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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jss.2010.06.020 Byline: Hossein Rahmani, Mohammad Mehdi Faghih, Mohsen Ebrahimi Moghaddam Keywords: Disk scheduling; GSR; Feasible schedule; Scan-group Abstract: Disk scheduling has an important role in QOS guarantee of soft real-time environments such as video-on-demand and multimedia servers. Since now, some disk-scheduling algorithms have been proposed to schedule real-time disk requests. One of the most recent algorithms is global seek-optimizing real-time (GSR) that schedules the disk requests with different ready times by a global regrouping scheme. In the present paper, we propose a real-time disk-scheduling algorithm based on GSR that is called IGSR (improved GSR). IGSR creates the scan-groups of the requests and tries to find a good feasible schedule by optimized grouping with considering another chance for tasks that miss their deadlines at initial grouping. With regard to the admission policy of tasks, two different version of proposed method are presented: the first one has been designed for the case that all the disk requests available simultaneously and second one has been designed for the case that requests are admitted dynamically (GSR does not support the second one). It means that in the second case, the request queue may change when a task is running but in the first one it does not change. Simulation results showed IGSR outperformed GSR and some other related works in terms of maximum supportable streams, number of missed deadlines, and disk throughput. Author Affiliation: Electrical and Computer Engineering Department Shahid Beheshti University, G.C, Valenjak Ave., Tehran 1983963113, Iran Article History: Received 30 June 2009; Revised 9 June 2010; Accepted 9 June 2010

Color constancy is the ability of computing color constant descriptors of objects independent of the light illuminating the scene. Although many algorithms exist for color constancy, they are all based on specific assumptions and none of these algorithms can be considered as universal. Gray World and Gray Edge are some samples of such methods. The Gray-World assumption and its variants form a class of color constancy algorithms that are called Gray algorithms. Although the Gray algorithms provide a good approximation of scene illuminant in many times, their performances are low when the Gray assumptions are not satisfied completely. In this article, we proposed a method to improve the Gray algorithms. This method which is called Neural Gray employs a neural network to model the Gray assumptions based on image statistics. In other words, Gray algorithms act as a global search to find the neighborhoods of the scene illuminant vector then the neural network acts as a local search and compensates the algorithms error. Experiments on some benchmark datasets showed that the proposed approach improved the performance of Gray algorithms and it also outperforms state of the art methods. © 2013 Wiley Periodicals, Inc. Col Res Appl, 39, 571-581, 2014 [ABSTRACT FROM AUTHOR]