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Mobility management is one of the major issues in mobile networks to provide an efficient and low-cost service. In this paper, we intend a prediction-based location management scheme for locating a mobile host (MH) or mobile station, which depends on its history of movement pattern of a mobile subscriber. A multilayer neural network (MNN) model for mobile movement prediction is designed to predict the future movement of a mobile host. For predicting the location of a mobile host the MNN is trained with respect to the data obtained from the movement pattern. The difficulties associated with the location management can be solved by nonlinear neural network that is computationally efficient. The major issue in feed forward neural network such as Multilayer perceptron (MLP) trained with back Propagation (BP) is that it requires a large amount of computation time for learning the network. Functional Link Neural Network (FLNN) is proposed here and that is simpler than MLP-BP. This is basically a single layer structure in which nonlinearity is introduced where the input pattern is enhanced with nonlinear functional expansion. The novelty of the proposed work is it requires less computation than that of MLP-BP. With proper choice of functional expansion in case of FLANN, this network performs better than multilayer perceptron with back propagation. It is observed from the simulation result that FLANN outperforms MLP-BP in terms of performance error. It is also shown that proposed network is computationally cheap and gives better classification accuracy than that of MLP classifier. [ABSTRACT FROM PUBLISHER]

This work describes the neural network technique to solve location management problem. A multilayer neural model is designed to predict the future prediction of the subscriber based on the past predicted information of the subscriber. In this paper a prediction based location management scheme is proposed for locating a mobile terminal in a communication without losing quality maintain a good response. There are various methods of location management schemes for prediction of the mobile user. Based on individual characteristic of the user, prediction based location management can be implemented. This work is purely analytical which need the past movement of the subscriber. The movement of the mobile target is considered as regular and uniform. An artificial neural network model is used for mobility management to reducing the total cost. Single or multiple mobile targets can be predicted. Among all the neural techniques multilayer perceptron is used for this work. The records is collected from the past movement and is used to train the network for the future prediction. The analytical result of the prediction method is found to be satisfactory. [ABSTRACT FROM PUBLISHER]

Speech enhancement aims to improve speech quality by using various algorithms. The main objective of enhancement is to improvement in intelligibility and overall perceptual quality of degraded speech signal using audio signal processing techniques. In the field of speech enhancement, enhancing of speech means degraded by noise in its wide range of applications such as mobile phones, VoIP, teleconferencing systems etc. In general there are three different methods used to estimate speech intelligibility. Namely, Speech Intelligibility Index (SII), Speech Transmission Index (STI) and Articulation Index (AI). Here it is proposed that SII is most robust physical measure and the comparison between Speech Intelligibility index in presence of stationary noise (White Gaussian Noise) and non-stationary noise (Speech noise) is done. Simulation result shows that SII is better in presence of non-stationary noise (a female voice of sampling frequency 16 KHz). Here two wideband speech signals are considered for performance evaluation since it brings the improvement over traditional narrowband such as increases the intelligibility and enables the spatial auditory displays etc. The speech signal are generated and simulated with the MATLAB environment. The real time speech signals are recorded with the help of acoustic sensor present inside the microphone. [ABSTRACT FROM PUBLISHER]