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The brain-computer interface consists of connecting the brain with machines using the brainwaves as a mean of communication for several applications that help to improve human life. Unfortunately, Electroencephalography that is mainly used to measure brain activities produces noisy, non-linear and non-stationary signals that weaken the performances of Common Spatial Pattern (CSP) techniques. As a solution, deep learning waives the drawbacks of the traditional techniques, but it still not used properly. In this paper, we propose a new approach based on Convolutional Neural Networks (ConvNets) that decodes the raw signal to achieve state-of-the-art performances using an architecture based on Inception. The obtained results show that our method outperforms state-of-the-art filter bank common spatial patterns (FBCSP) and ShallowConvNet on based on the dataset IIa of the BCI Competition IV.

In order to provide ubiquitous access for the users, future generation network integrate a multitude of radio access technologies (RAT’S) which can interoperate between them. However, the most challenging problem is the selection of an optimal radio access network, in terms of quality of service anywhere at anytime. This paper proposes a novel ranking algorithm, which combines multi attribute decision making (MADM) and Mahalanobis distance. Firstly, a classification method is applied to build a classes which having the homogeneous criteria. Afterwards, the Fuzzy AHP, MADM method is applied to determine weights of inter-classes and intraclasses. Finally, Mahalanobis distance is used to rank the alternatives. The simulation results show that the proposed algorithm can effectively reduce the ranking abnormality and the number of handoffs