Examinando por Materia "Interface"
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- PublicaciónAcceso abiertoAplicación de control de un bípedo humanoide para animación en stop motion(Universidad de San Buenaventura - Cali, 2015) López López, Carlos Andrés; Marín Velásquez, Deysi; Hurtado Banguero, Andrés FelipeThis undergraduate thesis was realized as an applied investigation. It was developed in order to integrate a humanoid biped controlled by an application through Bluetooth technology in a scene created under Stop Motion technique. It explains the necessary process to accomplish the implementation and construction of a scale set with its corresponding props, artistic adaptation and lightning, in addition, the making of the puppets suits and the changes in the biped facing. Additionally the previous research to programme an interface, following the usability parameters, applying knowledge of color theory, distribution and metaphors (icons), evaluating the possible programming languages compatibles with the robot and with the greatest benefits. Afterwards there are named the tests than was made to the system with its results.
- PublicaciónAcceso abiertoImplementación de una interfaz cerebro-computador (BCI) que permita controlar el movimiento de elevación y avance durante el vuelo de un cuadricóptero (dron)(Universidad de San Buenaventura - Cali, 2017) Castro Gutiérrez, Andrés Felipe; Ortega Hernández, Juan Felipe; Valencia Murillo, José FernandoIn this project, a brain-computer interface (BCI) is implemented allowing flight control of a simulated drone through the use of steady state visual evoked potentials (SSVEP) that were generated by a light dashboard that could be adjusted to blink at different frequencies. Through a sweep of frequencies made between 20 and 40 Hz, five different frequencies can be identified: 22, 25, 26, 27 and 34 Hz, which were assigned to the same number of movements: ascent, descent, advance, rewind and rotate clockwise, respectively. The project makes use of a Matlab script that is responsible for the reception and processing of the EEG data, identifying the stimulus selected by the user, calculating which of the five frequencies has the highest energy during a control period of two seconds. The interface was validated by applying five pattern tests to six users, each test had the duration of one minute, the efficiency found was between 72.18% and 95.33%.