This paper aims to describe the design and implementation of the height control system for the quadrotor AR. Drone 2.0 making use of a fuzzy logic in a previously established environment. This device has a height control system both in simulation and in the real platform. Three controllers are developed by fuzzy logic whose parameters are obtained from the drone's sensors in such a way that it allows to control height and angles of orientation (Pitch, Roll and Yaw) as long as certain levels of battery charge are considered so that the system does not become unstable. For the visualization and interaction with the drone a Matlab® interface is designed and implemented, that allows communication between the user and all system functions in such a way that the mode of execution can be chosen, follow the reference parameters autonomously, store data for a later analysis, and visualize the displacements to observe the efficiency of system.

Sign languages (or signed languages) are languages that use visual techniques, primarily with the hands, to transmit information and enable communication with deaf-mutes people. This language is traditionally only learned by people with this limitation, which is why communication between deaf and non-deaf people is difficult. To solve this problem we propose an autonomous model based on convolutional networks to translate the Colombian Sign Language (CSL) into normal Spanish text. The scheme uses characteristic images of each static sign of the language within a base of 24000 images (1000 images per category, with 24 categories) to train a deep convolutional network of the NASNet type (Neural Architecture Search Network). The images in each category were taken from different people with positional variations to cover any angle of view. The performance evaluation showed that the system is capable of recognizing all 24 signs used with an 88% recognition rate.