Graphs as data structures are used in many applications, for example image analysis, scene description, natural language processing. The paper deals with Acyclic Graph Data Structures (AGDS) and with a learning process in a model of a Self-Organizing Map (SOM) that has been modified for processing of AGDS. To the modified SOM Neural Network (SOM NN), there are added contexts and counters which are built in a training phase of the neural network. The trained SOM NN in active phase can compute information which is used to built some acyclic substructure. The prepared application was tested on the real data of study programs, the test results are evaluated using a winner differentiation and a confidence criterion.

This paper focuses on the presentation of an approach to language pattern recognition and distinguishes two concepts: abstraction and structural complexity. Abstraction as well as language patterns are examined from both current state and future perspectives. The abstraction is traced through a specific set of tools, and the premises on which the measuring methodology stands are critically analyzed with respect to theoretical and application concerns. Particular attention is paid to the main features that characterize language patterns, proposing a method for automatized raise of abstraction level based on recognition of patterns in program source code (thus not design patterns), with contribution to a new approach in development of programming languages. In addition, a large group of programs is examined with the goal of predicting the future development and application of the language patterns. All the presented experiments are performed within a specific domain of programs, providing sample derivation trees.

We propose an incremental fuzzy clustering algorithm for hybrid data discovery. The algorithm is based on the ASM model where data items are represented by agents placed in a two dimensional grid. The agents will group themselves into clusters by making simple moves in their environment. They will try to get closer to each other if they are rather similar or to get away from each other if they are rather different. The algorithm allocates a new agent on the grid whenever a new data item arrives. At each step the new agent contacts an agent from the grid and if they are similar then they will group together in the same cluster. Whenever a new cluster is created the agents will try to merge the cluster with one of the previously created clusters. If a newly created agent does not find a similar fellow then it will start an ASM-like process in order to search for one and thus the data is clustered.

Need of storing a huge amount of data has grown over the past years. Whether data are of multimedia types (e.g. images, audio, or video) or are produced by scientific computation, they should be stored for future reuse or for sharing among users. Users also need their data as quick as possible. Data files can be stored on a local file system or on a distributed file system. Local file system provides the data quickly but does not have enough capacity for storing a huge amount of the data. On the other hand, a distributed file systems (DFS) provide many advantages such as reliability, scalability, security, capacity, etc. In the paper, traditional DFS like AFS, NFS and SMB will be explored. These DFS were chosen because of their frequent usage. Next, new trends in these systems with a focus on increasing performance will be discussed. These include the organization of data and metadata storage, usage of caching, and design of replication algorithms. This paper provides overview of existing algorithms which are used in DFS. Described algorithms can be used as a basis for any future work.

Mining sequential patterns is an important problem in the field of data mining and many algorithms and optimization techniques have been published to deal with that problem. The GSP algorithm, which is one of them, can be used for mining sequential patterns with some additional constraints. In this paper, we propose a new algorithm for mining multi-level sequential patterns based on GSP. The idea is that if a more general item appears in a pattern, the pattern has higher or at least the same support as the one containing the corresponding specific item. However, too generalized sequence patterns are not important for user. In our algorithm generalization uses a selective method based on information content of patterns. This allows us to mine more patterns with the same minimal support threshold and to reveal new potentially useful patterns.

Traffic control is one of the current key issues to increase road safety and to minimize congestion and pollution in urban areas. To achieve optimized traffic control, new traffic models are developed featuring algorithms profiting of different input data for particular city areas. Current advanced traffic simulating systems and modelling tools are based on various traffic models, but they have limited possibilities for modification or replacement of the in-built traffic models and corresponding data inputs. Development of new traffic models encompasses repetitive composition of experiments from different data inputs. It is difficult to create an experiment if larger amount of data has to be input manually into the model. Our object is to create an open environment in which traffic models could be connected to various data inputs and experiments with different settings could be launched and evaluated. To simplify the development process, a system prototype based on Extensible Stylesheet Language configurations is proposed to automate the compositions of experiments. The prototype is tested on a particular traffic control model developed at the Institution of Information Theory and Automation of the Academy of Sciences of the Czech Republic. The prototype allows for testing the model on larger areas (North of Zličín in Prague, Czech Republic) and due to the automation, the duration of the experiment compositions was reduced to seconds.

Medical research is the sphere of interest all around the world, creates and presents new methods for diagnostics and treatment of the patient. Cancer in whatever form is very dangerous, only soon diagnostics and proper treatment can help the patient. The aim of the "Brain tumour detection" project is to detect and locate the anomalies and tumours inside the head. 26 standard markers and unlimited amount of own markers defined in the application allow detecting wide range of anomalies. Tumour can be visualized in 2D, 3D models to show everything strange coloured. It also allows monitoring the progress in time.

This paper discusses transmission channels evaluation, error models and their subsequent simulations for the transmission of video content. To create a network topology and error channel models, NS-2 environment is used. In this paper, we simulated two basic error channel models, namely Gilbert-Elliot and Binary Symmetric channel model. Network traffic in topology is created by NT-MyEvalvid2 toolkit, which also serves to evaluate the data transferred and their subsequent visualization. In paper, we compare the measure of packet loss when using these error models with different input parameters and their impact on the quality of video transmission parameter evaluated by PSNR.

There are several types of ultra wideband (UWB) radar systems which can be used with advantage for the purpose of detection and tracking of persons moving behind obstacles. The intention of this paper is to test in real conditions performance of an impulse UWB radar in comparison with an M-sequence UWB radar for the mentioned application. For that purpose a through-wall measurement with two persons walking in fully furnished room was realized and corresponding signals acquired by different types of UWB radar systems have been consequently processed and analyzed.

Currently, lasers are so powerful that they allow transmitting signal from behind the glass window, thus increasing the possibility of placement and security FSO systems. But still the visibility of transmission and environmental impact of environment on the signal transmitted remains a big problem. This paper deals in detail with a description of the instrument for measuring the density of fog, humidity and temperature of the environment. Long-term measurement of these parameters allows us to create models for transmission FSO environment.

The article deals with the study of hydrogen absorption (hydrogenation) and desorption (dehydrogenation) in amorphous ferromagnetic FeCrB ribbons prepared by rapid quenching from the melt. A simple theoretical model was proposed for description of hydrogen concentration and determination of diffusion coefficient in these materials. Using this model diffusion coefficient can be calculated using experimentally obtained values of average hydrogen concentration in the sample during dehydrogenation process. The values of diffusion coefficients are comparable with those obtained by hydrogen permeation test. The proposed method is not only simple but it also makes possible to determine diffusion coefficient under the same conditions as the changes of other properties of these materials during their dehydrogenation.