Monitoring and classification of human activity has been an active area of research for the past few years due to the increasing demands in healthcare sector. Quick aid for falls in elderly persons and detecting emergency situations are few leading cause of such interest. In this paper, a human activity recognition system based on motion patterns on a smartphone is proposed for classification of activities such as fall, walk, run, ascending, and descending stairs. The binned distribution based feature of acceleration data has been used for classification purpose. A systematic approach for classification of different activities using threshold and multistage Support Vector Machine (SVM) has been developed. Experimental results show considerable accuracy in activity recognition with the proposed scheme.

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This paper lays out the current work and discuss about the virtual lab simulation of rolling element bearing defects either the inward or external race or on the moving component surface that are visually undetectable. The present experiment proposed in this paper teaches how a bearings fault in the system is monitored based on the vibration analysis. Bearing failures have incredible effect on industry and economy. Bearing disappointment is frequently ascribed to be one of the significant reasons for breakdown in mechanical pivoting machines work at low and high velocity. Bearing vibration can be useful in the detection of various faults and also helps in processing of effects of the defects present in the different components of the bearing. The vibrations created by a solitary point abandon on different parts of the bearing under steady spiral burden are anticipated by utilizing a hypothetical model. The model incorporates variety in the reaction because of the impact of bearing measurements, turning recurrence dispersion of burden.

Parallel and distributed systems that support the shared memory paradigm are becoming widely accepted in many areas of computing. The memory consistency model of a shared-memory multiprocessor system influences both the performance and the programmability of the system. Under optimal condition it is found that multithreading contributes to more than 50 percent of performance improvement, while the improvement from Relaxed Consistency Memory (RCM) models varies between 30-40 percent of total performance gain. The relaxed consistency memory model has been realized on a Graphics Processing Unit (GPU) using Open Computing Language (OpenCL) as the programming language. This memory model has been applied on a case study of high order matrix multiplication and their performance has been analysed in terms of two metrics: GPU Computation Percentage (GCP) and GPU Load Balance (GLB). With sufficient parallelism and high PCIe data transfer bandwidth, the RCM model on GPU gives the better performance in comparison to a sequential model on CPU.