The dose regimen of a drug gives important information about the dose sizes, dose frequency and the duration of treatment. Optimisation of dose regimens is critical to ensure therapeutic success of the drug and to minimise its possible adverse effects. The central theme of this thesis is the Efficient Dosing (ED) algorithm - a computation algorithm developed by us for optimisation of dose regimens. In this thesis, we have attempted to develop a quantitative framework for measuring the efficiency of a dose regimen for specified criteria and computing the most efficient dose regimen using the ED algorithm. The criteria considered by us seek to prevent over- and under-exposure to the drug. For example, one of the criteria is to maintain the drug's concentration around a desired target level. Another criterion is to maintain the concentration within a therapeutic range or window. The ED algorithm and its various extensions are programmed in MATLAB R . Some distinguishing features of our methods are: mathematical explicitness in the optimisation process for a general objective function, creation of a theoretical base to draw comparisons among competing dose regimens, adaptability to any drug for which the PK model is known, and other computational features. We develop the algorithm further to compute the optimal ratio of two partner drugs in a fixed dose combination unit and the efficient dose regimens. In clinical trials, the parameters of the PK model followed by the drug are often unknown. We develop a methodology to apply our algorithm in an adaptive setting which enables estimation of the parameters while optimising the dose regimens for the typical subject in each cohort. A potential application of the ED algorithm for individualisation of dose regimens is discussed. We also discuss an application for computation of efficient dose regimens for obliteration of a pre-specified viral load.

This work deals with indexing, difeerent types of indexing structures their advantages and disadvantages. It provides the basis for a search engine with support of morphology or difeerent file formats. Provides insight to the basic ideas, which answer is aim of the master's thesis.

We implement techniques of small area estimation (SAE) to study consumption, a welfare indicator, which is used to assess poverty in the 2003-2004 Nepal Living Standards Survey (NLSS-II) and the 2001 census. NLSS-II has detailed information of consumption, but it can give estimates only at stratum level or higher. While population variables are available for all households in the census, they do not include the information on consumption; the survey has the `population' variables nonetheless. We combine these two sets of data to provide estimates of poverty indicators (incidence, gap and severity) for small areas (wards, village development committees and districts). Consumption is the aggregate of all food and all non-food items consumed. In the welfare survey the responders are asked to recall all information about consumptions throughout the reference year. Therefore, such data are likely to be noisy, possibly due to response errors or recalling errors. The consumption variable is continuous and positively skewed, so a statistician might use a logarithmic transformation, which can reduce skewness and help meet the normality assumption required for model building. However, it could be problematic since back transformation may produce inaccurate estimates and there are difficulties in interpretations. Without using the logarithmic transformation, we develop hierarchical Bayesian models to link the survey to the census. In our models for consumption, we incorporate the `population' variables as covariates. First, we assume that consumption is noiseless, and it is modeled using three scenarios: the exponential distribution, the gamma distribution and the generalized gamma distribution. Second, we assume that consumption is noisy, and we fit the generalized beta distribution of the second kind (GB2) to consumption. We consider three more scenarios of GB2: a mixture of exponential and gamma distributions, a mixture of two gamma distributions, and a mixture of two generalized gamma distributions. We note that there are difficulties in fitting the models for noisy responses because these models have non-identifiable parameters. For each scenario, after fitting two hierarchical Bayesian models (with and without area effects), we show how to select the most plausible model and we perform a Bayesian data analysis on Nepal's poverty data. We show how to predict the poverty indicators for all wards, village development committees and districts of Nepal (a big data problem) by combining the survey data with the census. This is a computationally intensive problem because Nepal has about four million households with about four thousand households in the survey and there is no record linkage between households in the survey and the census. Finally, we perform empirical studies to assess the quality of our survey-census procedure.

La prolifération des capteurs d'images dans de nombreux appareils électroniques, et l'évolution des capacités de traitements à proximité de ces capteurs ouvrent un champ d'exploration pour l'implantation et l'optimisation d'algorithmes complexes de traitement d'images afin de proposer des systèmes de vision artificielle embarquée. Ces travaux s'inscrivent dans la problématique dite d'adéquation algorithme-architecture (A3). Ils portent sur une classe d'algorithmes appelée réseau de neurones à convolutions (ConvNet) et ses applications en analyse de visages embarquée. La chaîne d'analyse de visages, introduite par Garcia et al., a été choisie d'une part pour ses performances en taux de détection/reconnaissance au niveau de l'état de l'art, et d'autre part pour son caractère homogène reposant sur des ConvNets. La première contribution de ces travaux porte sur une étude d'adéquation de cette chaîne d'analyse de visages aux processeurs embarqués. Nous proposons plusieurs adaptations algorithmiques des ConvNets, et montrons que celles-ci permettent d'obtenir des facteurs d'accélération importants (jusqu'à 700) sur un processeur embarqué pour mobile, sans dégradation des performances en taux de détection/reconnaissance. Nous présentons ensuite une étude des capacités de parallélisation des ConvNets, au travers des travaux de thèse de N. Farrugia. Une exploration "gros-grain" du parallélisme des ConvNets, suivie d'une étude de l'ordonnancement interne des processeurs élémentaires, conduisent à une architecture parallèle paramétrable, capable de détecter des visages à plus de 10 images VGA par seconde sur FPGA. Nous proposons enfin une extension de ces études à la phase d'apprentissage de ces réseaux de neurones. Nous étudions des restrictions de l'espace des hypothèses d'apprentissage, et montrons, sur un cas d'application, que les capacités d'apprentissage des ConvNets ne sont pas dégradées, et que le temps d'apprentissage peut être réduit jusqu'à un facteur cinq. / Proliferation of image sensors in many electronic devices, and increasing processing capabilities of such sensors, open a field of exploration for the implementation and optimization of complex image processing algorithms in order to provide embedded vision systems. This work is a contribution in the research domain of algorithm-architecture matching. It focuses on a class of algorithms called convolution neural network (ConvNet) and its applications in embedded facial analysis. The facial analysis framework, introduced by Garcia et al., was chosen for its state of the art performances in detection/recognition, and also for its homogeneity based on ConvNets. The first contribution of this work deals with an adequacy study of this facial analysis framework with embedded processors. We propose several algorithmic adaptations of ConvNets, and show that they can lead to significant speedup factors (up to 700) on an embedded processor for mobile phone, without performance degradation. We then present a study of ConvNets parallelization capabilities, through N. Farrugia's PhD work. A coarse-grain parallelism exploration of ConvNets, followed by study of internal scheduling of elementary processors, lead to a parameterized parallel architecture on FPGA, able to detect faces at more than 10 VGA frames per second. Finally, we propose an extension of these studies to the learning phase of neural networks. We analyze several hypothesis space restrictions for ConvNets, and show, on a case study, that classification rate performances are almost the same with a training time divided by up to five.

The Master Thesis deals with logistic module innovation of information system ERP. The principle of innovation is based on implementation of heuristic algorithms which solve Travel Salesman Problems (TSP). The software MATLAB is used for analysis and tests of these algorithms. The goal of Master Thesis is the comparison of selections algorithm, which are suitable for economic purposes (accuracy of solution, speed of calculation and memory demands).