In this paper the results of numerical simulation of neurotransmitter fast transport in a presynaptic bouton of a biological neuron are presented. A mathematical model governing the transport that is fully described in [2] is recalled. A numerical simulation scheme, used parameters and their origins are described. Finally, the results of the simulation are presented.

In this paper, we place some left restrictions on derivations in CD grammar systems with phrase- structure grammars, controlled by the regular languages. The rst restriction requires that every production is always applied within the rst k nonterminals in every sentential form, for some k  1. The second restriction says how many blocks of non-terminals can be in each sentential form. The third restriction extends the second restriction and says how many blocks of non-terminals with limited length can be in each sentential form. We demonstrate that under these restrictions, the grammar systems generate dierent families of languages. Indeed, under the rst restriction, these systems generate only context-free languages. Under the second restriction, even one-component systems characterize the entire family of recursively enumerable languages. In the end, the family of languages generated by grammar systems under the third restriction is equal to the family of languages generated by programmed grammars with context-free rules without -rules of nite index.

This work is motivated by the ˇCern´y Conjecture – an old unsolved problem in the automata theory. We describe the results of the experiments on synchronizing automata, which have led us to two interesting results. The first one is that the size of an automaton alphabet may play an important role in the issue of synchronization: we have found a 5-state automaton over a 3-letter alphabet which attains the upper bound from the ˇCern´y Conjecture, while there is no such automaton (except ˇCern´y automaton C5) over a binary alphabet. The second result emerging from the experiments is a theorem describing the dependencies between the automaton structure S expressed in terms of the so-called merging system and the maximal length of all minimal synchronizing words for automata of type S.

This paper presents several normalization techniques used in handwritten numeral recognition and their impact on recognition rates. Experiments with five different feature vectors based on geometric invariants, Zernike moments and gradient features are conducted. The recognition rates obtained using combination of these methods with gradient features and the SVM-rbf classifier are comparable to the best state-of-art techniques.

A field of science such as artificial neural networks (ANN) arouses more and more interest all over the world, not only among academics and researchers. Since its beginning in the 1940‟ties it has experienced its ups and downs. From the huge fascination in the early stage to nearly falling into oblivion after Minsky‟s book [10]. Then in the 80‟ties interest in ANN experienced a rapid growth which surprised everyone and resulted in a dramatic rise and demand for information about ANN. This interest is also a result of a very dynamic development of computer science in the recent years as well as increasing need for iterative computational models, to which most definitely we can include ANN.

The idea of the project is to trace the robot position and orientation and precise it by the implementation of the advanced algorithms based on triangulation and trilateration methods. Precise navigation is a difficult task since the odometry sensors used in this context are subject to systematic and random errors. The former ones result from inaccuracies in the mechanical construction of the vehicles, e.g. the wheel diameters can be unequal or the nominal direction of the wheels is different from real.