grid power, improve its functionality, and enhance its performance. This research presents a new architecture for the Grid Broker that can assist with high priority requests and be processed first. This system is also used to monitor and provision the grid providers' work during the job running. It uses a multi-agent system to facilitates its work and accomplish its tasks. The proposed approach is evaluated using the Jade simulator. The results show that using the proposed approach can enhance the way of dealing with high priority requests coming to the grid.

This paper presents a novel vehicle color classification technique for classifying vehicles into seven categories under different lighting conditions via color correction. First, to reduce lighting effects, a mapping function is built to minimize the color distortions between frames. In addition to color distortions, the effect of specular highlights can also make the window of a vehicle appear white and degrade the accuracy of vehicle classification. To reduce this effect, a window-removal task is performed to make vehicle pixels with the same color more concentrated on the analyzed vehicle. Thus, a vehicle can be more accurately classified into its corresponding category even when it is shone by strong sunlight. One major problem in vehicle color classification is that there are many shade colors; for example, white versus silver and black versus navy. Traditional methods lack the ability to classify vehicles with shade colors because a wrong classifier is designed by putting vehicles with the same label together even though their chromatic attributes are different. To treat this problem, a novel tree-based classifier is designed for classifying vehicles into chromatic/nonchromatic classes with their nonchromatic strengths and then into detailed color classes with their color features. The separation can significantly improve the accuracy of vehicle color classification even that vehicles are with various shade colors and captured under different lighting conditions.

This paper proposes a novel surveillance system for detecting exceptional scene changes as abnormal events with a mobile camera mounted on a robot. In contrast to abnormal event analysis using fixed cameras, three key problems should be tackled in this system, i.e., scene construction, robot localization, and scene comparison. For the first problem, scene construction, a clustering scheme is proposed for extracting a set of key frames from the surveillance environment. Each key frame is further divided into a set of patches, which forms a sparse representation for representing scene contents. In addition to the compression effect, the scheme can tackle the effects of misalignment and lighting changes well. For the localization problem, a novel patch matching method is proposed to reduce not only the size of the search space but also the size of the feature dimensions in similarity matching. To prune the search space, a set of projection kernels is used to construct a ring structure. Then, one order of time complexity in the similarity calculation can be reduced from the structure. After scene searching, the robot location is not always guaranteed to be successfully registered to the scene map. Thus, a novel spider-web map is proposed to tackle the effect of misalignment and then detect different exceptional scene changes from the videos. The proposed method has been rigorously tested on a variety of videos to demonstrate its superiority in object detection and abnormal scene change detection.