The lifting methodology can be used for simplifying the closed-loop stability study of a system in which the measurement data are acquired at different frequencies. This is because the lifting operators allow treating this kind of systems as if there is only one control frequency, which reduces the complexity of the problem in a very important way. The four-rotor microhelicopter designed by the authors, whose control is faced, is a nonlinear plant with sensors operating at two different frequencies. First, the system is linearised cancelling the rotors dynamics. Then, the authors implement a model reference adaptive control structure for identifying the gains of the linear reference plant and performing the adaptive control of the microhelicopter. At this point, the system stability study is carried out by just computing the eigenvalues of the closed-loop states matrix thanks to the lifting operators.

This paper proposes a novel method for the calibration of a stereo camera system used to reconstruct 3D scenes. An error in the pitch angle of the cameras causes the reconstructed scene to exhibit some distortion with respect to the real scene. To do the calibration procedure, whose purpose is to eliminate or at least minimize said distortion, machine learning techniques have been used, and more specifically, regression algorithms. These algorithms are trained with a large number of vectors of input features with their respective outputs, since, in view of the application of the procedure proposed, it is important that the training set be sufficiently representative of the variety that can occur in a real scene, which includes the different orientations that the pitch angle can take, the error in said angle and the effect that all this has on the reconstruction process. The most efficient regression algorithms for estimating the error in the pitch angle are derived from decision trees and certain neural network configurations. Once estimated, the error can be corrected, thus making the reconstructed scene appear more like the real one. Although the authors base their method on U-V disparity and employ this same technique to completely reconstruct the 3D scene, one of the most interesting features of the method proposed is that it can be applied regardless of the technique used to carry out said reconstruction.

This study evaluates the performance and portability of the Weather Research and Forecasting model in high-performance computing environments, comparing traditional baremetal deployments with containerized executions using Singularity. Experiments were conducted on the TeideHPC system, focusing on execution time, the impact of different compilers (Intel vs. GCC), and parallelization strategies (MPI and OpenMP). The results indicate that while Singularity introduces a performance overhead of 11 to 15%, it offers significant advantages in portability and reproducibility. Additionally, the study highlights the importance of compiler choice and the influence of container image size on startup times, emphasizing the need for careful optimization in containerized HPC workflows.

U-V disparity is a technique that is commonly used to detect obstacles in 3D scenes, modeling them as a set of vertical planes. In this paper, the authors describe the general lines of a method based on this technique for fully reconstructing 3D scenes, and conduct an analytical study of its performance and sensitivity to errors in the pitch angle of the stereoscopic vision system. The equations of the planes calculated for a given error in this angle yield the deviation with respect to the ideal planes (with a zero error in the angle) for a large test set consisting of planes with different orientations, which is represented graphically to analyze the method’s qualitative and quantitative performance. The relationship between the deviation of the planes and the error in the pitch angle is observed to be linear. Two major conclusions are drawn from this study: first, that the deviation between the calculated and ideal planes is always less than or equal to the error considered in the pitch angle; and second, that even though in some cases the deviation of the plane is zero or very small, the probability that a plane of the scene deviates from the ideal by the greatest amount possible, which matches the error in the pitch angle, is very high.