In this work, a new intelligent control strategy based on neural networks is proposed to cope with some external disturbances that can affect quadrotor unmanned aerial vehicles dynamics. Specifically, the variation of the system mass during logistic tasks and the influence of the wind are considered. An adaptive neuromass estimator and an adaptive neural disturbance estimator complement the action of a set of PID controllers, stabilizing the UAV and improving the system performance. The control strategy has been extensively tested with (...) different trajectories: linear, helical, circular, and even a lemniscate one. During the experiments, the mass of the UAV is triplicated and winds of 6 and 9 in Beaufort’s scale are introduced. Simulation results show how the online learning of the estimator increases the robustness of the controller, reducing the effects of the changes in the mass and of the wind on the quadrotor. (shrink)

This paper develops computational models to monitor patients with hip replacement surgery. The Kinect camera is used to capture the movements of patients who are performing rehabilitation exercises with both lower limbs, specifically, ‘side step’ and ‘knee lift’ with each leg. The information is measured at 25 body points with their respective coordinates. Features selection algorithms are applied to the 75 attributes of the initial and final position vector of each rehab exercise. Different classification techniques have been tested and Bayesian (...) networks, supervised classifier system and genetic algorithm with neural network have been selected and jointly applied to identify the correct and incorrect movements during the execution of the rehabilitation exercises. Besides, prediction models of the evolution of a patient are developed based on the average values of some motion related variables. These models can help to fasten the recovery of these patients. (shrink)

Automated Guided Vehicles (AGV) are an essential element of transport in industry 4.0. Although they may seem simple systems in terms of their kinemat.

Traditional literature considers complex biometric sources such as words, letters and signatures for writer verification/identification. In this work the use of small segments of the handwritten stroke for writer verification is proposed. A grapheme is defined as the concatenation of smaller segments or fragments. Two models of grapheme are developed based on the idea that the segments are parts of a circle with or without direction. The average of Gray Level of the Perpendicular Line to the Skeleton and Local Binary (...) Pattern are adopted as descriptors. A database of 3,000 images of 50 writers, with 6 types of segments and with 10 samples per segment has been developed. A binary output support vector machine was applied as classifier. Thus, 50 classifiers were trained using 100 balanced data sets generated using subsampling of the majority class. Experiments are carried out with the proposed models, with an identity verification hit rate of 97 % on average. The positive results of experiments show that it is possible to achieve high precision in writer verification using segment of graphemes. (shrink)