Positionnement dynamique | EPFL Graph Search

thumb|alt=alternative de l'image à compléter|Navire de soutien Offshore Toisa Perseus avec en arrière-plan la cinquième génération de navire de forage en eau très profonde Discoverer Enterprise Le positionnement dynamique (DP) est un système contrôlé par ordinateur qui permet à un navire de maintenir sa position en utilisant ses propres moyens de propulsion. Le système DP est constitué de trois parties. Les capteurs recueillent les informations, la console informatique effectue les calculs et sert d'interface pour l'opérateur, et les actionneurs agissent sur consigne. C'est un système automatique informatisé. Les forces extérieures telles que celles dues au vent et à l'état de la mer sont mesurées par des anémomètres et des accéléromètres (centrales inertielles). Les capteurs de position comprennent des GPS différentiels, des câbles d'aplomb, ainsi que des balises acoustiques que l'on peut larguer sur le fond ou fixer sur des mobiles de référence. Le compas gyroscopique fournit l'indication de cap. Elle est constituée d'un ordinateur et d'une console incluant les accessoires usuels tels que souris et clavier, ainsi que d'un joystick et de boutons sécurisés dédiés à la manipulation directe du système. Le logiciel DP contient un modèle mathématique qui inclut les informations relatives au comportement du navire soumis à des forces extérieures telles que le vent ou le courant, ainsi que l'emplacement des moyens de propulsion. Selon la tâche du navire l'opérateur choisit le mode DP adéquat (voir plus loin) et sélectionne les capteurs. Il définit ensuite les paramètres d'évolution du navire telles que la vitesse maximum, la courbe d'accélération/décélération, la vitesse de changement de cap (rotation) et le centre de rotation du navire, l'écart toléré, la puissance maximum accordée aux propulseurs. L'ordinateur calcule ensuite l'angle de braquage nécessaire des propulseurs et la puissance à utiliser.

Graph Signal Processing

Ntorina Thanou

Over the past few decades we have been experiencing an explosion of information generated by large networks of sensors and other data sources. Much of this data is intrinsically structured, such as traffic evolution in a transportation network, temperature values in different geographical locations, information diffusion in social networks, functional activities in the brain, or 3D meshes in computer graphics. The representation, analysis, and compression of such data is a challenging task and requires the development of new tools that can identify and properly exploit the data structure. In this thesis, we formulate the processing and analysis of structured data using the emerging framework of graph signal processing. Graphs are generic data representation forms, suitable for modeling the geometric structure of signals that live on topologically complicated domains. The vertices of the graph represent the discrete data domain, and the edge weights capture the pairwise relationships between the vertices. A graph signal is then defined as a function that assigns a real value to each vertex. Graph signal processing is a useful framework for handling efficiently such data as it takes into consideration both the signal and the graph structure. In this work, we develop new methods and study several important problems related to the representation and structure-aware processing of graph signals in both centralized and distributed settings. We focus in particular in the theory of sparse graph signal representation and its applications and we bring some insights towards better understanding the interplay between graphs and signals on graphs. First, we study a novel yet natural application of the graph signal processing framework for the representation of 3D point cloud sequences. We exploit graph-based transform signal representations for addressing the challenging problem of compression of data that is characterized by dynamic 3D positions and color attributes. Next, we depart from graph-based transform signal representations to design new overcomplete representations, or dictionaries, which are adapted to specific classes of graph signals. In particular, we address the problem of sparse representation of graph signals residing on weighted graphs by learning graph structured dictionaries that incorporate the intrinsic geometric structure of the irregular data domain and are adapted to the characteristics of the signals. Then, we move to the efficient processing of graph signals in distributed scenarios, such as sensor or camera networks, which brings important constraints in terms of communication and computation in realistic settings. In particular, we study the effect of quantization in the distributed processing of graph signals that are represented by graph spectral dictionaries and we show that the impact of the quantization depends on the graph geometry and on the structure of the spectral dictionaries. Finally, we focus on a widely used graph process, the problem of distributed average consensus in a sensor network where sensors exchange quantized information with their neighbors. We propose a novel quantization scheme that depends on the graph topology and exploits the increasing correlation between the values exchanged by the sensors throughout the iterations of the consensus algorithm.

EPFL2016

Graph Signal Processing

Ntorina Thanou

EPFL2016