Abstract : Recognizing human activities in fully controlled environment is very simple task but in partially observable and uncontrolled environments recognizing activities from videos is a challenging problem and has many practical applications for instance Security and Surveillance. Computer Vision has been researched for a long period of time to recognize human activities from video on which the environment is uncontrolled, the problem is reduced to activity prediction from unfinished activity streaming, which has been studied by many researchers. There could be uncontrolled environments that may create error for the recognition. In this paper, we try to combine both supervised and unsupervised learning network method that can recognize human activities from both controlled and uncontrolled environments.

Abstract : In methodology of face recognition includes the phases of dataset preparation, image pre-processing, feature extraction and classification. In which Feature Extraction step in face recognition is a vital role for accuracy of recognition. There are so many feature extraction technique developed through research but no one technique is sufficient for extracting all the feature of a Face image so that 100% can be achieved. So there are a need of how to increases the recognition rate in face recognition. The research issue of Face Recognition is to select the features which are required to represent a Face Recognition. In this paper we presented new features extraction techniques for face recognition using Discrete Cosine Transform and Gabor Filter.

Abstract : Maximum power point trackers (MPPTs) play an important role in the photovoltaic (PV) power system to maximize the overall efficiency of the system for available operating conditions. Hence lots of researchers have dev loped different Maximum Power Point tracking algorithms and shown superior results. In this paper authors have studied the various aspects of maximum power point tracked solar photo voltaic system using Hill climbing, Incremental conductance. Look up method and Particle Swarm Optimization (PSO) based method. The exhaustive review clearly shows that modified hill climbing methods give superior results in comparison to conventional hill climbing method.

Abstract : Renewable energy sources are the most important and emerging energy sources as the non renewable sources are depleting day by day. Solar energy or the photovoltaic (PV) energy is gaining popularity since it is clean, pollution free and long lasting. For maximum output power from a solar cell various tracking (MPPT) method are developed and adopted but the hunt for a better one is still on. In the same context this paper presents a Particle swarm optimization (PSO) based generalized PV model using Matlab/ Simulink software. The major advantage of this paper is that we can use it for the maximization of the PV output power or for the minimization of difference (or error) between the maximum power and power obtained in each trial. Furthermore, the developed algorithm is simple and the proposed model can be quite easily implemented on the test system. To estimate the effectiveness of the proposed model it was tested on Matlab/Simulink environment for 6 different population sizes and for each pop size 30 trails are carried out. Its results are compared with the Hill climbing and Look up table method and the competence of PSO based method in terms of convergence speed and maximum power is highlighted using simulation results.