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Komprese dvousměrných texturních dat založaná na víceůrovňové vektorové kvantizaci - doplňkový materiál
Havran, V. ; Filip, Jiří ; Myszkowski, K.
The Bidirectional Texture Function (BTF) is becoming widely used for accurate representation of real-world material appearance. In this paper a novel BTF compression model is proposed. The model resamples input BTF data into a parametrization, allowing decomposition of individual view and illumination dependent texels into a set of multidimensional conditional probability density functions. These functions are compressed in turn using a novel multi-level vector quantization algorithm. The result of this algorithm is a set of index and scale code-books for individual dimensions. BTF reconstruction from the model is then based on fast chained indexing into the nested stored code-books. In the proposed model, luminance and chromaticity are treated separately to achieve further compression. The proposed model achieves low distortion and compression ratios 1:233-1:2040, depending on BTF sample variability.
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Measuring social cohesion in the class groups
Buchtík, Martin ; Soukup, Petr (advisor) ; Tuček, Milan (referee)
The thesis discusses the possibilities of measuring social cohesion in collectives of high school student classes in an analytical sociological research. The aim of the work is to create a valid tool, which would enable the measuring of social cohesion. It is empirically based on a survey "Cohesion and solidarity: Social cohesion in class collectives". In the introductory part of the work the contemporary theoretical discourse is presented. On its grounds are discussed the reasons for choosing multidimensional approach of Ade Kearnes and Ray Forrest as a conceptual base for examining cohesion. The concept is then described in detail, it is operationalized and it is also compared to the approaches of other authors. The analytical part of the work addresses three problems. First of all, the suitability of the theoretical model, with regard to the data, is verified using the confirmatory factor analysis. The validated model is then analyzed and set into a wider context. Eventually, in the final part of the work, a 10 item set measuring social cohesion in class collectives is introduced. It is derived from the findings formulated in previous parts of the thesis. Its validity, reliability and possibility of generalization are discussed.
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Application of optimization methods in hydrological modeling
Jakubcová, Michala ; Máca, Petr (advisor) ; Hanel, Martin (referee)
Finding the optimal state of reality is the main purpose of the optimization process. The best variant from many possibilities is selected, and the effectiveness of the given system increases. Optimization has been applied in many real life engineering problems as in hydrological modelling. Within the hydrological case studies, the optimization process serves to estimate the best set of model parameters, or to train model weights in artificial neural networks.
Particle swarm optimization (PSO) is relatively recent optimization technique, which has only a few parameters to adjust, and is easy to implement to the selected problem. The original algorithm was modified by many authors. They
focused on changing the initialization of particles in the swarm, updating the population topology, adding new parameters into the equation, or incorporating shuffling mechanism into the algorithm.
The modifications of PSO algorithm improve the performance of the optimization, prevent the premature convergence, and decrease computation time. Therefore, the main aims of the presented doctoral thesis consist of proposal of a new PSO modification with its implementation in C++ programming language. More PSO variants were compared and analysed, and the best methods based on benchmark problems were applied in two hydrological case studies.
The first case study focused on utilization of PSO algorithms in inverse problem related to estimation of parameters of rainfall-runoff model Bilan. In the second case study, combination of artificial neural networks with PSO methods was introduced for forecasting the Standardized precipitation evapotranspiration drought index.
It was found out, that particle swarm optimization is a suitable tool for solving problems in hydrological modelling. The most effective PSO modifications are the one with adaptive version of parameter of inertia weight, which updates the velocity of particles during searching through the multidimensional space via feedback information. The shuffling mechanism and redistribution of particles into complexes, at which the PSO runs separately, also significantly improve the performance.
The contribution of this doctoral thesis lies in creation of new PSO modification, which was tested on benchmark problems, and was successfully applied in two hydrological case studies. The results of this thesis also extended the utilization of PSO methods in real life engineering optimization problems. All analysed PSO algorithms are available for later use within other research projects.
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Methodology of design multidimensional databases in the farm environment
Vasilenko, Alexandr ; Klimešová, Dana (advisor) ; Toman, Prokop (referee)
This dissertation thesis is focused on bulk unsolicited messages which are present in
current time in all sectors of electronic communications. It is not only e-mail
communication, but also in online forums, discussion contributions, social networking
and more. Analyze spam messages is therefore an essential element in preventing
flooding user mailboxes.
Antispam countermeasures is a set of processes, software tools and methods. It is
necessary to harmonize all these components into one cooperating piece of service.
Administrators of email servers are trying to keep their servers optimally configured. The
problem is that spammers trying continuously these defense mechanisms and filter bypass
to enhance spam processes. Their work is very sophisticated and this fight does not yet
have a clear winner. After enhancement techniques either processes takes place after a
certain time to balance the advantages and disadvantages.
For these reasons it is necessary to have a tool which can be analyzed in depth junk
messages with dynamic data views. This tool can be Online Analytical Processing (OLAP
below), which is very suitable for this purpose.
Presented a method of data extraction and transformation and preparation for storage in
a warehouse DT-MEZ (Data Pump - metadata email messages). This method is part of
the methodology ASOLAP (Antispam - OLAP).
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Set-indexed stochastic processes
Schenk, Martin ; Rataj, Jan (referee) ; Pawlas, Zbyněk (advisor)
This thesis deals with the problem of estimating the joint probability distribution of a marked process' parameters from a censored data. First, a Nelson-Aalen estimator of the cumulative hazard rate for one-dimensional case is constructed. This estimator is then smoothed by using a kernel function estimator. Then, a Kaplan-Meier estimator of the survival function is brought in. Further, a theory of set-indexed random processes is built up to be a base for the construction of a generalized Nelson-Aalen estimator of the cumulative hazard rate, which is then again smoothed. For a special case, a generalized Kaplan-Meier estimator of the multidimensional survival function is constructed. The application of the mentioned generalized estimators is shown on a particular case. These estimators are then used on simulated data.
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Eliptic indexing of multidimensional databases
Danko, Ondrej ; Hoksza, David (referee) ; Skopal, Tomáš (advisor)
In this work variation of R-tree, which hierarchically partition indexed space using minimum volume covering ellipsoids (MVCE) instead of usually used minimum bounding rectangles, is presented. Main aspects, which determine R-tree index structure performance, are studied from the available resources at the beginning. Base on this studies "e-Rtree" (ellipsoid R-tree) is designed. Afterward algorithms of MVCE construction are carefully analyzed, as the choice of the algorithm is crucial for the efficiency of indexing and retrieval. At the end of the work, eR-tree implementation over ATOM framework is presented along with experiments done on synthetic and real data sets.
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Nonabsolutely convergent integrals
Kuncová, Kristýna ; Malý, Jan (advisor) ; Rataj, Jan (referee)
Title: Nonabsolutely convergent integrals Author: Kristýna Kuncová Department: Department of Mathematical Analysis Supervisor: Prof. RNDr. Jan Malý, DrSc., Department of Mathematical Analysis Abstract: Our aim is to introduce an integral on a measure metric space, which will be nonabsolutely convergent but including the Lebesgue integral. We start with spaces of continuous and Lipschitz functions, spaces of Radon measures and their dual and predual spaces. We build up the so-called uniformly controlled integral (UC-integral) of a function with respect to a distribution. Then we investigate the relationship between the UC-integral with respect to a measure and the Lebesgue integral. Then we introduce another kind of integral, called UCN-integral, based on neglecting of small sets with respect to a Hausdorff measure. Hereafter, we focus on the concept of n-dimensional metric currents. We build the UC-integral with respect to a current and then we proceed to a very general version of Gauss-Green Theorem, which includes the Stokes Theorem on manifolds as a special case. Keywords: Nonabsolutely convergent integrals, Multidimensional integrals, Gauss-Green Theorem 1
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Level Sets of Multivariate Density Functions and their Estimates
Kubetta, Adam ; Hlubinka, Daniel (advisor) ; Zichová, Jitka (referee)
A level set of a function is defined as the region, where the function gets over the specified level. A level set of the probability density function can be considered an alternative to the traditional confidence region because on certain conditions the level set covers the region with minimal volume over all regions with a given confidence level. The benefits of using level sets arise in situations where, for example, the given random variables are multimodal or the given random vectors have strongly correlated components. This thesis describes estimates of the level set by means of a so called plug-in method, which first estimates density from the data set and then specifies the level set from the estimated density. In addition, explicit direct methods are also studied, such as algorithms based on support vectors or dyadic decision trees. Special attention is paid to the nonparametric probability density estimates, which form an essential tool for plug-in estimates. Namely, the second chapter describes histograms, averaged shifted histograms, kernel density estimates and its generalization. A new technique transforming kernel supports is proposed to avoid the so called boundary effect in multidimensional data domains. Ultimately, all methods are implemented in Mathematica and compared on financial data sets.
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Relationship of poverty and conflict in international affairs
Gabriel, David ; Dubský, Zbyněk (advisor) ; Druláková, Radka (referee)
This master thesis focuses on a relationship between poverty and conflict. The aim is to analyze poverty as a potential cause of internal conflict. The thesis is divided into two parts. The first part discusses the multidimensional character of poverty, the nature of a nexus between poverty and conflict and introduces modern concepts of economic causes of conflict. The second part tests if higher personal income reduces the probability of internal conflict to emerge. Both phenomena are transformed into a simple mathematical function in order to test their nexus. Characteristics of both regional and universal nature of their relationship are scrutinized. Finally, poverty is not universally believed to be a major cause of civil conflicts. However, poverty is suggested to be a major cause of internal conflicts if personal income is not high enough to satisfy basic human meets. The level of violence, under which conflicts are likely to emerge, is believed to be set at the level of 2$ (of daily income). #English version of the thesis is available per request.#
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