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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
41

The Pattern of Distribution of Refractive Error among Primary School Children of Malamulele Community, Vhembe District, Limpopo Province

Baloyi, Voster Hlawulani Austine 05 1900 (has links)
MPH / Department of Public Health / See the attached abstract below
42

Theoretical and Numerical Analysis of Super-Resolution Without Grid / Analyse numérique et théorique de la super-résolution sans grille

Denoyelle, Quentin 09 July 2018 (has links)
Cette thèse porte sur l'utilisation du BLASSO, un problème d'optimisation convexe en dimension infinie généralisant le LASSO aux mesures, pour la super-résolution de sources ponctuelles. Nous montrons d'abord que la stabilité du support des solutions, pour N sources se regroupant, est contrôlée par un objet appelé pré-certificat aux 2N-1 dérivées nulles. Quand ce pré-certificat est non dégénéré, dans un régime de petit bruit dont la taille est contrôlée par la distance minimale séparant les sources, le BLASSO reconstruit exactement le support de la mesure initiale. Nous proposons ensuite l'algorithme Sliding Frank-Wolfe, une variante de l'algorithme de Frank-Wolfe avec déplacement continu des amplitudes et des positions, qui résout le BLASSO. Sous de faibles hypothèses, cet algorithme converge en un nombre fini d'itérations. Nous utilisons cet algorithme pour un problème 3D de microscopie par fluorescence en comparant trois modèles construits à partir des techniques PALM/STORM. / This thesis studies the noisy sparse spikes super-resolution problem for positive measures using the BLASSO, an infinite dimensional convex optimization problem generalizing the LASSO to measures. First, we show that the support stability of the BLASSO for N clustered spikes is governed by an object called the (2N-1)-vanishing derivatives pre-certificate. When it is non-degenerate, solving the BLASSO leads to exact support recovery of the initial measure, in a low noise regime whose size is controlled by the minimal separation distance of the spikes. In a second part, we propose the Sliding Frank-Wolfe algorithm, based on the Frank-Wolfe algorithm with an added step moving continuously the amplitudes and positions of the spikes, that solves the BLASSO. We show that, under mild assumptions, it converges in a finite number of iterations. We apply this algorithm to the 3D fluorescent microscopy problem by comparing three models based on the PALM/STORM technics.

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