QRS detection is becoming more popular in detecting the heart beat rate. The improvement is done by using the new filter. The data and control parallelism is used in order to improve the execution time and speed of the parallel based hybrid MAMEMI filter technique This research work focus on providing better performance in heart beat detection algorithm by using parallel hybrid filter.An enhanced algorithm has been proposed to enhance the performance of QRS detection. Different parameters are used for the performance analysis. Accuracy,F_Measure, and Detection_Error_rate are the parameters which are used to evaluate the performance of heart beat algorithm. The results of proposed algorithm are compared with existing heart beat detection algorithm for performance comparison. On the other hand the performance of the proposed method is also improved using parallelism. Parallel proposed method shows better results than Sequential proposed method. The Mean improvement in execution time is 0.80. 

Lane detection has recognition in real time vehicular ad-hoc system. That study work concentrate on giving greater efficiency in lane detection by utilizing the additive Hough transform to identify the curve lanes and convert into data parallelism in order to improve the speed of the proposed technique by using fork and join process. To accomplish performance evaluation various metrics is likely to be considered. The performance of lane detection algorithms is generally evaluated in terms of algorithm results and parallel results. Algorithm results is evaluated in terms of accuracy, error rate, execution time ,overhead and parallel results is evaluated in terms of speed, efficiency etc. To complete performance comparison the result of proposed algorithm is going to be compared with existing lane detection algorithms. Intelligent transportation systems are available these days for increasing the safety of the vehicles and reduce incident ratio. A new technique which uses modified additive hough transform is used to reduce the limitations of existing technique. The proposed algorithm has been designed and implemented in MATLAB. 

Power Efficiency is the efficient use of power and is a crucial come forth in cloud computing environment. Green Computing is nothing but is a cloud computing with efficient use of power and green refers to make the environment friendly to the user by saving heat and power. Data centre power efficiency in cloud environment will be reduced when virtualization is used as contrary to physical resource deployment to book adequate to grant all application requests. Nevertheless, in any case of the resource provisioning approximation, occasion remains in the way in which they are made attainable and workload is scheduled. The objective of this research work is therefore to pack workload into servers, selected as a function of their cost to operate, to achieve (or as close to) the utmost endorsed employment in a cost-efficient manner, avoiding occurrences where devices are under-utilized and management cost is acquired inefficiently. This work has enhanced the existing work by introducing the dynamic wake up calls either to shut down the active servers or restart the passive server. The wakeup calls has been initiated dynamically. The overall objective is to decrease the response time of users which will be increased during wakeup time in existing research work.