Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were proposed in this paper as two models of non-linear ANN based equalization techniques in order to optimize processing performance, tracking, and minimize error of the channel effects and the ascending noise with 16 QAM Modulation, this work will be referenced with one of the most used linear adaptive equalization; Recursive least squares (RLS), as an evaluation. The two models will be compared in terms of efficiency and robustness facing noisy channel.

In this paper, an approach of trajectory tracking is proposed. The approach is based on two controls. Firstly, a quasi sliding mode control is proposed of the angular velocity in aim to converge the angle error to zero in short time with asymptotic stability. Secondly, a global sliding mode control for linear velocity is proposed, in order to bring the position error to zero and ensure the asymptotic stability by using the Lyapunov theory. Finally, the proposed control shows the performance of the algorithm, and the simulation results show good convergence for circular, sinusoidal and specific trajectories.

In this paper, a new capacitive pressure sensor (CPS) is investigated and modeled by means of neural approach. The sensing principle in our pressure sensor is based on the determination of the change in the capacity induced by the applied pressure. A ring oscillator is used to convert the capacity variation of the pressure sensor to an output frequency. A multilayer perceptron neural network is used to predict the applied pressure which causes a variation of the capacity including the temperature effects. This model is implemented as an electronic device into PSPICE simulator library, where the device should reproduce faithfully the pressure sensor behavior. Moreover, a new inverse model called smart sensor has been developed, in order to remove the nonlinearity behavior of sensor response. The obtained results make the proposed smart sensor as a potential alternative for high performances pressure sensing applications.

Background: Collaborative learning is an important pedagogical strategy which gained a huge interest in critical domains such as the medical field. However, to ensure the quality of this learning method, it is necessary to focus intention not only on the cognitive aspect but also on the social activities that represent an essential issue during collaborative learning sessions. Our objective in this study is to highlight the collaborative aspect in the group learning method of clinical reasoning. Methods: In this work, we have focused on cognitive studies that are interested in the clinical reasoning processes, while proposing a model dedicated to the design of collaborative clinical reasoning learning environment in synchronous mode. This model is primarily interested in social activities that have a strong influence on the collaborative learning effectiveness, and they are generally treated implicitly by basing on the improvisation and spontaneity of the learners group. Results: The research idea was embodied through a collaborative learning clinical reasoning environment based on Web 2.0 technologies. We chose this technology to benefit from its ease of use and from its technical performances that can significantly contribute to the development of the cognitive and social aspects in the collaborative learning environment. Conclusion: Our first contact with medical students showed us that they are appreciating this learning method. Indeed, to evaluate objectively our choices reliability, we plan to accomplish this research with a quantitative study based on real experiences with clinicians and medical students. The suggested study will allow us to collect the necessary lessons to work in depth on the relevant questions concerning the cognitive and social activities in the collaborative clinical reasoning learning.