Hand gesture recognition is an attractive research field with a wide range of applications, including video games and telesurgery techniques. Another important application of hand gesture recognition is the translation of sign language, which is a complicated structured form of hand gestures. In sign language, the fingers' configuration, the hand's orientation, and the hand's relative position to the body are the primitives of structured expressions. The importance of hand gesture recognition has increased due to the prevalence of touchless applications and the rapid growth of the hearing-impaired population. However, developing an efficient recognition system needs to overcome the challenges of hand segmentation, local hand shape representation, global body configuration representation, and gesture sequence modeling. In this paper, a novel system is proposed for dynamic hand gesture recognition using multiple deep learning architectures for hand segmentation, local and global feature representations, and sequence feature globalization and recognition. The proposed system is evaluated on a very challenging dataset, which consists of 40 dynamic hand gestures performed by 40 subjects in an uncontrolled environment. The results show that the proposed system outperforms stateof-the-art approaches, demonstrating its effectiveness.

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This paper investigates optimum siting of wind turbine generators from the viewpoint of site and wind turbine generator selection. This analysis methodology is done at the planning and development stages of installation of wind power stations will enable the wind power developer or the power utilities to make a judicious and rapidly choice of potential site and wind turbine generator system from the available potential sites and wind turbine generators respectively. The methodology of analysis is based on the computations of annual capacity factors, which are done using the Weibull distribution function and power curve model. This method is applied to install a wind energy conversion system at four sites in Algeria.

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This article concerns a computer aided pathological speech therapy program, based on speech models such as the hidden Markov model and artificial intelligence networks, in order to help persons, suffering from language pathologies, follow a correction learning process, with different interactive feedbacks, aiming to evaluate the degree of evolution of the illness or the therapy. We dealt with the Arabic occlusive sigmatism as a prime approach, which is the inability to pronounce the [s] or [ ]. Results obtained are satisfying and the therapy program is prepared, for autonomous use by patients, for deep analysis and verifications.

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In this article, we introduce a localization system to reduce the accumulation of errors existing in the dead-reckoning method of mobile robot localization. Dead-reckoning depends on the information that comes from the encoders. Many factors, such as wheel slippage, surface roughness, and mechanical tolerances, affect the accuracy of dead-reckoning. Therefore, an accumulation of errors exists in the dead-reckoning method. In this article, we propose a new localization system to enhance the localization operation of the mobile robots. The proposed localization system uses the extended Kalman filter combined with infrared sensors in order to solve the problems of dead-reckoning. The proposed system executes the extended Kalman filter cycle, using the walls in the working environment as references (landmarks), to correct errors in the robot's position (positional uncertainty). The accuracy and robustness of the proposed method are evaluated in the experiment results' section.