Scheduling algorithms play an important role in design of real-time systems. Due to high processing power and low price of multiprocessors, real-time scheduling in such systems is more interesting; however, more complicated. Earliest Deadline First (EDF) and Least Laxity First (LLF) are two well-known and extensively applied dynamic scheduling algorithms on which many researches have already been done.However, to the best of our knowledge, the efficiency of aforementioned algorithms has not been compared under similar conditions. Perhaps the main reason is that LLF algorithm is fully dynamic and impractical to implement. In this research, we have used a job-level dynamic and practical version of LLF which is called Modified Least Laxity First (MLLF) algorithm instead of the traditional LLF and have compared its performance with EDF algorithm from many different aspects. The success ratio has been chosen as the key factor for evaluation of the algorithms.

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It has been proved that there is no optimal online scheduler for uniform parallel machines. Despite its non-optimality, EDF is an appropriate algorithm to use in such environments. However, its performance significantly degrades in overloaded situations. Moreover, EDF produces a relatively large number of migrations which may prove unacceptable for use on some parallel machines. In this paper a new deadlinebased algorithm for scheduling real-time tasks on uniform parallel machines is presented. The performance of this algorithm is then compared with that of EDF algorithm. It is shown that our proposed approach not only demonstrates a performance close to that of EDF in non-overloaded conditions but also has supremacy over EDF in overloaded situations in many aspects. Furthermore, it imposes much less overhead on the system.

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In this paper a fast edge detection algorithm based on a simple logic has been implemented for road boundary detection in non-uniform light condition. Road images taken in the campus have been used to test the algorithm. First the image samples are segregated into different segments depending on the intensity. Subsequently standard edge detectors are applied to extract the edges in each of the segments. A logical operation between the edges of the different segments brings out the edges of the final image.

Recently, there has been great interest in Bioinformatics among researches from various disciplines such as computer science, mathematics, statistics and artificial intelligence. Bioinformatics mainly deals with solving biological problems at molecular levels. One of the classic problems of bioinformatics which has gain a lot attention lately is Haplotyping, the goal of which is categorizing SNP-fragments into two clusters and deducing a haplotype for each. Since the problem is proved to be NP-hard, several computational and heuristic methods have addressed the problem seeking feasible answers. In this work it is shown that using PCM to solve Haplotyping problem in DALY dataset yields better results comparing to current available methods.