Parallel parking, in general, is a moderate difficulty maneuver. Moreover, for inexperienced drivers, it can be a stressful situation that can lead to errors such as stay far from the sidewalk or damage another vehicle resulting in traffic tickets that range from simple parking violation to crash-related violations. In this work, we propose a computationally effective approach to perform a collisionfree parallel parking. The method will calculate the minimum parking space needed and then the efficient path for the parallel parking. This method is computationally inexpensive in comparison with the current state of the art. Moreover, it could be used by any car because the parameters needed to perform all computations are taken from the specifications of real cars. Preliminary results of this work were summarized in [1] that was presented at the 15th International IEEE Conference on Intell...

Parallel parking, in general, is a moderate difficulty maneuver. Moreover, for inexperienced drivers, it can be a stressful situation that can lead to errors such as stay far from the sidewalk or damage another vehicle resulting in traffic tickets that range from simple parking violation to crash-related violations. In this work, we propose a computationally effective approach to perform a collisionfree parallel parking. The method will calculate the minimum parking space needed and then the efficient path for the parallel parking. This method is computationally inexpensive in comparison with the current state of the art. Moreover, it could be used by any car because the parameters needed to perform all computations are taken from the specifications of real cars. Preliminary results of this work were summarized in [1] that was presented at the 15th International IEEE Conference on Intell...

El presente proyecto consiste en implementar un sistema de reconocimiento de caracteres numéricos escritos a mano para la actualización de una base de datos. El funcionamiento del sistema se basa en tomar una foto con una cámara web a un formulario de datos llenados manualmente, reconocerlos y guardarlos en la base de datos, en sus casillas correspondientes. El sistema tiene la capacidad de reconocer números escritos a mano gracias al entrenamiento de redes neuronales. Para un correcto entrenamiento, se utilizaron 185 ejemplares de cada número escritos por diferentes personas (un total de 1850 caracteres). El producto final está implementado en Matlab y se relaciona con las bases de datos de Microsoft Access, donde se guardan los números reconocidos.