Improving the communication path reliability of WiMAX mesh network using multi sponsor technique

Recently Worldwide Interoperability for Microwave Access (WiMAX) mesh network has emerged as a promising wireless technology in order to enable fast, cost-effective network deployment. However, achieving these requirements is a daunting task due to the mesh subscriber station failures in the presence of the adversarial environment. In fact, mesh subscriber station failure is one of the important challenges in satisfying the requirements of the communication path in the WiMAX mesh network. To cope with the node failures during communication path bypassing the intermediate nodes, the enhancement of communication path reliability is of the utmost importance in the WiMAX mesh network. In this paper, a new technique based on multi sponsor nodes is presented to enhance the reliability of the multi hop communication path. Markov model based on the multi sponsor nodes is also applied to enhance the communication path reliability when the network face with malfunctioning nodes. Ultimately, a generic model based on the stochastic attribution of WiMAX mesh network is developed to measure the reliability of the multi hop communication path. Consequently, multi sponsor technique in WiMAX could more thoroughly improve the reliability of the communication path in the WiMAX mesh network

Artificial neural networks to predict of liquidus temperature in hypoeutectic Al-Si cast alloys

Determining the liquidus temperature of cast alloys is an important factor in considering the superheating temperature and melt treatment of aluminium-silicon cast alloys. In addition to experimental calculation, the liquidus temperature can also be determined using simulation software for more reliable results. In this study, Artificial Neural Network (ANN) with hyperbolic tangent was selected to predict the liquidus temperature of Al-Si alloys as a function of chemical composition. The neural network was trained with seven input parameters (Si, Fe, Cu, Mn, Mg, Zn and Ti) and one output parameter (liquidus temperature). Training and testing dataset has been chosen from different published works, any casting software and aluminium binary phase diagrams. The accuracy of neural network was verified using values reported in literatures. The result of this investigation has shown that the backpropagation feed forward neural network is accurate enough to predict liquidus temperature