There has been steady growth in the adoption of Unmanned Aerial Vehicle (UAV) swarms by operators due to their time and cost benefits. However, this kind of system faces an important problem, which is the calculation of many optimal paths for each UAV. Solving this problem would allow control of many UAVs without human intervention while saving battery between recharges and performing several tasks simultaneously. The main aim is to develop a Reinforcement Learning based system capable of calculating the optimal flight path for a UAV swarm. This method stands out for its ability to learn through trial and error, allowing the model to adjust itself. The aim of these paths is to achieve full coverage of an overflight area for tasks such as field prospection, regardless of map size and the number of UAVs in the swarm. It is not necessary to establish targets or to have any previous knowledge other than the given map. Experiments have been conducted to determine whether it is optimal to establish a single control for all UAVs in the swarm or a control for each UAV. The results show that it is better to use one control for all UAVs because of the shorter flight time. In addition, the flight time is greatly affected by the size of the map. The results give starting points for future research, such as finding the optimal map size for each situation.

The number of applications using unmanned aerial vehicles (UAVs) is increasing. The use of UAVs in swarms makes many operators see more advantages than the individual use of UAVs, thus reducing operational time and costs. The main objective of this work is to design a system that, using Reinforcement Learning (RL) and Artificial Neural Networks (ANNs) techniques, can obtain a good path for each UAV in the swarm and distribute the flight environment in such a way that the combination of the captured images is as simple as possible. To determine whether it is better to use a global ANN or multiple local ANNs, experiments have been done over the same map and with different numbers of UAVs at different altitudes. The results are measured based on the time taken to find a solution. The results show that the system works with any number of UAVs if the map is correctly partitioned. On the other hand, using local ANNs seems to be the option that can find solutions faster, ensuring better trajectories than using a single global network. There is no need to use additional map information other than the current state of the environment, like targets or distance maps.

Automatic detection of Alzheimer’s disease is a very active area of research. This is due to its usefulness in starting the protocol to stop the inevitable progression of this neurodegenerative disease. This paper proposes a system for the detection of the disease by means of Deep Learning techniques in magnetic resonance imaging (MRI). As a solution, a model of neuronal networks (ANN) and two sets of reference data for training are proposed. Finally, the goodness of this system is verified within the domain of the application.