Neste trabalho, uma análise quantitativa e qualitativa para a estabilização dinâmica de um pêndulo invertido com uma força externa senoidal aplicada no ponto de suspensão é feita. Inicialmente, a perturbação externa é composta de um único cosseno, então uma generalização é feita, usando uma soma de N cossenos com diferentes amplitudes e frequências. Aproximações são testadas e o tempo durante o qual o pêndulo invertido permanece estável é explorado quando N é grande, a fim de recuperar o padrão do caso onde N = 1. O caso específico de oscilações periódicas e quase periódicas, quando N = 2, é analisado e diagramas de estabilidade considerando diferentes frequências e amplitudes são estudados. Depois, um ruído Gaussiano additivo é adicionado ao sistema para que a degradação dos diagramas de estabilidade gerados por variâncias diferentes possam ser estudados. Todos os pontos deste trabalho são corroborados por simulações, as quais integram numericamente as equações de movimento do sistema através do método de Runge-Kutta de quarta ordem. Os algoritmos e detalhes extras dos métodos de integração usados são explorados numa publicação deste trabalho, a qual está apresentada, nesta dissertação, como um apêndice. / In this work a quantitative and qualitative analysis of the dynamical stabilization of an inverted pendulum with a sinusoidal external perturbation applied at the suspension point is made. Initially, the external perturbation is composed of a single cosine, then a generalization is made using a sum of N cosines with different amplitudes and frequencies. Approximations are tested, and the time for which the inverted pendulum remains stable is explored when N is large, in order to recover the pattern of the case when N = 1. The specific case of periodic and almost periodic oscillations, when N = 2, is analysed and stability diagrams considering different frequencies and amplitudes are studied. Later, an additive Gaussian noise is added to the system so the degradation of the stability diagrams generated by different variances can be studied. All points of this work are corroborated by simulations, which numerically integrate the system’s equation of motion through a fourth order Runge-Kutta method. Algorithms and extra details on the integration methods used are explored in a publication of this work, which is presented in this thesis as an appendix.

Στην παρούσα διπλωματική εργασία επιχειρείται η ανάλυση και η εκτίμηση της συχνότητας απλών ημιτονοειδών σημάτων υπό την παρουσία λευκού Γκαουσιανού θορύβου (AWGN).Η εκτίμηση παραμέτρων απλών ημιτονοειδών σημάτων υπό την παρουσία προσθετικού Γκαουσιανού θορύβου αποτελεί ένα κλασσικό πρόβλημα και σημαντικό αντικείμενο μελέτης εξαιτίας της πληθώρας των εφαρμογών που έχει στην θεωρία ελέγχου, στην επεξεργασία σημάτων, στις ψηφιακές επικοινωνίες, στην βιοϊατρική τεχνολογία κ.α.Η εκτίμηση της συχνότητας είναι συνήθως το θέμα ‘ζωτικής σημασίας’ του προβλήματος για δύο σημαντικούς λόγους. Αφ’ενός οι συχνότητες πρέπει να εκτιμηθούν διότι αποτελούν μη-γραμμικές συναρτήσεις στην ληφθείσα ακολουθία δεδομένων και αφ’ ετέρου έχοντας καθοριστεί οι συχνότητες, οι υπόλοιπες παράμετροι του σήματος όπως είναι το πλάτος και η φάση του, μπορούν να υπολογιστούν άμεσα. Αρχικά γίνεται μία σύντομη εισαγωγή στις βασικές έννοιες πάνω στις οποίες δομείται η εκτίμηση παραμέτρων ενός ημιτονοειδούς σήματος και έπειτα παρουσιάζονται μερικοί αλγόριθμοι εκτίμησης. Πιο συγκεκριμένα παρουσιάζεται η διαδικασία κατασκευής τους και αναλύονται οι επιδόσεις τους. Τέλος παραθέτουμε και προσομοιώσεις μέσω υπολογιστή για κάθε αλγόριθμο ξεχωριστά και συγκρίνουμε την επίδοση του καθενός με τους υπόλοιπους. Από την σύγκριση αυτή γίνεται εξαγωγή χρήσιμων συμπερασμάτων σχετικά με τον προσδιορισμό των παραμέτρων κάθε αλγόριθμου αλλά και με την καταλληλότητα κάθε αλγόριθμου για συγκεκριμένες συνθήκες θορύβου. / In this thesis attempts to analyze and estimate the frequency of single sinusoid signals in Additive White Gaussian Noise (AWGN). Parameter estimation of sinusoids has been a classical problem and it is still an important research topic because of its numerous applications in multiple disciplines such as control theory, signal processing, digital communications, biomedical engineering etc. Estimation of the frequencies is often the crucial step in the problem for two principally reasons. Firstly, frequencies should be estimated because they are nonlinear functions in the received data sequence and secondly, once frequencies have been determined, the remaining parameters, such as amplitude and phase, can then be computed straightforwardly. Primarily we introduce some basic concepts on parameters estimation of sinusoid signals and then several estimation algorithms. More specifically shows the fabrication process of these algorithms and analyze their performance. Finally, we quote computer simulations for each algorithm separately and compare their performance. From these comparisons we can draw conclusions on the determination of parameters for each algorithm and the appropriateness of algorithms for specific noise conditions.

Neste trabalho, uma análise quantitativa e qualitativa para a estabilização dinâmica de um pêndulo invertido com uma força externa senoidal aplicada no ponto de suspensão é feita. Inicialmente, a perturbação externa é composta de um único cosseno, então uma generalização é feita, usando uma soma de N cossenos com diferentes amplitudes e frequências. Aproximações são testadas e o tempo durante o qual o pêndulo invertido permanece estável é explorado quando N é grande, a fim de recuperar o padrão do caso onde N = 1. O caso específico de oscilações periódicas e quase periódicas, quando N = 2, é analisado e diagramas de estabilidade considerando diferentes frequências e amplitudes são estudados. Depois, um ruído Gaussiano additivo é adicionado ao sistema para que a degradação dos diagramas de estabilidade gerados por variâncias diferentes possam ser estudados. Todos os pontos deste trabalho são corroborados por simulações, as quais integram numericamente as equações de movimento do sistema através do método de Runge-Kutta de quarta ordem. Os algoritmos e detalhes extras dos métodos de integração usados são explorados numa publicação deste trabalho, a qual está apresentada, nesta dissertação, como um apêndice. / In this work a quantitative and qualitative analysis of the dynamical stabilization of an inverted pendulum with a sinusoidal external perturbation applied at the suspension point is made. Initially, the external perturbation is composed of a single cosine, then a generalization is made using a sum of N cosines with different amplitudes and frequencies. Approximations are tested, and the time for which the inverted pendulum remains stable is explored when N is large, in order to recover the pattern of the case when N = 1. The specific case of periodic and almost periodic oscillations, when N = 2, is analysed and stability diagrams considering different frequencies and amplitudes are studied. Later, an additive Gaussian noise is added to the system so the degradation of the stability diagrams generated by different variances can be studied. All points of this work are corroborated by simulations, which numerically integrate the system’s equation of motion through a fourth order Runge-Kutta method. Algorithms and extra details on the integration methods used are explored in a publication of this work, which is presented in this thesis as an appendix.

Neste trabalho, uma análise quantitativa e qualitativa para a estabilização dinâmica de um pêndulo invertido com uma força externa senoidal aplicada no ponto de suspensão é feita. Inicialmente, a perturbação externa é composta de um único cosseno, então uma generalização é feita, usando uma soma de N cossenos com diferentes amplitudes e frequências. Aproximações são testadas e o tempo durante o qual o pêndulo invertido permanece estável é explorado quando N é grande, a fim de recuperar o padrão do caso onde N = 1. O caso específico de oscilações periódicas e quase periódicas, quando N = 2, é analisado e diagramas de estabilidade considerando diferentes frequências e amplitudes são estudados. Depois, um ruído Gaussiano additivo é adicionado ao sistema para que a degradação dos diagramas de estabilidade gerados por variâncias diferentes possam ser estudados. Todos os pontos deste trabalho são corroborados por simulações, as quais integram numericamente as equações de movimento do sistema através do método de Runge-Kutta de quarta ordem. Os algoritmos e detalhes extras dos métodos de integração usados são explorados numa publicação deste trabalho, a qual está apresentada, nesta dissertação, como um apêndice. / In this work a quantitative and qualitative analysis of the dynamical stabilization of an inverted pendulum with a sinusoidal external perturbation applied at the suspension point is made. Initially, the external perturbation is composed of a single cosine, then a generalization is made using a sum of N cosines with different amplitudes and frequencies. Approximations are tested, and the time for which the inverted pendulum remains stable is explored when N is large, in order to recover the pattern of the case when N = 1. The specific case of periodic and almost periodic oscillations, when N = 2, is analysed and stability diagrams considering different frequencies and amplitudes are studied. Later, an additive Gaussian noise is added to the system so the degradation of the stability diagrams generated by different variances can be studied. All points of this work are corroborated by simulations, which numerically integrate the system’s equation of motion through a fourth order Runge-Kutta method. Algorithms and extra details on the integration methods used are explored in a publication of this work, which is presented in this thesis as an appendix.

This thesis focuses on comparing methods of denoising by deep learning and their implementation. In the last few years, it has become clear that it is not necessary to have paired data, as for noisy and clean pictures, to train convolution neural networks but it is sufficient to have only noisy pictures for denoising in particular cases. By using methods described in this thesis it is possible to effectively remove i.e. additive Gaussian noise and what more, it is possible to achieve better results than by using statistic methods, which are being used for denoising these days.

This master´s work is focused on the comparison of subjective assessment of the quality of video sequences. In this study, data are obtained by hardware and sofware techniques and they are compared. In the introduction, methods of video compressions are described. The main part of this work deals wtih the exploring of different methods of subjective assessment of the quality of video sequences. Finally, obtained results from different methods, are evaluated and discussed.

Title: Long range dependence in time series Author: Alexander Till Department: Department of Probability and Mathematical Statistics Supervisor: RNDr. Michaela Prokešová, Ph.D. Abstract: The diploma thesis demonstrates the necessity of a study of long range dependence, introduces fractional Gaussian noise and discusses possible definitions of long memory. It is done by notions of ergodic theory and by second moment characteristics and spectral density. These definitions are confronted with the model of fractional Gaussian noise and with intuitive understanding of long range memory. Relations and connections between these criteria are studied as well. The work is restricted to the study of discrete time processes. 1

Title: Long range dependence in time series Author: Alexander Till Department: Department of Probability and Mathematical Statistics Supervisor: RNDr. Michaela Prokešová, Ph.D. Abstract: The diploma thesis demonstrates the necessity of a study of long range dependence, introduces fractional Gaussian noise and discusses possi- ble definitions of long memory. It is done by notions of ergodic theory and by second moment characteristics and spectral density. These definitions are confronted with the model of fractional Gaussian noise and with intuitive un- derstanding of long range memory. Relations and connections between these criteria are studied as well. The work is restricted to the study of discrete time processes. Method for Hurst index estimation for fractional Gaussian noise and it's application on logarithmic returns of shares of selected produ- cers of beer are included in this work. 1

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We explore the applicability of local polynomial approximation of signals for noise suppression. In the context of data regression, Savitzky and Golay showed that least-squares approximation of data with a polynomial of fixed order, together with a constant window length, is identical to convolution with a finite impulse response filter, whose characteristics depend entirely on two parameters, namely, the order and window length. Schafer’s recent article in IEEE Signal Processing Magazine provides a detailed account of one-dimensional Savitzky-Golay (SG) filters. Drawing motivation from this idea, we present an elaborate study of two-dimensional SG filters and employ them for image denoising by optimizing the filter response to minimize the mean-squared error (MSE) between the original image and the filtered output. The key contribution of this thesis is a method for optimal selection of order and window length of SG filters for denoising images. First, we apply the denoising technique for images contaminated by additive Gaussian noise. Owing to the absence of ground truth in practice, direct minimization of the MSE is infeasible. However, the classical work of C. Stein provides a statistical method to overcome the hurdle. Based on Stein’s lemma, an estimate of the MSE, namely Stein’s unbiased risk estimator (SURE), is derived, and the two critical parameters of the filter are optimized to minimize the cost. The performance of the technique improves when a regularization term, which penalizes fast variations in the estimate, is added to the optimization cost. In the next three chapters, we focus on non-Gaussian noise models. In Chapter 3, image degradation in the presence of a compound noise model, where images are corrupted by mixed Poisson-Gaussian noise, is addressed. Inspired by Hudson’s identity, an estimate of MSE, namely Poisson unbiased risk estimator (PURE), which is analogous to SURE, is developed. Combining both lemmas, Poisson-Gaussian unbiased risk estimator (PGURE) minimization is performed to obtain the optimal filter parameters. We also show that SG filtering provides better lowpass approximation for a multiresolution denoising framework. In Chapter 4, we employ SG filters for reducing multiplicative noise in images. The standard SG filter frequency response can be controlled along horizontal or vertical directions. This limits its ability to capture oriented features and texture that lie at other angles. Here, we introduce the idea of steering the SG filter kernel and perform mean-squared error minimization based on the new concept of multiplicative noise unbiased risk estimation (MURE). Finally, we propose a method to robustify SG filters, robustness to deviation from Gaussian noise statistics. SG filters work on the principle of least-squares error minimization, and are hence compatible with maximum-likelihood (ML) estimation in the context of Gaussian statistics. However, for heavily-tailed noise such as the Laplacian, where ML estimation requires mean-absolute error minimization in lieu of MSE minimization, standard SG filter performance deteriorates. `1 minimization is a challenge since there is no closed-form solution. We solve the problem by inducing the `1-norm criterion using the iteratively reweighted least-squares (IRLS) method. At every iteration, we solve an l`2 problem, which is equivalent to optimizing a weighted SG filter, but, as iterations progress, the solution converges to that corresponding to `1 minimization. The results thus obtained are superior to those obtained using the standard SG filter.