The acoustic echo problem arises whenever there is acoustic coupling between a loudspeaker and a microphone, such as in a teleconference system. This problem is traditionally solved by using an acoustic echo canceler (AEC), which models the echo path with adaptive filters. Long adaptive filters are necessary for satisfactory echo cancellation, which makes AEC highly computationally complex. Recently, a low-complexity echo suppression scheme was presented, the perceptual acoustic echo suppressor (PAES). Spectral modification is used to suppress the echoes, and the complexity is reduced by incorporating perceptual theories. However, under ideal conditions AEC performs better than PAES. This thesis considers a hybrid system, which combines AEC and PAES. AEC is used to cancel low-frequency echo components, while PAES suppresses high-frequency echo components. The hybrid system is simulated and assessed, both through subjective listening tests and objective evaluations. The hybrid scheme is shown to have virtually the same perceived quality as a full-band AEC, while having a significantly lower complexity and a higher degree of robustness.

Bakalauro darbas „Protėvių aidas” atspindi šių dienų aktualijas, paliesdamas visuomenei svarbų klausimą apie mūsų senovės baltų kostiumą. Nagrinėjamos aprangos dalys ir fragmentai šių dienų aplinkoje, aiškinamasi kaip baltiškumo simboliai atsispindi drabužių kolekcijose. Analizė buvo atliekama siekiant geriau susipažinti su senovės baltų apranga ir suprojektuoti moteriškų drabužių kolekciją. Pirmame skyriuje analizuojami baltų aprangos istoriniai ypatumai, moterų drabužiai, papuošalų nešiojimas kaip pagrindinis dekoro elementas. Antrame darbo skyriuje nagrinėjami archeologų, filosofų, menininkų pagrindimai, argumentai apie baltų kultūros problemas. Argumentuojami dizainerių pasisakymai apie baltiško kostiumo interpretavimą šiandien. Trečiajame skyriuje pateikiamos įvairios baltų kostiumo refleksijos lietuvių kostiumo dizainerių kūryboje, menininkų kūrybinių darbų pavyzdžiai, bei įvairių autorių sukurtos drabužių kolekcijos. Ketvirtame skyriuje pristatomi lininių drabužių ansamblių vizualūs baltiško kostiumo meniniai ieškojimai, formų, faktūrų, simbolių, kolorito ypatumų analizė, siekiant tai interpretuoti drabužių kolekcijoje, pagrindžiama idėja, pateikiami eskizai, projektai, technologiniai mėginiai, aprašomas drabužių ansamblių kūrimo procesas – konstravimas, modeliavimas, siuvimas. Pateikiamos įvykdytų drabužių modelių fotografijos ir numatomas kolekcijos pristatymas. Penktame skyriuje aptariamos sukurtos drabužių kolekcijos realizavimo galimybės, pateikiama... [toliau žr. visą tekstą] / The Bachelor’s Work “The Echo of the Ancestors” reflects the urgent problems of recent time. It touches upon a subject of the ancient Baltic costume. The parts and the fragments of the clothes are analyzed in the modern surroundings. It is explained how the Baltic symbols are reflected in the collections of clothes. Analysis was made in order to find out more information about the ancient Baltic clothes and to design the collection of female clothes. Historic features of the Baltic female clothes, wearing jewelry as the main element of decoration are analyzed in the first chapter of this work. The main arguments about the problems of the Baltic culture given by archeologists, philosophers and artists are given in the second chapter of this work. The designers’ attitude towards the modern interpretation of the Baltic costume is also disclosed here. Different reflections of the Baltic costume in the creative works of Lithuanian costume designers, the examples of creative works and collections of different authors are described in the third chapter. Presentation of the linen collection of clothes by showing artistic search for Baltic costume is given in the fourth chapter. There is also analysis of peculiarities of form, texture, symbols and colors in order to interpret it in the clothes collection there is a well-founded idea. Sketches, projects and technological samples are presented. The process of creation of clothes ensemble (construction... [to full text]

The task of this thesis is to study methods for the acquisition perfusní imaging based on dynamic MR imaging with T1 contrast. It describes methods of measurement of T1 relaxation time and the possibility of evaluating the results. It further describes the phantoms and their use. And it is here mentioned for the dynamic acquisition protocol perfusní imaging. There is also described in detail created a program for automatic control of the NMR system. In the experimental measurements are performed on static and dynamic phantom, are also evaluated perfusion parameters from the Flash sequence.

Deep Learning hat in den letzten Jahren mit der Entwicklung leistungsfähigerer Hardware und neuer Architekturen wie dem Convolutional Neural Network (CNN), Transformer, und Netzwerken aus Long-Short Term Memory (LSTM)-Zellen ein rasantes Wachstum erlebt. Modelle für viele verschiedene Anwendungsfälle wurden erfolgreich veröffentlicht, und Deep Learning hat Einzug in viele alltägliche Anwendungen gehalten. Einer der größten Nachteile komplexer Modelle wie den CNNs oder LSTMs ist jedoch ihr hoher Energieverbrauch und der Bedarf an großen Mengen annotierter Trainingsdaten. Zumindest letzteres Problem wird teilweise durch die Einführung von neuen Methoden gelöst, die mit nicht-annotierten Daten umgehen können. In dieser Arbeit werden Echo State Networks (ESNs), eine Variante der rekurrenten neuronalen Netze (RNN), betrachtet, da sie eine Möglichkeit bieten, die betrachteten Probleme vieler Deep-Learning Architekturen zu lösen. Einerseits können sie mit linearer Regression trainiert werden, einer relativ einfachen, effizienten und gut etablierten Trainingsmethode. Andererseits sind ESN-Modelle interessante Kandidaten für die Erforschung neuer Trainingsmethoden, insbesondere unüberwachter Lerntechniken, die später in Deep-Learning-Methoden integriert werden können und diese effizienter und leichter trainierbar machen, da sie in ihrer Grundform relativ einfach zu erzeugen sind. Zunächst wird ein allgemeines ESN-Modell in einzelne Bausteine zerlegt, die flexibel zu neuen Architekturen kombiniert werden können. Anhand eines Beispieldatensatzes werden zunächst Basis-ESN-Modelle mit zufällig initialisierten Gewichten vorgestellt, optimiert und evaluiert. Anschließend werden deterministische ESN-Modelle betrachtet, bei denen der Einfluss unterschiedlicher zufälliger Initialisierungen reduziert ist. Es wird gezeigt, dass diese Architekturen recheneffizienter sind, aber dennoch eine vergleichbare Leistungsfähigkeit wie die Basis-ESN-Modelle aufweisen. Es wird auch gezeigt, dass deterministische ESN-Modelle verwendet werden können, um hierarchische ESN-Architekturen zu bilden. Anschließend werden unüberwachte Trainingsmethoden für die verschiedenen Bausteine des ESN-Modells eingeführt, illustriert und in einer vergleichenden Studie mit Basis- und deterministischen ESN-Architekturen als Basis evaluiert. Anhand einer Vielzahl von Benchmark-Datensätzen für die Zeitreihenklassifikation und verschiedene Audioverarbeitungsaufgaben wird gezeigt, dass die in dieser Arbeit entwickelten ESN-Modelle in der Lage sind, ähnliche Ergebnisse wie der Stand der Technik in den jeweiligen Bereichen zu erzielen. Darüber hinaus werden Anwendungsfälle identifiziert, für die bestimmte ESN-Modelle bevorzugt werden sollten, und es werden die Grenzen der verschiedenen Trainingsmethoden diskutiert. Abschließend wird gezeigt, dass zwischen dem übergeordneten Thema Reservoir Computing und Deep Learning eine Forschungslücke existiert, die in Zukunft zu schließen ist.:Statement of authorship vii Abstract ix Zusammenfassung xi Acknowledgments xiii Contents xv Acronyms xix List of Publications xxiii 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Reservoir Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Objective and Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Echo State Network 5 2.1 Artificial neuron model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 The basic Echo State Network . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 Advanced Echo State Network structures . . . . . . . . . . . . . . . . . . . . 15 2.4 Hyper-parameter optimization of Echo State Networks . . . . . . . . . . . . . 22 3 Building blocks of Echo State Networks 25 3.1 Toolboxes for Reservoir Computing Networks . . . . . . . . . . . . . . . . . . 25 3.2 Building blocks of Echo State Networks . . . . . . . . . . . . . . . . . . . . . 26 3.3 Define Extreme LearningMachines . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4 Define Echo State Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.5 Sequential hyper-parameter optimization . . . . . . . . . . . . . . . . . . . . . 32 4 Basic, deterministic and hierarchical Echo State Networks 35 4.1 Running example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2 Performance of a basic Echo State Network . . . . . . . . . . . . . . . . . . . 37 4.3 Performance of hierarchical Echo State Networks . . . . . . . . . . . . . . . . 42 4.4 Performance of deterministic Echo State Network architectures . . . . . . . . 44 4.5 Performance of hierarchical deterministic Echo State Networks . . . . . . . . 50 4.6 Comparison of the considered ESN architectures . . . . . . . . . . . . . . . . 52 4.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5 Unsupervised Training of the Input Weights in Echo State Networks 57 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 5.3 Optimization of the KM-ESN model . . . . . . . . . . . . . . . . . . . . . . . 63 5.4 Performance of the KM-ESN . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 5.5 Combination of the KM-ESN and deterministic architectures . . . . . . . . . 74 5.6 Hierarchical (determinstic) KM-ESN architectures . . . . . . . . . . . . . . . 77 5.7 Comparison of the considered KM-ESN architectures . . . . . . . . . . . . . . 80 5.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 6 Unsupervised Training of the Recurrent Weights in Echo State Networks 85 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 6.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 6.3 Optimization of the pre-trained models . . . . . . . . . . . . . . . . . . . . . . 88 6.4 Performance of the KM-ESN-based models . . . . . . . . . . . . . . . . . . . 93 6.5 Comparison of all considered ESN architectures . . . . . . . . . . . . . . . . . 95 6.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 7 Multivariate time series classification with non-standard Echo State Networks 101 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 7.2 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 7.3 Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 7.4 Optimization of the hyper-parameters . . . . . . . . . . . . . . . . . . . . . . 105 7.5 Comparison of different ESN architectures . . . . . . . . . . . . . . . . . . . . 107 7.6 Overall results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 7.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 8 Application of Echo State Networks to audio signals 123 8.1 Acoustic Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 8.2 Phoneme Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 8.3 Musical Onset Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 8.4 Multipitch tracking in audio signals . . . . . . . . . . . . . . . . . . . . . . . . 157 8.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 9 Conclusion and Future Work 165 9.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 9.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 Bibliography 169 / The field of deep learning has experienced rapid growth in recent years with the development of more powerful hardware and new architectures such as the Convolutional Neural Network (CNN), transformer, and Long-Short Term Memory (LSTM) cells. Models for many different use cases have been successfully published, and deep learning has found its way into many everyday applications. However, one of the major drawbacks of complex models based on CNNs or LSTMs is their resource hungry nature such as the need for large amounts of labeled data and excessive energy consumption. This is partially addressed by introducing more and more methods that can deal with unlabeled data. In this thesis, Echo State Network (ESN) models, a variant of a Recurrent Neural Network (RNN), are studied because they offer a way to address the aforementioned problems of many deep learning architectures. On the one hand, they can easily be trained using linear regression, which is a simple, efficient, and well-established training method. On the other hand, since they are relatively easy to generate in their basic form, ESN models are interesting candidates for investigating new training methods, especially unsupervised learning techniques, which can later find their way into deep learning methods, making them more efficient and easier to train. First, a general ESN model is decomposed into building blocks that can be flexibly combined to form new architectures. Using an example dataset, basic ESN models with randomly initialized weights are first introduced, optimized, and evaluated. Then, deterministic ESN models are considered, where the influence of random initialization is reduced. It is shown that these architectures have a lower computational complexity but that they still show a comparable performance to the basic ESN models. It is also shown that deterministic ESN models can be used to build hierarchical ESN architectures. Then, unsupervised training methods for the different building blocks of the ESN model are introduced, illustrated, and evaluated in a comparative study with basic and deterministic ESN architectures as a baseline. Based on a broad variety of benchmark datasets for time-series classification and various audio processing tasks, it is shown that the ESN models proposed in this thesis can achieve results similar to the state-of-the-art approaches in the respective field. Furthermore, use cases are identified, for which specific models should be preferred, and limitations of the different training methods are discussed. It is also shown that there is a research gap between the umbrella topics of Reservoir Computing and Deep Learning that needs to be filled in the future.:Statement of authorship vii Abstract ix Zusammenfassung xi Acknowledgments xiii Contents xv Acronyms xix List of Publications xxiii 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Reservoir Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Objective and Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Echo State Network 5 2.1 Artificial neuron model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 The basic Echo State Network . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 Advanced Echo State Network structures . . . . . . . . . . . . . . . . . . . . 15 2.4 Hyper-parameter optimization of Echo State Networks . . . . . . . . . . . . . 22 3 Building blocks of Echo State Networks 25 3.1 Toolboxes for Reservoir Computing Networks . . . . . . . . . . . . . . . . . . 25 3.2 Building blocks of Echo State Networks . . . . . . . . . . . . . . . . . . . . . 26 3.3 Define Extreme LearningMachines . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4 Define Echo State Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.5 Sequential hyper-parameter optimization . . . . . . . . . . . . . . . . . . . . . 32 4 Basic, deterministic and hierarchical Echo State Networks 35 4.1 Running example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2 Performance of a basic Echo State Network . . . . . . . . . . . . . . . . . . . 37 4.3 Performance of hierarchical Echo State Networks . . . . . . . . . . . . . . . . 42 4.4 Performance of deterministic Echo State Network architectures . . . . . . . . 44 4.5 Performance of hierarchical deterministic Echo State Networks . . . . . . . . 50 4.6 Comparison of the considered ESN architectures . . . . . . . . . . . . . . . . 52 4.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5 Unsupervised Training of the Input Weights in Echo State Networks 57 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 5.3 Optimization of the KM-ESN model . . . . . . . . . . . . . . . . . . . . . . . 63 5.4 Performance of the KM-ESN . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 5.5 Combination of the KM-ESN and deterministic architectures . . . . . . . . . 74 5.6 Hierarchical (determinstic) KM-ESN architectures . . . . . . . . . . . . . . . 77 5.7 Comparison of the considered KM-ESN architectures . . . . . . . . . . . . . . 80 5.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 6 Unsupervised Training of the Recurrent Weights in Echo State Networks 85 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 6.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 6.3 Optimization of the pre-trained models . . . . . . . . . . . . . . . . . . . . . . 88 6.4 Performance of the KM-ESN-based models . . . . . . . . . . . . . . . . . . . 93 6.5 Comparison of all considered ESN architectures . . . . . . . . . . . . . . . . . 95 6.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 7 Multivariate time series classification with non-standard Echo State Networks 101 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 7.2 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 7.3 Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 7.4 Optimization of the hyper-parameters . . . . . . . . . . . . . . . . . . . . . . 105 7.5 Comparison of different ESN architectures . . . . . . . . . . . . . . . . . . . . 107 7.6 Overall results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 7.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 8 Application of Echo State Networks to audio signals 123 8.1 Acoustic Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 8.2 Phoneme Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 8.3 Musical Onset Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 8.4 Multipitch tracking in audio signals . . . . . . . . . . . . . . . . . . . . . . . . 157 8.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 9 Conclusion and Future Work 165 9.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 9.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 Bibliography 169

No description available.

La réalisation d’une mémoire quantique pour la lumière met en jeu les aspects les plus fondamentaux de l’interaction matière-rayonnement. Pour capturer l’information quantique portée par la lumière, le matériau doit être capable de se maintenir dans un état de superposition quantique. Le temps de stockage est limité par la durée de vie de cet état, caractérisée par le temps de cohérence. Les premières expériences ont été réalisées dans des vapeurs atomiques froides, bien connues. Plus récemment, les ions de terres rares en matrice cristalline (REIC) ont attiré l’attention par leurs long temps de cohérence, associés à de larges bandes passantes d’interaction. Pour exploiter ces bonnes propriétés, des protocoles spécifiques ont été proposés. Nous nous sommes tournés vers un dérivé prometteur de l’écho de photon, le Peigne Atomique de Fréquences (AFC, proposé en 2008), fondé sur la transmission du champ incident à travers un profil d’absorption spectralement périodique. Les premiers chapitres de ce manuscrit présentent ce protocole et les travaux effectués durant cette thèse pour en améliorer l’efficacité (i.e. la probabilité de capter et de restituer l’information incidente), en augmenter la bande passante et la capacité de multiplexage et en mesurer le bruit. Les chapitres suivants présentent un nouveau protocole, proposé dans notre groupe durant cette thèse, et baptisé ROSE (Revival Of Silenced Echo). Ce protocole, très proche de l’écho de photon, a été démontré et caractérisé expérimentalement. Il semble très prometteur en termes d’efficacité, de bande passante et de bruit. / The development of a quantum memory for light involves the most fundamental aspects of the light-matter interaction. To store the quantum information carried by light, the material has to be able to stay in a state of quantum superposition. The storage time is limited by the lifetime of this state, characterized by the coherence time. The first experiments involved the well-known cold atomic vapors. More recently, Rare Earth Ions doped Crystals (REIC) have drawn attention because of their remarkably long coherence time, combined with a large interaction bandwidth. Specific protocols have been proposed to take the most out of these properties. We have opted for a promising spin-off of the well-known photon echo, named the Atomic Frequency Comb (AFC, proposed in 2008), based on the transmission of the incoming field through a spectrally periodic absorption profile. The first chapters of this manuscript present this protocol and our works aimed at improving its efficiency (the probability for capturing and retrieving the incoming information), increasing its bandwidth and its multiplexing capacity and measuring its noise. The following chapters present a new protocol, proposed in our group during this thesis, and called Revival Of Silenced Echo (ROSE). This protocol, similar to the photon echo, have been demonstrated and characterized experimentally. It seems really promising in terms of efficiency, bandwidth and noise.

This dissertation is focused on the research of the applicability of the thermographic method in the diagnosis of concrete bridges in the Czech Republic. The theoretical part characterizes selected defects of concrete structures and the principle of their detection. The practical part describes the measurements made in the laboratory on the test specimen and the field on the concrete bridge. Most attention is paid to infrared thermography, ground-penetrating radar and ultrasonic pulse-echo method. Based on the results of the dissertation, recommendations for the use of the thermographic test method in practice were developed.

Techniques based on principle of nuclear magnetic resonance (NMR) belong to the most modern methods for studying physical, chemical and biological properties of materials [1]. Their universality predestinates them for application in a wide range of scientific disciplines, e.g. in medicine to study properties of tissues. Advantages of techniques utilizing principle of NMR consist in their noninvasiveness and thoughtfulness to human health or studied material. In addition, no undesirable effects of magnetic force field have been so far proved by research. Objectives of this Diploma Thesis are evaluation of MR images of tissue cultures and determination of protons amount included in them. Theoretic part of the Thesis is devoted to the bases of NMR and provides basic overview of MR methods. The spin echo method (SE) is described in more details, including the process of assessment of technique’s parameters, e.g. general magnetization. Practical part of Diploma Thesis is focused on determination of integral of image intensity of clusters of early somatic embryos. Intensity integrals characterizing number of protons in growing cluster were calculated from MR images of spruce embryos contaminated by lead. The intensity of an image weighted by spin density is proportionate to the number of proton nuclei in the chosen slice. The Thesis describes further evaluation of relaxation time T2 from MR images weighted by spin density. Following part is dealing with determination of diffusion from MR images with application of compensation methods, three-measurement arrangement and presentation of obtained results. Images were processed by use of MATLAB and MAREVISI programs.

The thesis follows up a topic of fake news within the borders of Czech Republic. This frequently discussed phenomena is often linked to the pro-Kremlin propaganda, whose aim is to evoke fear across the citizens, as well as raise distrust towards reigning authorities, western institutions, a functionality of liberal democracy and at last but not least - distrust towards public media and mainstream media in general. This is being achieved by production of fake news, also known as fictive, false or manipulative articles, that are being spread through the social media. Despite its low credibility the news often make their way into the public discussion, forming the general opinion and as a result affecting many political decisions. In order to follow this topic up further, I will put in use the Adam Klein's concept, also known as information laundering.This concept describes the ways how these hateful articles, personal opinions and straight up false news are getting legitimised within the online world and subsequently spread through the social media under the disguise of respectable journalism. The thesis is aiming to map out the entire sphere of fake news media, its websites and Facebook and YouTube social profiles as well as describe individual aspects of the entire mechanism using quantitative analysis. In...

No description available.