User growth and penetration modeling for converged heterogeneous network

To evaluate the trail potential of converged heterogeneous network( CHN) market,the logistic method for adoption modeling of CHN is used. User growth & penetration have been taken as two variants to find saturation condition in market. Model is continuous in time but modifications are done for discrete recurrence equation,commonly known as logistic map. Dynamic and static phases are taken into consideration while penetration decay is not covered in this model.     

Modeling and analysis of multiservice queuing system for WiMAX network

An analytical queuing model is proposed for the classified services of WiMAX network. Simulation model is also developed that corresponds to the Markovian analytical model using Java modeling tool (JMT) . This is a new and efficient discrete event tool for queuing network modeling and workload analysis. QoS metrics have been evaluated for the multi-rate traffic in multiple scenarios. Results obtained from simulation are compared for validation and analysis. Outcomes show that the proposed model is more efficient than the conventional method by improving residence time,response time,increasing system throughput and efficiency at queuing level with a slight degradation in call acceptance factor.     

Application of improved BPNN in image restoration-learning coefficient

A new method of artificial intelligence based on a new improved back propagation neural network (BPNN) algorithm is partially applied in the problem of image restoration. In order to overcome the inherited issues in conventional back propagation algorithm i.e. slow convergence rate, longer training time, hard to achieve global minima etc., different methods have been used including the introduction of dynamic learning rate and dynamic momentum coefficient etc. With the passage of time different techniques has been used to improve the dynamicity of these coefficients. The method applied in this paper improves the effect of learning coefficient η by using a new way to modify the value dynamically during learning process. The experimental results show that this helps in improving the efficiency overall both in visual effect and quality analysis.     

Greyscale based learning in BPNN for image restoration problem

A new method of back propagation learning with respect to the problem of image restoration which is named as greyscale based learning in back propagation neural networks ( BPNN) is investigated. It is observed that by using this method the value of mean square error ( MSE) decreases significantly. In addition,this method also gives good visual results when it is applied in image restoration problem. This method is also useful to tackle the inherited drawback of falling into local minima by reducing its effect on overall system by bifurcating the learning locally different for different grey scale values. The performance of this algorithm has been studied in detail with different combinations of weights. In short,this algorithm provides much better results especially when compared with the simple back propagation algorithm with any further enhancements and without going for hybrid solutions.