The thesis focuses on a new concept of cellular automata control based on instructions. The instruction can be understood as a rule that checks the states of cells in pre-defined areas in the cellular neighbourhood. If a given condition is satisfied, the state of the central cell is changed according to the definition of the instruction. Because it's possible to perform more instructions in one computational step, their sequence can be understood as a form of a short program. This concept can be extended with simple operations applied to the instruction's prescription during interpretation of the instructions - an example of such operation can be row shift or column shift. An advantage of the instruction-based approach lies in the search space reduction. In comparison with the table-based approach, it isn't necessary to search all the possible configurations of the cellular neighbouhood, but only several areas determined by the instructions. While the groups of the inspected cells in the cellular neighbourhood are designed manually on the basis of the analysis of the solved task, their sequence in the chromosome is optimized by genetic algorithm. The capability of the proposed method of cellular automata control is studied on these benchmark tasks - majority, synchronization, self-organization and the design of combinational circuits.

The content of this thesis is to understand the principle of ultrasound imaging and mathematical models used to estimate perfusion parameters of concentration curves. Thesis deals with global optimization algorithms for finding parameters, an approximation of the actual data model curves. It also includes a comparison of different methods and used functions.

Regular model checking is a method for verifying infinite-state systems based on coding their configurations as words over a finite alphabet, sets of configurations as finite automata, and transitions as finite transducers. We implement regular model checking using inference of regular languages. The method builds upon the observations that for infinite-state systems whose behavior can be modeled using length-preserving transducers, there is a finite computation for obtaining all reachable configurations.   Our new approach to regular model checking via inference of regular languages is based on the Angluin's L* algorithm that is used for finding out an invariant which can answer our question whether the system satisfies some property.   We also provide an intro to the theory of finite automata, model checking, SAT solving and Anguin's L* and Bierman algorithm of learning finite automata.

In this work we deal with survival models, when we consider that with positive probability some patients never relapse because they are cured. We focus on two-component mixture model and model with biological motivation. For each model, we derive estimate of probability of cure and estimate of survival function of time to relaps of uncured patients by maximum likelihood method. Further we consider, that both probability of cure and survival time can depend on regressors. Models are then compared through simulation study. 1

The Generalization of the Digital Terrain Model Based on the TIN Abstract This diploma thesis deals with the up to now way and the possibilities of the digital terrain model generalization based on the TIN (the triangulate irregular network). New suitable way of the generalization of the digital terrain model procured from laser scanning data is proposed on the base of the existing generalization methods designated for digital models. Laser scanning data is characterized by a high areal density so the basic requirement is computing speed, maintaining the terrain features, such as a ridge, valley, steep hill, saddle, depression … and so on. The proposed algorithm is compared with the results of suggested algorithms and results from the generalization by the geographic software, such as Atlas DMT and ArcGIS.

In the present work, we study possibilities of using artificial neural networks for accelerating of evolutionary algorithms. Improving consists in decreasing in number of calls to the fitness function, the evaluation of which is in some kinds of optimization problems very time- consuming and expensive. We use neural network as a regression model, which serves for fitness estimation in a run of evolutionary algorithm. Together with the regression model, we work also with the real fitness function, which we use for re-evaluation of individuals that are selecting according to a beforehand chosen strategy. These individuals re-evaluated by the real fitness function are used for improving the regression model. Because a significant number of individuals are evaluated only with the regression model, the number of calls to the real fitness function, that is needed for finding of a good solution of the optimization problem, is substantially reduced.

The aim of this work is to examine the applicability of an algorithm for approximate frequency counting to act as an on-the-fly filter in the process of phrase table extraction in Statistical Machine Translation systems. Its implementation allows for the bulk of extracted phrase pairs to be much reduced with no significant loss to the ultimate quality of the phrase-based translation model as measured by the state-of-the-art evaluation measure BLEU. The result of this implementation is a fully working program, called eppex, capable of acting as an alternative to the existing tools for phrase table creation and filtration that are part of the open-source SMT system Moses. A substantial part of this work is devoted to the benchmarking of both the runtime performance and the quality of produced phrase tables achieved by the program when confronted with parallel training data comprised of 2 billions of words. Powered by TCPDF (www.tcpdf.org)

This thesis deals with an implementation of algorithm for computation of generator of almost split sequences ending at an indecomposable nonprojective module of path algebra over finite quiver. Algorithm is implemented in algebra system GAP (Groups, Algorithms, Programming) with additional package QPA (Quivers and Path Algebras). Powered by TCPDF (www.tcpdf.org)

Let G be a finite cyclic group. Solving the equation g^x = y for a given generator g and y is called the discrete logarithm problem. This problem is at the core of many modern cryptographic transformations. In this paper we provide a survey of algorithms to attack this problem, including the function field sieve, the fastest known algorithm applicable to the multiplicative group of a finite field. We also discuss the index calculus algorithm and some techniques improving its performance: the Coppersmith's algorithm and the polynomial sieving. The most important contribution of this paper is a C-language implementation of the function field sieve and its application to real inputs.

Scheduling problems and constraint satisfaction problems are generally known to be extremely hard. This thesis proposes a new evolutionary al- gorithm approach to solve a constrained-based scheduling problem. In this approach, variable orderings are evolved. The variable ordering serves as a parameter for the constraint solver. Its purpose is to determine the order in which variables are labelled by the solver. Hence the evolving individuals may be encoded as permutations. Therefore, our approach can be applied to a wider range of constraint satisfaction problems. Methods for generating the initial population of individuals based on the analysis of the precedence constraints graph are proposed. New genetic operators are presented and successfully applied. Our approach succeeded in finding a range of diverse schedules with the optimal makespan. Furthermore, multi-objective opti- mization was successfully attempted with the NSGA-II. 1