Abstract
In this paper, we present a low-cost versatile device developed to support the navigation of autonomous underwater vehicles. Unlike the usual transponders extensively used for LBL acoustic navigation of a single vehicle, this device allows the navigation of multiple vehicles and the remote tracking of their positions, without any extra acoustic signals being transmitted. The navigation beacon has a radio buoy connected to an underwater reconfigurable multi-frequency transponder. A remote tracking station receives data from the buoy and monitors the position of the vehicles in real time. We describe the mechanical, electronic and software modules involved, as well as the tracking algorithm, and we also present experimental data from an operational mission.

Abstract

Abstract
This paper analyses periodic gaits of multi legged locomotion systems. The joint signals are studied in the Fourier domain, namely, from the point of view of its reproducibility through low-pass actuators, The influence of several parameters is also considered and their critical values are investigated.

Abstract
The design of algorithms that explore multiple representation languages and explore different search spaces has an intuitive appeal. In the context of classification problems, algorithms that generate multivariate trees are able to explore multiple representation languages by using decision tests based on a combination of attributes. The same applies to model trees algorithms, in regression domains, but using linear models at leaf nodes. In this paper we study where to use combinations of attributes in decision tree learning, We present an algorithm for multivariate tree learning that combines a univariate decision tree with a discriminant function by means of constructive induction. This algorithm is able to use decision nodes with multivariate tests, and leaf nodes that predict a class using a discriminant function. Multivariate decision nodes are built when growing the tree, while junctional leaves are built when pruning the tree. Functional trees can be seen as a generalization of multivariate trees. Our algorithm was compared against to its components and two simplified versions using 30 benchmark datasets. The experimental evaluation shows that our algorithm has clear advantages with respect to the generalization ability and model sizes at statistically significant confidence levels.

Abstract

Abstract