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La Universidad de Gerona (The University of Girona)

La Universidad de Gerona, en catalán: Universitat de Girona, se originó en lo que se llama Estudi General y se fundó en 1446, gracias a que Alfonso IV de Aragón concedió la otorgación de títulos de Gramática, Retórica, Filosofía, Teología, Derecho y Medicina. Sin embargo, ésta fue clausurada y trasladada a Cervera el año 1717 por orden expresa de Felipe V.
The University of Girona (Catalan: Universitat de Girona) is located in the city of Girona, Catalonia, Spain. was founded in 1991, and as of 2009 consists of several campus and buildings across Girona: Barri Vell, Emili Grahit, Campus de Montilivi, Parc Científic i Tecnològic and Campus del Mercadal. A sixth one, Campus de Ciències de la Salut, is being built as of now.

More information: http://en.wikipedia.org/wiki/University_of_Girona

Range Searching in 2D

Range Searching in 2D

Main goals of the lecture: to understand and to be able to analyze the kd-trees and the range trees; to see how data structures can be used to trade the space used for the running time of queries
Nearest Neighbor Search

Nearest Neighbor Search

Problem: what's the nearest restaurant to my hotel?

Closest Pair Given a set S = {p1, p2, ..., pn} of n points in the plane find the two points of S whose distance is the smallest.
PLA DE PENSIONS DE LA GENERALITAT DE CATALUNYA

PLA DE PENSIONS DE LA GENERALITAT DE CATALUNYA

Productes amb condicions preferents pels partícips i beneficiaris del pla
The 13th Annual The Nuts and Bolts of Business Plans MIT Course 15.975 January 2002 Joe Hadzima Senior Lecturer, MIT Sloan School Managing Director, Main Street Partners LLC jgh@alum.mit.edu Joost Bonsen Yonald Chery Former Lead Organizer, MIT $50K Competition $50K Finalist, Founder Virtual Ink jpbonsen@alum.mit.edu yonald@newburynetworks.com TA: Timo Somervuo (somervuo@mit.edu) entrepreneurship.mit.edu/15975.html

The 13th Annual The Nuts and Bolts of Business Plans MIT Course 15.975 January 2002 Joe Hadzima Senior Lecturer, MIT Sloan School Managing Director, Main Street Partners LLC jgh@alum.mit.edu Joost Bonsen Yonald Chery Former Lead Organizer, MIT $50K Competition $50K Finalist, Founder Virtual Ink jpbonsen@alum.mit.edu yonald@newburynetworks.com TA: Timo Somervuo (somervuo@mit.edu) entrepreneurship.mit.edu/15975.html
LSIIT (UMR 7005) Laboratory of Computer Sciences, Image and Remote Sensing Director : Fabrice HEITZ Vice Director : J.-M. DISCHLER

LSIIT (UMR 7005) Laboratory of Computer Sciences, Image and Remote Sensing Director : Fabrice HEITZ Vice Director : J.-M. DISCHLER

Strasbourg 150 people 75 faculty members since 1994
UMR 5505 CNRS-INP-UPS-UT1

UMR 5505 CNRS-INP-UPS-UT1

Pr Jean-Pierre JESSEL jessel@irit.fr http://www.irit.fr/
An INRIA Project-team in partnership with four other institutions Stéphane Donikian IRISA/INRIA France donikian@irisa.fr

An INRIA Project-team in partnership with four other institutions Stéphane Donikian IRISA/INRIA France donikian@irisa.fr

Institut des Sciences du Mouvement
Voronoi Diagrams

Voronoi Diagrams

Planar straight line graph A planar straight line graph (PSLG) is a planar embedding of a planar graph G = (V, E) with: 1. each vertex v  V mapped to a distinct point in the plane, 2. and each edge e  E mapped to a segment between the points for the endpoint vertices of the edge such that no two segments intersect, except at their endpoints. edge (14) vertex (10) face (6) Observe that PSLG is defined by mapping a mathematical object (planar graph) to a geometric object (PSLG). That mapping introduces the notion of coordinates or location, which was not present in the graph (despite its planarity). We will see later that PSLGs will be useful objects. For now we focus on a data structure to represent a PSLG.

Fast Computation of Generalized Voronoi Diagrams Using Graphics Hardware Kenneth E. Hoff III, Tim Culver, John Keyser, Ming Lin, and Dinesh Manocha University of North Carolina at Chapel Hill SIGGRAPH ‘99 I will present a fast and simple way to compute generalized Voronoi diagrams using standard graphics hardware. This work was done in conjunction with Tim Culver, John Keyser, Ming Lin, and Dinesh Manocha at the University of North Carolina at Chapel Hill.
Encuentro Hispano-Francés de Realidad Virtual Laboratorio Decoroso Crespo (LDC) Universidad Politécnica de Madrid (UPM) Angélica de Antonio

Encuentro Hispano-Francés de Realidad Virtual Laboratorio Decoroso Crespo (LDC) Universidad Politécnica de Madrid (UPM) Angélica de Antonio
Incremental Linear Programming

Incremental Linear Programming

Maximize C1X1 + C2X2 + … + CdXd Subject to A1,1X1 + … + A1,dXd ≤ b1 A2,1X1 + … + A2,dXd ≤ b2 … An,1X1 + … + An,dXd ≤ bn Linear programming involves finding a solution to the constraints, one that maximizes the given linear function of variables. D = number of variables or dimensions. Objective function is the function to be maximized. Linear program is the set of constraints together with the objective function. Feasible region is the intersection of the half-spaces, which is the set of points that satisfy all the constraints. Feasible region can be bounded, unbounded, empty. If empty problem is infeasible
An Introduction to Unstructured Mesh Generation Material tret de: S. J. Owen, "A Survey of Unstructured Mesh Generation Technology", Proceedings 7th International Meshing Roundtable, 1998.

An Introduction to Unstructured Mesh Generation Material tret de: S. J. Owen, "A Survey of Unstructured Mesh Generation Technology", Proceedings 7th International Meshing Roundtable, 1998.

This course is designed to be an overview of some of the fundamental algorithms and processes used in finite element mesh generation
Intersections

Intersections

PROGRAMMING IN HASKELL Chapter 1 - Introduction

PROGRAMMING IN HASKELL Chapter 7 - Higher-Order Functions
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