In this paper we address the problem of Weighted Sum Rate (WSR) maximization for a K-user Multiple-Input Single-Output (MISO) cognitive Interference Channel (IFC) with linear transmit beamforming (BF) vectors in an underlay cognitive radio setting. We consider a set of L single-antenna Primary receivers to which the cognitive system can causes a limited amount of interference. We thus propose an iterative algorithm to determine the BF vectors for the secondary transmission. The optimization of the Lagrange multipliers involved in the optimization problem is based on the subgradient method. The expression of the BF vector can be interpreted as dual Uplink (UL) MMSE receiver that takes into account the interference caused by a fictitious link between the primary user and secondary base station. Finally Deterministic Annealing is applied to make the convergence of the algorithm easier.

Algorithms are presented which allow us to perform morphological transformations on unorganised sets of points represented by their Delaunay triangulation. The results show that these algorithms could behave as morphological operators such as erosion, dilatation, and opening do. They actually act as "shape filters". These algorithms are applied to the problem of scene reconstruction by stereoscopy in which objects are represented by unstructured clouds of 3D points.

Presents a method that estimates robot displacements in outdoor unstructured terrain. It computes the displacements on the basis of associations of 3D points sets produced by consecutive stereovision frames, the associations being determined by tracking pixels from one image frame to the other. The paper details the various steps of the algorithms, and first experimental results are presented: they show that the algorithm is able to estimate the 6 parameters of the robot position with a relative error smaller than about 5%, processing several hundreds of images over several tens of meters.

This paper presents the results of the simulation work concerning the development of a Module for Navigation which is Autonomous and Intelligent (MONAI), made at Aerospatiale. Entirely based on vision channels, this system allows to achieve efficiently the navigation and guidance function of a rover, with a very favourable cost/effectiveness ratio. It is first dealt with the general principle of the navigation/guidance architecture, then with the detailed functions regarding landmark update, obstacle detection and motion assessment, with the first results from the simulations run.