Median filtering is a non-linear filtering technique which is effective in removing impulsive noise from data. In this thesis, directional median filtering has been implemented using cumulative histogram of samples in several directions. Different methods to implement directional median filtering have been proposed. The filtered images are smoothed along the direction of the filtering window. All implementations aimed to generate outputs in the least amount of time, while reducing the resource utilization on hardware. The implementation methods were designed for Xilinx Virtex 5 FPGA devices but were also attempted on Spartan 3E. The proposed methods used less than 30% of the resources on Virtex 5 FPGA but the resource utilization on Spartan 3E exceeded the number of available resources. After an initial delay, methods 1 and 2 generate a new output for every 5 clock cycles while method 3 generates an output for every 1.5 clock cycles.

The aim of this paper is to introduce the readers to the field of cellular automata, their design and their usage for structural design. Genetic algorithms are usually involved in designing complicated cellular automata, and because of that they are also briefly described here. For the purposes of this work sorting networks are considered as suitable structures to be designed using cellular automata, however, they are not a part of the automata but they are generated separately by modified rules of a local transition function.

This work provides an introduction to the evolutionary algorithms and evolutionary design. It also describes disadvantages of direct encoding of a genotype to a phenotype and a method of nontrivial encoding which can solve these problems. We are particularly talking about the problems of the scalability of evolved solutions. We discuss a possible solution of described problem - a nontrivial genotype-phenotype mapping called development. This technique is demonstrated on an evolutionary design of a sequence of rewriting rules which is able to construct arbitrarily large sorting networks.

This work focuses on application of rewriting systems in the context of biology-inspired development of structures in evolutionary algorithms. As a part of this work, a method has been proposed for design of general structures that uses the aforementioned principle. This document includes an introduction into the theory of sorting networks and their design, the problem of which is later shown to be solvable using the proposed method. In addition, the theoretical background of evolutionary algorithms and rewriting systems is discussed. In the field of rewriting systems, an extension to the class of IL-systems has been created for use in the proposed method. The proposed method has been successfuly applied in the design of growing sorting networks. The results produced by the proposed method significantly outperform the results of the currently best-known comparable method.

This master's thesis introduces the Random Boolean Networks as a developmental model in the evolutionary design. The representation of the Random Boolean Networks is described. This representation is combined with an evolutionary algorithm. The genetic operators are described too. The Random Boolean Networks are used as the developmental model for  the evolutionary design of the combinational circuits and the sorting networks. Moreover a representation of the Random Boolean Networks for the design of image filters is introduced. The proposed methods are evaluated in different case-studies. The results of the experiments are discussed together with the potential improvements  and topics of the next research.