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Principal Components Analysis (PCA) and Canonical Correlation Analysis (CCA) are among the methods used in Multivariate Data Analysis. PCA is concerned with explaining the variance-covariance structure of a set of variables through a few linear combinations of these variables. Its general objectives are data reduction and interpretation. CCA seeks to identify and quantify the associations between two sets of variables i.e Pulp fibres and Paper variables.PCA shows that the first PC already exceeds 90% of the total variability. According to the proportion of variability explained by each canonical variable , the results suggest that the first two canonical correlations seem to be sufficient to explain the structure between
Pulp and Paper characteristics with 98.86%. Despite the fact that the first the two canonical
variables keep 98% of common variability, 78% was kept in the pulp fiber set and about
94% of the paper set as a whole. In the proportion of opposite canonical variable,there were
approximately 64% for the paper set of variables and 78% for the pulp fiber set of variables
kept for the two respectively.

Il s'agit d'un gabarit simple destiner à des étudiants qui n'ont jamais utilisé LaTeX. J'ai inclus plusieurs commentaires afin de donner quelques explications quant aux commandes utilisées.

A LaTeX class implementing the new (as of 2015) NIH Biographical Sketch Format. The original template can be found at the author's GitHub page.
This LaTeX document class tries to adhere to the Biographical Sketch formatting requirements outlined in NIH Notice NOT-OD-15-032. This new format is required for applications submitted for due dates on or after May 25, 2015.
I tried to mimic the example documents provided on the SF 424 (R&R) Forms and Applications page as closely as possible. I intend to use this class for my own upcoming grant submissions; however I offer no guarantee of conformity to NIH requirements.

The lecture notes are based on Tom Coates' lecture on toric varieties. A few references:
M. Audin, Toric actions on symplectic manifolds
W. Fulton, Toric varieties
Cox-Schenck-Little, Toric varieties

The goal of this project is to explore both the theory behind the Extended Kalman Filter and the way it was used to localize a four-wheeled mobile-robot. This can be achieved by estimating in real-time the pose of the robot, while using a pre-acquired map through Laser Range Finder (LRF). The LRF is used to scan the environment, which is represented through line segments. Through a prediction step, the robot simulates its kinematic model to predict his current position. In order to minimize the difference between the matched lines from the global and local maps, a update step is implemented. It should be noted that every measurement has associated uncertainty that needs to be taken into account when performing each step of the Extended Kalman Filter. These uncertainties, or noise, are described by covariance matrices that play a very important role in the algorithm. Since we are dealing with an indoor structured environment, mainly composed by walls and straight-edged objects, the line segment representation of the maps was the chosen method to approach the problem.

João Rosa

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