Robotic control systems based on bioinspired multi-agent systems: application of the principles of neuroscience to robotics

Robotics is a field that presents a large number of problems because it depends on a large number of disciplines, devices, technologies and tasks. Its expansion from perfectly controlled industrial environments toward open and dynamic environment presents a many new challenges, such as robots household robots or professional robots. To facilitate the rapid development of robotic systems, low cost, reusability of code, its medium and long term maintainability and robustness are required novel approaches to provide generic models and software systems who develop paradigms capable of solving these problems. For this purpose, in this paper we propose a model based on multi-agent systems inspired by the human nervous system able to transfer the control characteristics of the biological system and able to take advantage of the best properties of distributed software systems.

GPGPU implementation of growing neural gas: application to 3D scene reconstruction

Self-organising neural models have the ability to provide a good representation of the input space. In particular the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time-consuming, especially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This paper proposes a Graphics Processing Unit (GPU) parallel implementation of the GNG with Compute Unified Device Architecture (CUDA). In contrast to existing algorithms, the proposed GPU implementation allows the acceleration of the learning process keeping a good quality of representation. Comparative experiments using iterative, parallel and hybrid implementations are carried out to demonstrate the effectiveness of CUDA implementation. The results show that GNG learning with the proposed implementation achieves a speed-up of 6× compared with the single-threaded CPU implementation. GPU implementation has also been applied to a real application with time constraints: acceleration of 3D scene reconstruction for egomotion, in order to validate the proposal.

Nanoarchitectures Based on Layered Titanosilicates Supported on Glass Fibers: Application to Hydrogen Storage

This work reports on the synthesis of nanosheets of layered titanosilicate JDF-L1 supported on commercial E-type glass fibers with the aim of developing novel nanoarchitectures useful as robust and easy to handle hydrogen adsorbents. The preparation of those materials is carried out by hydrothermal reaction from the corresponding gel precursor in the presence of the glass support. Because of the basic character of the synthesis media, silica from the silicate-based glass fibers can be involved in the reaction, cementing its associated titanosilicate and giving rise to strong linkages on the support with the result of very stable heterostructures. The nanoarchitectures built up by this approach promote the growth and disposition of the titanosilicate nanosheets as a house-of-cards radially distributed around the fiber axis. Such an open arrangement represents suitable geometry for potential uses in adsorption and catalytic applications where the active surface has to be available. The content of the titanosilicate crystalline phase in the system represents about 12 wt %, and this percentage of the adsorbent fraction can achieve, at 298 K and 20 MPa, 0.14 wt % hydrogen adsorption with respect to the total mass of the system. Following postsynthesis treatments, small amounts of Pd (<0.1 wt %) have been incorporated into the resulting nanoarchitectures in order to improve their hydrogen adsorption capacity. In this way, Pd-layered titanosilicate supported on glass fibers has been tested as a hydrogen adsorbent at diverse pressures and temperatures, giving rise to values around 0.46 wt % at 298 K and 20 MPa. A mechanism of hydrogen spillover involving the titanosilicate framework and the Pd nanoparticules has been proposed to explain the high increase in the hydrogen uptake capacity after the incorporation of Pd into the nanoarchitecture.

Estimation of the Proportion of Underachieving Students in Compulsory Secondary Education in Spain: An Application of the Rasch Model

There are very few studies in Spain that treat underachievement rigorously, and those that do are typically related to gifted students. The present study examined the proportion of underachieving students using the Rasch measurement model. A sample of 643 first-year high school students (mean age = 12.09; SD = 0.47) from 8 schools in the province of Alicante (Spain) completed the Battery of Differential and General Skills (Badyg), and these students' General Points Average (GPAs) were recovered by teachers. Dichotomous and Partial credit Rasch models were performed. After adjusting the measurement instruments, the individual underachievement index provided a total sample of 181 underachieving students, or 28.14% of the total sample across the ability levels. This study confirms that the Rasch measurement model can accurately estimate the construct validity of both the intelligence test and the academic grades for the calculation of underachieving students. Furthermore, the present study constitutes a pioneer framework for the estimation of the prevalence of underachievement in Spain.