Bioinformatics is one of the field where high performance computation widely used. Pattern matching is essential task in Bio-informatics. A powerful technique for searching sequence patterns in the biological sequence databases is the pattern recognition. Significant increase in the number of protein sequences and DNA expanded the need for the enhancement of performance of pattern matching. Hence fast and high performance algorithms are highly demanded in many applications of computational molecular biology and bioinformatics. In this paper we present a parallel processing approach for pattern matching algorithm using distributed parallel programming paradigm Message Passing Interface (MPI). The focus of the research is the implementation of basic algorithm naïve for pattern matching by utilizing compute nodes of high performance computing server optimally. The parallel algorithm finds correct matches and experimental results show very high performance gain over sequential approach.
Parallel computation has become a recent trend in research from past few years. Parallel Computation is widely used in weather prediction, earthquake prediction, nanotechnology, astronomy for the study of planetary movements, pharmaceutics, defense for weapon simulation and so on. Satellite plays a major role in military for communication and surveillance. Area surveillance is one of the major application of satellite used in defense. Even though the technology enhancement occurs day by day, nabbing the terrorist activities and illegal border crossing into the nation has become a major primary issue. In this paper, a novel parallel processing approach for border surveillance in monitoring the borders of land and marine of a nation using single satellite is presented. A video dehazing algorithm is considered for parallel processing using OpenMP, a shared memory programming. Independent tasks are assigned to multiple threads of a core exhibiting data and task parallelism. Thus proposed idea of parallel video dehazing resulted in increase in speedup compared to sequential execution time.
In this paper a pedestrian detection and tracking system is developed that consisting of simple segmentation scheme for pedestrian detection and tracking at night time inorder to avoid pedestrian vehicle related accident. In this pedestrian detection system we use an image or a video already available that is captured using NIR camera. Based on segmentation a new vertical edge detection technique is developed and used to identify edges due to pedestrians. After that blob detection and merging is performed using connected component labeling to form a potential pedestrian image blocks which is called as candidate blocks. In this paper two different type of tracking schemes are explained. One is based on template matching and other one is based on image segmentation. The candidate blocks are rejected based on some certain criteria in order to reduce occurred false positives blocks. MATLAB tool is used to simulate system and has been tasted using various pedestrian videos and for images of different size and situation. Knowing correct pedestrian detected by number of pedestrian scene in video gives the system accuracy. So the proposed algorithm meets the same robustness of reference by reducing the number of operations needed also reduces the processing time, satisfies the requirements of real time applications.
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