Simulation methods are used when closed-form solutions do not exist. An interesting simulation method that has been widely used in many scientific fields is the Monte Carlo method. Not only it is a simple technique that enables to estimate the quantity of interest, but it can also provide relevant information about the value to be estimated through its confidence interval. However, the use of classical Monte Carlo method is not a reasonable choice when dealing with rare event probabilities. In fact, very small probabilities require a huge number of simulation runs, and thus, the computational time of the simulation increases significantly. This observation lies behind the main motivation of the present work. In this thesis, we propose efficient importance sampling estimators to evaluate rare events probabilities. In the first part of the thesis, we consider a variety of turbulence regimes, and we study the outage probability of free-space optics communication systems under a generalized pointing error model with both a nonzero boresight component and different horizontal and vertical jitter effects. More specifically, we use an importance sampling approach,based on the exponential twisting technique to offer fast and accurate results. We also show that our approach extends to the multihop scenario. In the second part of the thesis, we are interested in assessing the outage probability achieved by some diversity techniques over generalized fading channels. In many circumstances, this is related to the difficult question of analyzing the statistics of the sum of random variables. More specifically, we propose robust importance sampling schemes that efficiently evaluate the outage probability of diversity receivers over Gamma-Gamma, α − µ, κ − µ, and η − µ fading channels. The proposed estimators satisfy the well-known bounded relative error criterion for both maximum ratio combining and equal gain combining cases. We show the accuracy and the efficiency of our approach compared to naive Monte Carlo via some selected numerical simulations in both case studies. In the last part of this thesis, we propose efficient importance sampling estimators for the left tail of positive Gaussian quadratic forms in both real and complex settings. We show that these estimators possess the bounded relative error property. These estimators are then used to estimate the outage probability of maximum ratio combining diversity receivers over correlated Nakagami-m or correlated Rician fading channels

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Diagonally dominant matrices arise in many applications. In this work, we exploit the structure of diagonally dominant matrices to provide sharp entrywise relative perturbation bounds. We first generalize the results of Dopico and Koev to provide relative perturbation bounds for the LDU factorization with a well conditioned L factor. We then establish relative perturbation bounds for the inverse that are entrywise and independent of the condition number. This allows us to also present relative perturbation bounds for the linear system Ax=b that are independent of the condition number. Lastly, we continue the work of Ye to provide relative perturbation bounds for the eigenvalues of symmetric indefinite matrices and non-symmetric matrices.

In this diploma thesis an algorithm for the evaluation of picture quality of H.264-coded video sequences is introduced and applied. As a measure of picture quality objective metric the peak signal to noise ratio (PSNR) is used. While the computation of the PSNR usually requires a reference signal and compares it to the distorted video sequence, this algorithm is able to evaluate PSNR following the coded transform coefficients. Thus, no reference signal is needed.

Dans cette thèse, nous nous intéressons à développer des outils mathématiques, afin de traiter une gamme des signaux biologiques et économiques. En premier lieu, nous proposons la transformée Dynalet, considérée comme une alternative, pour des signaux de relaxation sans symétrie interne, à la transformée de Fourier et à la transformée ondelette. L'applicabilité de cette nouvelle approximation est illustrée sur des données réelles. Ensuite, nous corrigeons la ligne de base des signaux biologiques spectrométriques, à l'aide d'une régression expectile pénalisée, qui, sur les applications proposées, est plus performante qu'une régression quantile. Puis, afin d'éliminer le bruit blanc, nous adaptons aux signaux spectrométriques une nouvelle approche combinant ondelette, seuillage doux et composants PLS. Pour terminer, comme les signaux peuvent être considérés comme des données fonctionnelles, d'une part, nous développons une vraisemblance locale fonctionnelle dont le but est d'effectuer une classification supervisée des courbes, et, d'autre part, nous estimons l'opérateur de régression pour une réponse scalaire positive non nulle, par minimisation de l'erreur quadratique moyenne relative. De plus, les lois asymptotiques de notre estimateur sont établies et son efficacité est illustrée sur des données simulées et sur des données spectroscopiques et économiques. / In this thesis, we focus on developing mathematical tools to treat a range of biological and economic signals. First, we propose the Dynalet transform for non-symmetrical biological relaxation signals. This transform is considered as an alternative to the Fourier transform and the wavelet transform. The applicability of the new approximation approach is illustrated on real data. Then, for spectrometric biological signals, we correct the baseline using a penalized expectile regression. Thus, the proposed applications show that our proposed regression is more efficient than the quantile regression. Then to remove random noise, we adapt to spectrometric data a new denoising method that combine wavelets, soft thresholding rule and PLS components. Finally, note that the biological signals may be often regarded as functional data. On one hand, we develop a functional local likelihood aiming to perform a supervised classification of curves. On the other hand, we estimate the regression operator with positive responses, by minimizing the mean squared relative error. Moreover, The asymptotic distributions of our estimator are established and their efficiency is illustrated on a simulation study and on a spectroscopic and economic data set.

This work deals with evaluation of equipment used for non-contact measurement of distance between vehicles. First are discussed various principles of measuring and after the selection of the most appropriate solution is proposed a detailed block diagram of modulation, demodulation and evaluation of the whole system and its mathematical description. Another part is devoted to describing the activities of the microprocessor controller, with which is controlled evaluation part and to the control program. It also analyzes the maximal attainable accuracy of measurement and the errors that affect it. The last part includes a circuit design and simulation of selected functional block, which is a coincidence /quadrature/ detector and construction documents for evaluation and demodulation part of the measurement instrument.