Enseñanza de sistemas BIM en el ámbito universitario

Una de las exigencias técnicas más demandadas hoy en día dentro del ámbito edificatorio es el manejo de programas de diseño arquitectónico integrado, al convertirse en una formación indispensable frente al diseño tradicional. De este modo, la utilización de tecnologías BIM (Building Information Modeling) en el ámbito proyectual está suponiendo un impulso profesional cualitativo muy importante mediante la utilización de bases de datos específicas asociadas a dibujos convencionales desde distintas perspectivas y a todos los niveles. El objeto del presente estudio es la aplicación constructiva de esta herramienta en el ámbito docente de la Universidad de Alicante, suponiendo una oportunidad para implementar el estudio de nuevas tecnologías y conocer una interesante herramienta de trabajo implantada actualmente en muchas empresas de arquitectura y construcción. La metodología y los contenidos impartidos en el curso consideran una aplicación práctica de forma que los conocimientos adquiridos sean graduales y de aplicación sucesiva. En conclusión, el curso planteado responde a las crecientes necesidades profesionales en el ámbito constructivo con herramientas BIM y enriquece las habilidades de los estudiantes, mejorando su pericia en el ámbito del diseño y ampliando su capacidad de visión espacial; ambas cualidades indispensables en la práctica profesional arquitectónica.

3D model reconstruction using neural gas accelerated on GPU

In this work, we propose the use of the neural gas (NG), a neural network that uses an unsupervised Competitive Hebbian Learning (CHL) rule, to develop a reverse engineering process. This is a simple and accurate method to reconstruct objects from point clouds obtained from multiple overlapping views using low-cost sensors. In contrast to other methods that may need several stages that include downsampling, noise filtering and many other tasks, the NG automatically obtains the 3D model of the scanned objects. To demonstrate the validity of our proposal we tested our method with several models and performed a study of the neural network parameterization computing the quality of representation and also comparing results with other neural methods like growing neural gas and Kohonen maps or classical methods like Voxel Grid. We also reconstructed models acquired by low cost sensors that can be used in virtual and augmented reality environments for redesign or manipulation purposes. Since the NG algorithm has a strong computational cost we propose its acceleration. We have redesigned and implemented the NG learning algorithm to fit it onto Graphics Processing Units using CUDA. A speed-up of 180× faster is obtained compared to the sequential CPU version.

Management of social networks in the educational process

The research developed in this work consists in proposing a set of techniques for management of social networks and their integration into the educational process. The proposals made are based on assumptions that have been proven with simple examples in a real scenario of university teaching. The results show that social networks have more capacity to spread information than educational web platforms. Moreover, educational social networks are developed in a context of freedom of expression intrinsically linked to Internet freedom. In that context, users can write opinions or comments which are not liked by the staff of schools. However, this feature can be exploited to enrich the educational process and improve the quality of their achievement. The network has covered needs and created new ones. So, the figure of the Community Manager is proposed as agent in educational context for monitoring network and aims to channel the opinions and to provide a rapid response to an academic problem.

3DCOMET: 3D compression methods test dataset

The use of 3D data in mobile robotics applications provides valuable information about the robot’s environment. However usually the huge amount of 3D information is difficult to manage due to the fact that the robot storage system and computing capabilities are insufficient. Therefore, a data compression method is necessary to store and process this information while preserving as much information as possible. A few methods have been proposed to compress 3D information. Nevertheless, there does not exist a consistent public benchmark for comparing the results (compression level, distance reconstructed error, etc.) obtained with different methods. In this paper, we propose a dataset composed of a set of 3D point clouds with different structure and texture variability to evaluate the results obtained from 3D data compression methods. We also provide useful tools for comparing compression methods, using as a baseline the results obtained by existing relevant compression methods.