Resumen: Este artículo propone un prototipo totalmente funcional para el monitoreo del transporte público, el principal objetivo es proporcionar información en tiempo real a los usuarios que utilizan los autobuses como medio de transporte, y al mismo tiempo, proporcionar datos históricos de cada unidad para conocer la información relacionada con los horarios y retrasos de las rutas realizadas en los diferentes circuitos; para lograr el objetivo se ha utilizado un conjunto de recursos y medios tecnológicos que se centran en las capacidades de una computadora de placa reducida Raspberry PI. De manera general, el prototipo está formado por cuatro módulos: el rastreador de GPS, que consta de dos componentes principales: una Raspberry PI y un módulo GPS NEO-6M Ublox. El servicio web y la base de datos se implementan en PostgreSQL, que almacena la información a través de un servicio web RestFul desarrollado en PHP. La aplicación web para la gestión de seguimiento de bus está desarrollada en PHP y utiliza el marco Bootstrap para la interfaz de usuario. Finalmente, la aplicación móvil para Android destinada a usuarios finales, se desarrolló en la plataforma de Visual Studio 2015 con Xamarin y se programó en C#.

No abstract

No abstract

In the last two-decade, robotics has attracted a lot of attention from the biomedical sectors, to help physically disabled people in their quotidian lives. Therefore, the research of robotics applied in the control of an anthropomorphic robotic arm to people assistance and rehabilitation has increased considerably. In this context, robotic control is one of the most important problems and is considered the main part of trajectory planning and motion control. The main solution for robotic control is inverse-kinematics, because it provides the angles of robotic arm joints. However, there are disadvantages in the algorithms presented by several authors because the trajectory calculation needs an optimization process which implies more calculations to generate an optimized trajectory. Moreover, the solutions presented by the authors implied devices where the people are dependent or require help from other people to control these devices. This article proposes an algorithm to calculate an accuracy trajectory in any time of interest using an LCD touch screen to calculate the inverse-kinematics and get the end-point of the gripper; the trajectory is calculated using a novel distribution function proposed which makes an easy way to get fast results to the trajectory planning. The obtained results show improvements to generate a safe and fast trajectory of an anthropomorphic robotic arm using an LCD touch screen allowed calculating short trajectories with minimal fingers moves.

In recent years, unmanned aerial vehicles (UAVs) have been used in different areas of applications such as rescue operations, surveillance, agriculture, aerial mapping, engineering applications and research, among others, in order to perform tasks with greater efficiency. This work focuses on the use of UAVs in the fishing sector in order to optimise the detection process of a shoal of fish. In this sense, the main idea is to perform images recognition using the images acquired through videos captured by UAV in the open sea; to achieve the objective the convolutional neural networks were used, a new dataset with different images captured through UAV videos in the open sea were taken into account, these classes correspond to dolphin, dolphin_pod, open_sea, and seabirds. The training tests were by transfer of learning using the following models: Inception V3, MobileNet V2, and NASNet-A (large) trained on TensorFlow platform. The experimental results show the detection performance with high-precision values in reasonable processing time. This study ends with a critical discussion of the experimental results.