The dissertation thesis deals with 3D reconstruction of the temporomandibular joint from 2D slices of tissue obtained by magnetic resonance. The current practice uses 2D MRI slices in diagnosing. 3D models have many advantages for the diagnosis, which are based on the knowledge of spatial information. Contemporary medicine uses 3D models of tissues, but with the temporomandibular joint tissues there is a problem with segmenting the articular disc. This small tissue, which has a low contrast and very similar statistical characteristics to its neighborhood, is very complicated to segment. For the segmentation of the articular disk new methods were developed based on the knowledge of the anatomy of the joint area of the disk and on the genetic-algorithm-based statistics. A set of 2D slices has different resolutions in the x-, y- and z-axes. An up-sampling algorithm, which seeks to preserve the shape properties of the tissue was developed to unify the resolutions in the axes. In the last phase of creating 3D models standard methods were used, but these methods for smoothing and decimating have different settings (number of polygons in the model, the number of iterations of the algorithm). As the aim of this thesis is to obtain the most precise model possible of the real tissue, it was necessary to establish an objective method by which it would be possible to set the algorithms so as to achieve the best compromise between the distortion and the model credibility achieve.

This diploma thesis is based on the current issue of the presence of pharmaceuticals in various components of the environment. Concerning the contamination by residues of pharmaceuticals the most affected environment is the aquatic environment where these substances leaks especially from wastewater treatment plants, which eliminate them during the cleaning process only partially. This work is focused on the selected group of pharmaceuticals, non-steroidal anti-inflammatory drugs (salicylic acid, ibuprofen, caffeine, naproxen, ketoprofen, diclofenac) in waste water. For analysis purposes two types of sampling were used and compared: the conventional spot sampling of wastewater and the sampling using passive samplers POCIS. The sampling took place at the inflow and outflow of the wastewater treatment plant in Brno Modřice. The solid phase extraction (SPE) using Oasis HLB columns was used as the extraction method. Extracted sample was derivatized then. Derivatization agents were: MSTFA (N-methyl-N-(trimethylsilyl)trifluoroacetamid) and BSTFA (N, O-bis(trimethylsilyl) trifluoroacetamid). The final analysis was performed using gas chromatography with mass spectrometric detection Time-of-Flight (GC/TOF-MS).

This master's thesis deals with demonstration of various approaches to probabilistic inference in Bayesian networks. Basics of probability theory, introduction to Bayesian networks, methods for Bayesian inference and applications of Bayesian networks are described in theoretical part. Inference techniques are explained and complemented by their algorithm. Techniques are also illustrated on example. Practical part contains implementation description, experiments with demonstration applications and conclusion of the results.

The presented doctoral thesis aims at development a new efficient tool for optimization of uniformity of point samples. One of use-cases of these point sets is the usage as optimized sets of integration points in statistical analyses of computer models using Monte Carlo type integration. It is well known that the pursuit of uniformly distributed sets of integration points is the only possible way of decreasing the error of estimation of an integral over an unknown function. The tasks of the work concern a survey of currently used criteria for evaluation and/or optimization of uniformity of point sets. A critical evaluation of their properties is presented, leading to suggestions towards improvements in spatial and statistical uniformity of resulting samples. A refined variant of the general formulation of the phi optimization criterion has been derived by incorporating the periodically repeated design domain along with a scale-independent behavior of the criterion. Based on a notion of a physical analogy between a set of sampling points and a dynamical system of mutually repelling particles, a hyper-dimensional N-body system has been selected to be the driver of the developed optimization tool. Because the simulation of such a dynamical system is known to be a computationally intensive task, an efficient solution using the massively parallel GPGPU platform Nvidia CUDA has been developed. An intensive study of properties of this complex architecture turned out as necessary to fully exploit the possible solution speedup.

In this work is designed programme unit for acquisition analog acoustic signlas from sensors. There is used accurate A/D converter ADS1287 by the company Texas Iinstrument with resolution of 24 bits to digitizing these signals. There is used 32-bit microprocessor AT91SAM7S64 by the company Atmel to control this A/D converter and sending digitized data to PC. This microprocessor has implemented USB interface. By force of developmental programme units whit microprocessor and A/D converter is produced programme for microprocessor in developmental setting IAR Embedded Workbench IDE 5.0 and simple aplication for PC in setting Borland C++ Builder. Both of those programs are in language C++. Rate of sampling is 26 kHz. On the basis is realized programme unit USB-ADC whit microprocessor and A/D converter.

Description of construction of analog-to-digital and digital-to-analog convertors for audio signal and distribution via wireless channel, are the objectives of my master's thesis. There are descriptions of DPS construction, design of panels and measurement of system parameters in the project, above all sampling and reconstruction of audio signal, power consumption and signal range of wireless modules. At the end is discussed real usage and suggestions for further developement.

The aim of this thesis is to create data acquisition application. The software module is designed for the industrial panel PC. Power Panel 520 from Bernecker + Reiner and the data acquisition board NI USB-6215 from National Instruments represent the fundamental hardware components for this project. Data acquisition and visualization tasks are controlled by a standalone application made in LabVIEW 2011. OPC, industrial standard in communication, is responsible for mutual interconnection between control (PLC) and data acquisition part. Network shared variables and Apple iPad 2 allow remote surveillance for operator. Finally, the creation of user-friendly interface and integration of technologies from different vendors, are main goals of this project.

The topic of this master's thesis is the time lapse from stationary camera images. Unwanted phenomena, which arise in time lapse, were analyzed and algorithms to overcome these limitations were designed. The algorithms were implemented and compared using the captured dataset. The resulting application creates time lapse from the video input and allows users to choose the processing technique including the setting of appropriate parameters.

In my thesis I~ deal with the design, implementation and testing of the advanced parallel Estimation of Distribution Algorithm (EDA) utilizing copula theory to create a~ probabilistic model. A~new population is created by the process of sampling the joint distribution function, which models the current distribution of the subpopulation of promising individuals . The usage of copulas increases the efficiency of the learning process and sampling the probabilistic model. It can be separated into mutually independent marginal distributions and the copula , which represents the correlations between the variables of the solved problem. This concept initiated the usage of the parallel island architecture , in which the migration of probabilistic models belonging to individual islands ' subpopulations was used instead of the migration of individuals . The statistical tests used in the comparison of the proposed algorithm ( mCEDA = migrating Copula - based Estimation of Distribution Algorithm ) and the algorithms of other authors confirmed the effectiveness of the proposed concept .

This master’s thesis is dealing with remote measuring systems and their utilization in power engineering. In the first part there are described requirements on an autonomous measuring system and description of AMM and AMR systems. Communicating and data flows working on accurately defined communicating layers are also depicted in this part. The attention is devoted to the possibility of data processing and functions of devices offered by these attributes. In the following part there are described reasons for utilization of remote measuring systems in power engineering and the main advantages of connection of more devices into one unit. After finding of required parametres and functions of systems, a suggestion of terminal device is created in the same way. The suggestion is described in the measuring part with A/D converter, processing in microprocessor, measuring and evaluating alogorithms and attributes of communication of bus used. The last part is devoted to utilization of remote analysis in small power stations in dispersed production. Formation of deformation of voltage, harmonic analysis of signal and its application for data processing are described there. Described analysis was tested on data acquired from a cogeneration unit.