This article presents a remote management architecture of an unmanned aerial vehicles (UAVs) fleet to aid in the management of solar power plants and object tracking. The proposed system is a competitive advantage for sola r energy production plants, due to the reduction in costs for maintenance, surveillance, and security tasks, especially in large solar farms. This new approach consists of creating a hardware and software architecture that allows for performing different tasks automatically, as well as remotely using fleets of UAVs. The entire system, composed of the aircraft, the servers, communication networks, and the processing center, as well as the interfaces for accessing the services via the web, has been designed for this specific purpose. Image processing and automated remote control of the UAV allow generating autonomous missions for the inspection of defects in solar panels, saving costs compared to traditional manual inspection. Another application of this architecture related to security is the detection and tracking of pedestrians and vehicles, both for road safety and for surveillance and security issues of solar plants. The novelty of this system with respect to current systems is summarized in that all the software and hardware elements that allow the inspection of solar panels, surveillance, and people counting, as well as traffic management tasks, have been defined and detailed. The modular system presented allows the exchange of different specific vision modules for each task to be carried out. Finally, unlike other systems, calibrated fixed cameras are used in addition to the cameras embedded in the drones of the fleet, which complement the system with vision algorithms based on deep learning for identification, surveillance, and inspection.

<p class="MsoNormal" style="text-justify: inter-ideograph; margin: 0in 26.05pt 10pt 0in; line-height: normal; text-align: justify;"><span style="font-family: 'Arial','sans-serif'; mso-fareast-font-family: Calibri;"><span style="font-size: small;">In recent years, learning management systems (LMS) have become very popular in almost all traditional universities, generating a new learning strategy approach, mixing elements from both traditional and online learning: the blended learning or b-learning. How these new environments influence teaching activities and learning processes are the main topic of this paper.<span style="mso-spacerun: yes;">  </span>References about this subject are also analyzed, enriching them with the expertise and opinion of authors and other teachers. Finally, the students’ point of view is presented, through the results of a survey of Polytechnic School students at Universidad Europea de Madrid.</span></span></p><p>------</p><p class="MsoNormal" style="margin: 0in 26.05pt 10pt 0in; line-height: normal; text-align: justify;"><span style="font-family: 'Arial','sans-serif'; mso-fareast-font-family: Calibri; mso-ansi-language: ES-PR;" lang="ES-PR"><span style="font-size: small;"><strong><strong>El b-learning a examen: Ventajas, desventajas y opiniones</strong></strong></span></span></p><p class="MsoNormal" style="margin: 0in 26.05pt 10pt 0in; line-height: normal; text-align: justify;"><span style="font-family: 'Arial','sans-serif'; mso-fareast-font-family: Calibri; mso-ansi-language: ES-PR;" lang="ES-PR"><span style="font-size: small;"><strong>Resumen</strong></span></span></p><p class="MsoNormal" style="margin: 0in 26.05pt 10pt 0in; line-height: normal; text-align: justify;"><span style="font-family: 'Arial','sans-serif'; mso-fareast-font-family: Calibri; mso-ansi-language: ES-PR;" lang="ES-PR"><span style="font-size: small;">Desde hace ya varios años, han proliferado los espacios virtuales de enseñanza en la práctica totalidad de centros universitarios de enseñanza presencial, dando origen a una nueva modalidad de enseñanza que recoge elementos de la enseñanza presencial y de la enseñanza en línea: el blended learning o b-learning. La posible influencia de estos espacios en la actividad docente y en el proceso de aprendizaje es el objeto de este artículo. Se analizan referencias sobre el tema, enriqueciéndolas con la opinión y experiencia de los autores y su entorno laboral concreto. Finalmente, se presenta la opinión de los alumnos a través de los resultados de una encuesta realizada a un grupo de estudiantes de la Escuela Politécnica de la Universidad Europea de Madrid.</span></span></p>

A dataset of Spanish road traffic images taken from unmanned aerial vehicles (UAV) is presented with the purpose of being used to train artificial vision algorithms, among which those based on convolutional neural networks stand out. This article explains the process of creating the complete dataset, which involves the acquisition of the data and images, the labeling of the vehicles, anonymization, data validation by training a simple neural network model, and the description of the structure and contents of the dataset (which amounts to 15,070 images). The images were captured by drones (but would be similar to those that could be obtained by fixed cameras) in the field of intelligent vehicle management. The presented dataset is available and accessible to improve the performance of road traffic vision and management systems since there is a lack of resources in this specific domain.

This work presents the development and evaluation of a real-time 3D reconstruction system using drones. The system leverages innovative artificial intelligence techniques in photogrammetry and computer vision (CDS-MVSNet and DROID-SLAM) to achieve the accurate and efficient reconstruction of 3D environments. By integrating vision, navigation, and 3D reconstruction subsystems, the proposed system addresses the limitations of existing applications and software in terms of speed and accuracy. The project encountered challenges related to scheduling, resource availability, and algorithmic complexity. The obtained results validate the applicability of the system in real-world scenarios and open avenues for further research in diverse areas. One of the tests consisted of a one-minute-and-three-second flight around a small figure, while the reconstruction was performed in real time. The reference Meshroom software completed the 3D reconstruction in 136 min and 12 s, while the proposed system finished the process in just 1 min and 13 s. This work contributes to the advancement in the field of 3D reconstruction using drones, benefiting from advancements in technology and machine learning algorithms.