This paper introduces a new trend in video compression that based on content based coding this solution called Object Based Video Coding (OBVC). A new solution for Discrete Cosine Transform (DCT) used in video compression is presented. This solution uses the Artificial Neural Networks (ANNs) and has been implemented and tested with the OBVC. This paper tries to present a solution based on OBVC and ANN to speed-up the encoding and decoding processes for video compression and get more compression ratio.

Face recognition could be applied to a variety of practical applications and problems, including security and criminal identification systems. Face recognition using eigenface approach was motivated by information theory as it provides a practical solution. In this paper, the Principal Component Analysis (PCA) is used for eigenfaces (eigenvectors) computation. These eigenfaces present the extracted features for the faces to be recognized. A multilayer Artificial Neural Network (ANN) with back propagation adaptive learning algorithm is used for the classification phase. A number of experiments have been conducted on the system using the Olivetti Research Laboratory (ORL) database. Promising results have been achieved. Total performance accuracy on the data set used reached 98%.

In this paper, a Neural Network Model is designed for the classification of normal and abnormal electrocardiography (ECG) signals. Linear Prediction Coding (LPC) is used for extracting the features of the signals generated from each patient. The features of the signals are applied as inputs to train and test the Neural Network. Different Neural Network architectures investigated in order to achieve a better performance. Test results show that, the classification accuracy of the network can reach 98 %.

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