This paper concerns aliasing removal methods during the shadow displaying. Method of shadow mapping, its principles, procedure and mainly its drawbacks in the form of aliasing development are described. For the removal of this undesirable phenomenon, several aliasing suppressing methods are described - Percentage Closer Filter, Variance Shadow Map, Convulotion Shadow Map, Exponential Shadow Map a Bilateral Filter. I conclude my work with a proposal and implementation of a demonstrative application, which demonstrates the implemented results adequately. Also, the comparison of individual methods on the basis of their quality and computational demands is included.

The point cloud is an unorganized set of points with 3D coordinates (x, y, z) which represents a real object. These point clouds are acquired by the technology called 3D scanning. This scanning technique can be done by various methods, such as LIDAR (Light Detection And Ranging) or by utilizing recently developed 3D scanners. Point clouds can be therefore used in various applications, such as mechanical or reverse engineering, rapid prototyping, biology, nuclear physics or virtual reality. Therefore in this doctoral Ph.D. thesis, I focus on feature detection and visualization in a point cloud. These features represent parts of the object that can be described by the well--known mathematical model (lines, planes, helices etc.). The points on the sharp edges are especialy problematic for commonly used methods. Therefore, I focus on detection of these problematic points. This doctoral Ph.D. thesis presents a new algorithm for precise detection of these problematic points. Visualization of these points is done by a modified curve fitting algoritm with a new weight function that leads to better results. Each of the proposed methods were tested on real data sets and compared with contemporary published methods.

This thesis deals with the field of recommendation systems using deep neural networks and their use in book recommendation. There are the main traditional recommender systems analysed and their representations are summarized, as well as systems with more advanced techniques based on machine learning. The core of the thesis is to use convolutional neural networks for natural language processing and create a hybrid book recommendation system. Suggested system includes matrix factorization and make recommendation based on user ratings and book metadata, including texts descriptions. I designed two models, one with bag-of-words technique and one with convolutional neural network. Both of them defeat baseline methods. On the created data set, that was created from the Goodreads, model with CNN beats model with BOW.

In the modern world, email communication defines itself as the most used technology for exchanging messages between users. It is based on three pillars which contribute to the popularity and stimulate its rapid growth. These pillars are represented by free availability, efficiency and intuitiveness during exchange of information. All of them constitute a significant advantage in the provision of communication services. On the other hand, the growing popularity of email technologies poses considerable security risks and transforms them into an universal tool for spreading unsolicited content. Potential attacks may be aimed at either a specific endpoints or whole computer infrastructures. Despite achieving high accuracy during spam filtering, traditional techniques do not often catch up to rapid growth and evolution of spam techniques. These approaches are affected by overfitting issues, converging into a poor local minimum, inefficiency in highdimensional data processing and have long-term maintainability issues. One of the main goals of this master's thesis is to develop and train deep neural networks using the latest machine learning techniques for successfully solving text-based spam classification problem belonging to the Natural Language Processing (NLP) domain. From a theoretical point of view, the master's thesis is focused on the e-mail communication area with an emphasis on spam filtering. Next parts of the thesis bring attention to the domain of machine learning and artificial neural networks, discuss principles of their operations and basic properties. The theoretical part also covers possible ways of applying described techniques to the area of text analysis and solving NLP. One of the key aspects of the study lies in a detailed comparison of current machine learning methods, their specifics and accuracy when applied to spam filtering. At the beginning of the practical part, focus will be placed on the e-mail dataset processing. This phase was divided into five stages with the motivation of maintaining key features of the raw data and increasing the final quality of the dataset. The created dataset was used for training, testing and validation of types of the chosen deep neural networks. Selected models ULMFiT, BERT and XLNet have been successfully implemented. The master's thesis includes a description of the final data adaptation, neural networks learning process, their testing and validation. In the end of the work, the implemented models are compared using a confusion matrix and possible improvements and concise conclusion are also outlined.

This thesis deals with the field of Recommendation systems using Deep Neural Networks and their use in book recommendation. There are the main traditional recommender systems analysed and their representations are summarized, as well as systems with more advancec techniques based on machine learning.. The core of the thesis is the use of convolutional neural networks for natural language processing and the creation of a book recommendation system. Suggested system make recommendation based on user data, including user reviews and book data, including full texts.

This work deals with digital signal processing in the field of adaptive filtering. Fundamental basics of adaptive filtering are described and primary aim is to create executable laboratory examples, using adaptive filtering, in LabView programming language. These laboratory examples are intended to be used by students fo studying and during laboratory lessons. Objective is to connect the examples with external devices, such as microphone. A microphone is used as an user's speech input acquiring interface. In the thesis is depicted Wiener's filter and problem of adaptive filtering is discussed. Contemporary adaptive algorithms are described and their applications as well. Most mentioned is the LMS algorithm and it's forms. Laboratory examples use following concepts: Adaptive Echo Cancellation, Active Noise Control and System Identification. Each of these examples is solely executable (need for LabView or Run-time engine), consisting also of theory with diagrams. Examples therefore are usable even without manual.

This thesis deals with the filter algorithm design, implementing mathematical model to improve algorithm performance. Designed algorithms are implemented in a control unit of the experimental vehicle (filters signal used in the closed-loop controller). The improvement of the position estimation using Kalman Filter is demonstrated on the experimental vehicle. In the next part the design process of algorithm developing for dsPIC microcontroller using Matlab is described.

Fyzikálně přijatelné osvětlení v reálném čase je často dosaženo použitím aproximací. Současné metody často aproximují globální osvětlení v prostoru obrazu s využitím schopností moderních grafických karet. Dva techniky z této kategorie, screen-space ambient occlusion a screen-space directional occlusion jsou popsány detailněji v této práci. Screen-space directional occlusion je zobecněná verze screen-space ambient occlusion s podporou jednoho difúzního odrazu a závislostí na směrové informaci světla. Hlavním cílem projektu bylo experimentování s těmito metodami. Pro uniformní distribuci náhodných vzorek pro obě metody byla použita Halton sekvence. Pro potlačení šumu je použita bilaterální filtrace, která bere do úvahy geometrické vlastnosti scény. Metody jsou dál zrychleny použitím nižších rozlišení pro výpočet. Rekonstrukce výsledků do původní velikosti pro vytvoření konečného obrazu je realizována pomoci joint bilateral upsamplingu. Kromě metod globálního osvětlení byly v práci použity aj metody pro mapování stínů a HDR osvětlení.

The main task of this Masters Thesis is to understand questions of information retrieval and text classifi cation. The main research is focused on the text data, the semantic dictionaries and especially the knowledges inferred from the Wikipedia. In this thesis is also described implementation of the querying system, which is based on achieved knowledges. Finally properties and possible improvements of the system are talked over.

This thesis deals with intelligent systems for support of the semantic annotation of text. It discusses the motivation for creation of such systems and state of the art in the areas of their usage. The thesis also describes newly proposed and realised annotation system which realizes advanced functions of semantic filtering and presentation of annotation suggestion alternatives in a unique way. The results of finished experiments clearly show the advantages of proposed solution. They also prove that the user interface of the annotation tools affects the annotation process. The optimisation of displayed information for the task of disambiguation of ambiguous entity names was done and proposed methods to speedup and increase of quality of the created annotations was experimentally evaluated. The comparison with the Protégé general tool has proven the benefits of created system for collaborative ontology creation which should be anchored in the text. In the conclusion, all achieved results are analysed and summarized.