The theoretical contents of this thesis are studies of overcomplete models. Those are the models of signals, on which is set for their parametrization more variables, than it's necessary and consequently there's computed so-called sparse solution via iteration algorithms. A goal of this analysis is a selection just of the considerable (sparse) parameters. The theory is based on a linear algebra, vector spaces, bases and so-called frames. The task of the individual project of this thesis is a description and simulation of two speech coders: a classical coder based on linear predictive speech coding and a coder, that's making use of overcomplete stochastic ARMA processes models. A part of their realization is to simulate their decoders and a analyze their reconstruction quality. For their realization there is used MATLAB and an overcomplete models' library (toolbox frames).

This thesis mainly deals with the prediction of temperature on the components and the PCB during reflow soldering. The theoretical part describes the particular solder reflow process, types of heat transfer and temperature profiles. The practical part is divided into forecasting temperatures if the conveyor is stopped and the temperature predictions when the conveyor is in motion. In both parts of the measured temperature is compared with the predicted temperatures, which show the success rate of prediction. The last part of this work is part of the simulation, which helps in proper understanding of the issues discussed.

This thesis deal with an analysis of network traffic and its properties. In this thesis are discussed possibilities of prediction network traffic by FARIMA model, theory of chaos with Lyapunov exponent and by neural networks. The biggest attention was dedicated to prediction network traffic by neural networks. In Matlab with using Neural Network Toolbox were created, trained and tested recurrent networks for prediction specific types of network traffics, which was captured on local network. There were choosed Elman network, LRN and NARX network to test the prediction of network traffic, results were discussed. Thesis also introduce area of application ability prediction of network traffic, there is introduce design of system for dynamic allocation bandwidth with particular description of its prediction part. Thesis also states possible use designed system for dynamic allocation of bandwidth.

Knowledge of protein hot spots and the ability to successfully predict them while using only primary protein structure has been a worldwide scientific goal for several decades. This thesis describes the importance of hot spots and sums up advances achieved in this field of study so far. Besides that we introduce hot spot prediction algorithm using only a primary protein structure, based primarily on signal processing techniques. To convert protein sequence to numerical signal we use the EIIP attribute, while further processing is carried out via means of S-transform. The algorithm achieves sensitivity of more than 60 %, positive predictive value exceeds 50 % and the main advantage over competitive algorithms is its simplicity and low computational requirements.

The aim of this thesis was to study problems of prediction of data in computer networks. Furthermore, this work deals with network traffic and analyzing its properties. In this study were analyzed the possibilities of network traffic prediction using Farima model, the theory of chaos with Lyapunov exponents and neural networks. Possibilities of prediction with the focus on neural network were discussed in detail here, mainly on recurrent neural networks. Prediction was performed in Matlab development environment in Neural Network Toolbox, where they were created, trained and evaluated neural network to predict specific types of network traffic. For testing were selected Elman network NARX network and general LRN recurrent network. The results were clearly organized into tables and plotted in graphical relationships before and after the use of predictive techniques designed to final evaluation.

This work deals with stock market prediction via technical and psychological analysis. We introduce theoretical resources of technical and psychological analysis. We also introduce some methods of artificial intelligence, specially neural networks and genetic algorithms. We design a system for stock market prediction. We implement and test a part of system. In conclusion we discuss results.

The diploma thesis deals with the planning system of the stocks of finished products in the company Barum Continental Co., Ltd. This work describes the current state of the supply business process in order to meet the increased requirements during the delivery cycle. Based on the analysis are indentified factors influencing the planning of the stocks of finished products. The practical part of this work includes alternative proposals and recommendation for a more flexible reaction to customer requirements based on processing and monitoring statistical data.

The master thesis evaluates economic situation of a company using statistical methods. The paper comes out of the company's financial statement that have been economically analysed and led to statistical analysis. Using statistic methods together with historical data estimates future trends. Consequently, solutions that could lead to the company's financial stability and sustainable management.

This diploma thesis is focused on using statistical methods of technical analysis of stocks. The teoretical part describes basic principals of regression and technical analysis with a description of the technical indicators that are used to predict the future development of share prices and finding appropriate moment to buy or sell stocks. The results of the analysis is the comparison of indicators and their applicability to trade the stock titles.

Tato práce se zabývá návrhem, realizací a optimalizací systému určenímu k obchodování na finančních trzích, konkrétně s technologickými firmami trhu NASDAQ. K tomuto účelu jsou využívány technické indicatory a hlavně neuronových sítí. Vlastní řešení je pak realizováno v prostředi MATLAB.