Earticle

Wireless Ad hoc sensor nodes are playing a vital role in wireless data transmission infrastructure. Due to its compact size and energy efficient structure these nodes can be successfully deployed in wireless Ad hoc infrastructure where these nodes can be efficiently transmit the disaster related sensed data to Sink nodes via Ad hoc relay stations. In this Research study we are going to propose a Model for the Disaster survivor detection based on extremely critical Disaster situation where this energy efficient Architecture can successfully trace and locate thousands of people in critical circumstances. The emphasis of the research focuses on earth quake based disasters. Our proposed Model can also be successfully integrated with Telemedicine based infrastructure for emergency response authorities to take necessary measures in a limited span of time.

Goalkeeper robot in the league of humanoid robots has a key role so that its wrong decision and incorrect jump can cause changes in the result of a competition. This paper presents a method to detect the ball on the ground during the match, determine the coordinates of the ball with respect to the robot, calculating the speed and direction of the ball and eventually the time and place that the ball hits the goal. This can be the basis of making correct decision for the goalkeeper robot. The method is based on chasing the ball and calculating the coordinates of collision point. The results of applying this method on small size humanoid robot was excellent, except for the situations that inadequate rate of imaging lead to losing the ball or mistaken a similar object for the ball by the robot, quite right decisions in other states were made.

In this paper we have proposed a modified PSO based classification model for multidimensional real dataset and we have studied the results of experiment by implementing particle swarm optimization in classification. Evaluation of the performance of PSO based classifier has been made by considering several variation of parameters of the standard particle swarm optimizer. Here we have used a multidimensional real cancer dataset for classification using PSO to study the behavior of PSO parameters and to observe the accuracy of classification of PSO based classifier in different iterations. Extensive simulation has been carried out using UCI data, on which classification is done using our proposed algorithm. We have also explored the possible influence of variants of PSO on accuracy of classification. The obtained results indicate that particle swarm optimization is an effective technique for classification and can be used successfully on more demanding problem domain.

This paper presents a general approach for designing position adaptive control of a robot manipulator electrically controlled using passivity technique. Since a direct passive control structure requires precise knowledge of the robot model, the main interest of this contribution is to provide a direct adaptation law in order to improve performances against both robot model parameters uncertainties and disturbances. Besides, the proposed configuration permits a very remarkable facility of implementation. Simulations results are presented to illustrate the main points of this paper.

Demand-side management(DSM) is used to describe the actions of a utility, beyond the customer's meter, with the objective of altering the end-use of electricity whether it be to increase demand, decrease it, shift it between high and low peak periods, or manage it when there are intermittent load demands - in the overall interests of reducing utility costs. In other words DSM is the implementation of those measures that help the customers to use electricity more efficiently and in doing so reduces the customer utility costs. DSM can be achieved through improving the efficiency of various end-uses through better housekeeping ,correcting energy leakages, system conversion losses, and using renewable energy(RE) systems. In the past few decades the renewable energy sources have received grater interest and considerable attention has been given to efficient energy conversion and utilization techniques. Due to seasonal & periodic variations a single renewable energy source cannot provide continuous supply. In this case the Hybrid system with combination of two or more Resources can provide a better efficiency with considerable optimal cost. Present paper discusses a case study of an Urban restaurant. As a first step ,walkthrough Energy Audit has been carried out and using Dialus package Uplamping technique has been proposed.In the next step ,HOMER ‘NRELs simulation Tool has been used to model Hybrid RE system with diesel generator as the backup. In the last step, based on the model developed in the HOMER, Return on investment(ROI) has been calculated with & without subsidy. The proposed ROI model has shown 20 % optimal contribution of Hybird RE for the problem considered in the present study.

This paper presents an automatic system for detection of breast cancer using data preprocessing and Bayesian network. In this study, ReliefF algorithm is used for reducing the dimension of breast cancer database then a pre-processing is done on the data and ultimately Bayesian network classifier is used for classification. The system performance has been compared with model NN (neural network) and AR + NN (neural networks combined with association rules). The dimension of input feature space is reduced from nine to eight by using ReliefF. In test stage, 3-fold cross validation method was applied to the Wisconsin breast cancer database to evaluate the proposed system performance. The correct classification rate of proposed system is 98.1%.This research offered that the preprocessing is necessary on this data and combination of ReliefF and Bayesian network can be used to obtain fast automatic diagnostic systems for breast cancer.

Intrusion prospects in cloud paradigm are many and with high gains, may it be a bad user or a competitor of cloud client. Distributed model makes it vulnerable and prone to sophisticated distributed intrusion attacks like Distributed Denial of Service (DDOS) and Cross Site Scripting (XSS). Confronting new implementation situations, traditional IDSs are not well suited for cloud environment. To handle large scale network access traffic and administrative control of data and application in cloud, a new multi-threaded distributed cloud IDS model has been proposed. Our proposed cloud IDS handles large flow of data packets, analyze them and generate reports efficiently. Transparent reports are instantly send for information of cloud user and expert advice for cloud service provider’s network mis-configurations through a third party IDS monitoring and advisory service.

The paper demonstrates the forecasting of an aircraft trajectory in the vertical plane using gradient descent method for training a feed forward neural network system. For prediction of trajectory a neural networks system has been trained using a set of some arbitrary trajectories and then used to forecast for the new ones. Sliding Window method is being used for predictions, which is able to consider real points during flight to improve the precision in prediction. The results show that neural network can successfully be applied for such predictions

In this paper, we investigate the impact of Forward Error Correction (FEC) codes namely Cyclic Redundancy Code and Convolution Code on the performance of OFDM wireless communication system for speech signal transmission over both AWGN and fading (Rayleigh and Rician) channels in term of Bit Error Probability. The simulation has been done in conjunction with QPSK digital modulation and compared with uncoded resultstal modulation. In the fading channels, it is found via computer simulation that the performance of the Convolution interleaved based OFDM systems outperform than that of CRC interleaved OFDM system as well as uncoded OFDM channels.

One of the highly globalized fields such as Marine Industry, shipping operations are hugely dependent on e-collaboration tools that holds potential to provide quick and readily accessible information across ports, organizations and people across nations concerned with industry. Improving operational efficiency is very much dependent on business process transformation by affording better communication and collaboration between the different parties and information technology. This paper focuses on proposing an architectural methodology to overcome the frequently occurring problems in maritime sector by establishing collaboration between the service providers and potential users of the collaboration tool by identifying key processes. The methodology is developed based on empirical studies of Norwegian SW national initiative, MIS project (http :/ / www .sintef.no/Projectweb/ MIS/ )