Neeraj Bokde

Analysis of differencing and decomposition preprocessing methods for wind speed prediction

Abstract Preprocessing methods improve prediction accuracy in a significant way. Generally, they stabilize data series mean and variance and remove irregularities. In this paper, two of these methods have been used and compared in the Pattern Sequence based Forecasting (PSF) algorithm. These preprocessing methods are based on differencing and decomposition principles. In the decomposition approach, the Ensemble Empirical Mode Decomposition-Pattern Sequence based Forecasting (EEMD-PSF) model is used. It decomposes the data into finite numbers of subseries and improves the performance of the PSF method. In the Difference Pattern Sequence based Forecasting (DPSF) method, the effects of trends, seasonality, and irregularity components are reduced to some extent and their consequences are tested on distinct datasets with different patterns. While comparing the effects of these preprocessing methods with the effect of the PSF method for sets of wind speed data collected in the autonomous region of Galicia, Spain, and National Renewable Energy Laboratory (NREL), USA, in terms of prediction accuracy, both methods have performed better than the contemporary methods including single PSF, ARIMA, and LSSVM methods. In terms of computational time consumption, the DPSF method has outperformed the results of the EEMD-PSF model. The simulations revealed that the hybridization of preprocessing and PSF methods has significantly outperformed other state-of-the-art methods for short term wind speed prediction.

Real-Time Abandoned Object Detection Using Video Surveillance

Due to increase of attacks in public places, security has now become a major issue in public places. In this paper, we have proposed an abandoned object detection through video surveillance with real-time alarming. We use dual background subtraction method to find out the static object. It is been assumed that object which is part of foreground for longer period of time slowly turns as part of background. Background modelling is done using approximate median model. For foreground processing background subtracting method is followed by ANDing operation of frames to find out the static object. The system is simple to design and not having complexity of filters or complex calculation. PETS 2006 database is used for testing algorithm. The result shows satisfactory performance even under complex condition of shadow, moving crowd, and lightning condition. MATLAB R2013a is used for compilation of system.

RemoteWSN: A novel technique for remotely visualizing connectivity in WSN working on a weight based routing algorithm

Usually, WSN systems are designed to gather sensor data from the monitored area. The data is gathered by base station from the nodes in the network. Monitoring of nodes in WSN is one of the important part of sensor networks. Conventionally, there are various visualization tools for monitoring WSN, which are connected to server PCs or base station. The drawbacks of such visualizer tools were distance and communication unavailability limitations. In this paper, RemoteWSN, a visualization platform for WSN along with a weight based routing algorithm is proposed. RemoteWSN is a location independent monitoring system to determine the connectivity of nodes in WSN. As a result, user will be facilitated to monitor WSN connectivity remotely through his cell phone. Performance of proposed system is evaluated by means of small scale experimental setup.