Cílem práce je shrnout základní údaje týkající se myelinizace mozku dítěte v různých obdobích zrání mozku s určením časových milníků usnadňujících její praktické hodnocení. V další řadě se autoři zabývají možnostmi zobrazení procesu myelinizace mozku v dětském věku ve vztahu k její dynamice, variabilitě a souvisejícím patologickým nálezům především v zobrazení pomocí magnetické rezonance., The goal is to summarize the basic data relating to the myelination of child's brain at different periods of the maturation of the brain, identifying the time milestones to facilitate its practical evaluation. In the next series, the authors display options of imaging the process of myelination of the brain in children in relation to its dynamics, variability and related pathological findings by the magnetic resonance mainly., Martin Kynčl, Blanka Prosová, Eliška Mlynářová, Martina Ptáčníková, and Literatura
The Kohonen Self-organizing Feature Map (SOM) has been developed for clustering input vectors and for projection of continuous high-dimensional signal to discrete low-dimensional space. The application area, where the map can be also used, is the processing of text documents. Within the project WEBSOM, some methods based on SOM have been developed. These methods are suitable either for text documents information retrieval or for organization of large document collections. All methods have been tested on collections of English and Finnish written documents. This article deals with the application of WEBSOM methods to Czech written documents collections. The basic principles of WEBSOM methods, transformation of text information into the real components feature vector and results of documents classification are described. The Carpenter-Grossberg ART-2 neural network, usually used for adaptive vector clustering, was also tested as a document categorization tool. The results achieved by using this network are also presented.
The article presents a new rnethodology concerning the GPS signals
Processing and shows the signál pre-processing the influence on the quality of the prediction error. The next paraineter, which qualified the model quality, is exponential forgetting. For slowly tinie dependent models the exponential forgetting is approximately 0.98 - 0.99. The lower forgetting value points out the time varying model which is not usable for our modelling application. At the end of the article we achieved model for GPS signals with the appropriate prediction errors and with adequate exponential forgetting. AU theoretical results are practically applied on reál GPS signals and the achieved accuracy is much better according to the raw measured data.