Analytical based approaches in Optical Character Recognition (OCR) systems can endure a significant amount of segmentation errors, especially when dealing with cursive languages such as the Arabic language with frequent overlapping between characters. Holistic based approaches that consider whole words as single units were introduced as an effective approach to avoid such segmentation errors. Still the main challenge for these approaches is their computation complexity, especially when dealing with large vocabulary applications. In this paper, we introduce a computationally efficient, holistic Arabic OCR system. A lexicon reduction approach based on clustering similar shaped words is used to reduce recognition time. Using global word level Discrete Cosine Transform (DCT) based features in combination with local block based features, our proposed approach managed to generalize for new font sizes that were not included in the training data. Evaluation results for the approach using different test sets from modern and historical Arabic books are promising compared with state of art Arabic OCR systems.

Massive multiple-input multiple-output (MIMO) is a critical component of 5G cellular networks, which utilizes large numbers of antennas at both the transmitter and receiver to enhance throughput and radiated energy efficiency. Various linear detection techniques are employed with massive MIMO to counteract path loss and interference, and maximize throughput. The first aim of this paper is to analyse the performance of uplink massive MIMO system for different linear detection techniques including: Maximum ratio combining (MRC), zero-forcing (ZF), regularized ZF (RZF) and minimum mean squared error (MMSE) over Rayleigh channel model. The second aim is to jointly investigate the optimal values of signal-to-noise ratio (SNR), the number of antennas M and the number of users K for maximizing the spectral efficiency (SE) and energy efficiency (EE) through simulation using MATLAB and 3D plotting patterns. The obtained results show that the best SE and EE are achieved by uplink massive MIMO setup while using optimal values of SNR, M and K. It is observed that MMSE achieved the best performance. However, it requires estimation of average SNR at BS. Therefore, the best choice is ZF or RZF without any need for SNR estimation.

This paper proposes a novel implementation of the level set method that achieves real-time level-set-based object tracking. In the proposed algorithm, the evolution of the curve is realized by simple operations such as switching values of the level set functions and there is no need to solve any partial differential equations (PDEs) . The object contour could change due to the change in the location, orientation or due to the changeable nature of the object shape itself. Knowing the contour, the average color value for the pixels within the contour could be found. The estimated object color and contour in one frame are the bases for locating the object in the consecutive one. The color is used to segment the object pixels and the estimated contour is used to initialize the deformation process. Thus, the algorithm works in a closed cycle in which the color is used to segment the object pixels to get the object contour and the contour is used to get the typical-color of the object. With our fast algorithm, a real time system has been implemented on a standard Pc. Results from standard test sequences and our real time system are presented.

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