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EBAPy is an easy-to-use Python framework intended to help in the development of EEG-based applications. It allows performing an in-depth analysis of factors that influence the performance of the system and its computational cost. These factors include recording time, decomposition level of Discrete Wavelet Transform, and classification algorithm. The ease-of-use and flexibility of the presented framework have allowed reducing the development time and evaluating new ideas in developing biometric systems using EEGs. Furthermore, different applications that classify EEG signals can use EBAPy because of the generality of its functions. These new applications will impact human-computer interaction in the near future.

Vehicular accidents cause severe problems in our society including economic, material, and even life losses. The cause of those situations relies on several factors such as traffic density, vehicular flow, lack of traffic signaling and speed limit violations. Some of these problems cannot completely be eliminated but could be mitigated by proposing solutions such as people's awareness or intelligent radars to monitor speed limit violations. This work proposes a system to automatically generate fines in case of speed limit infractions. Our approach uses vehicular networks to monitor the vehicles' speed. We also propose a dissemination protocol to ensure the propagation and delivery of the generated fines at the road-side units, achieving a 94.99% and 99.91% fine delivery rate in urban scenarios with vehicles' densities of 30 and 200 vehicles per km 2 , respectively.INDEX TERMS Dissemination protocol, speed control system, speed limit infractions, vehicular networks.

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