Recent developments in nanotechnology provided an opportunity to solve many complex problems in the field of energy. Performance investigation of the nanoscale thermal cycles can prove crucial in the development of efficient and less polluting energy system. Due to the influence of boundary phenomenon and quantum degeneracy effects, a nanoscale engine performs according to statistical quantum thermodynamics instead of classical thermodynamics. In this study, a nanoscale Stirling engine operating on an ideal Maxwell-Boltzmann gas is investigated for multiobjective optimization.Optimization problem of Stirling cycle is formed considering the thermal efficiency, ecological coefficient of performance and entropy generation. An application example of a nanoscale Stirling engine is presented and solved using Heat Transfer Search algorithm. Maxwell-Boltzmann gas restricted in a finite domain is studied and the effect of different parameters, such as surface area ratio, volume ratio, and temperature ratio of the domain, is investigated. Sensitivity analysis is carried out to identify the effect of design variables on the performance parameters. Further, influence of the source temperature and the number of particles of working fluid on the objective functions is studied and presented.

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Abstract-There are many methods proposed for Background Subtraction algorithm in past years. Background subtraction algorithm is widely used for real time moving object detection in video surveillance system. In this paper we have studied and implemented different types of methods used for segmentation in Background subtraction algorithm with static camera. This paper gives good understanding about procedure to obtain foreground using existing common methods of Background Subtraction, their complexity, utility and also provide basics which will useful to improve performance in the future . First, we have explained the basic steps and procedure used in vision based moving object detection. Then, we have debriefed the common methods of background subtraction like Simple method, statistical methods like Mean and Median filter, Frame Differencing and W4 System method, Running Gaussian Average and Gaussian Mixture Model and last is Eigenbackground Model. After that we have implemented all the above techniques on MATLAB software and show some experimental results for the same and compare them in terms of speed and complexity criteria.

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