Chiral model for KˉN\bar{K}N interactions and its pole content

We use chirally motivated effective meson-baryon potentials to describe the low energy $\bar{K}N$ data including the characteristics of kaonic hydrogen. Our results are examined in comparison with other approaches based on the unitarity and dispersion relation for the inverse of the T-matrix. We demonstrate that the movements of the poles generated by the model upon varying the model parameters can serve as a tool to get additional insights on the dynamics of the strongly coupled $\pi\Sigma$-$\bar{K}N$ system.Comment: contribution to the Chiral10 workshop, Valencia (Spain), June 21-24, 201

SIDDHARTA impact on KˉN\bar{K}N amplitudes used in in-medium applications

We have performed new fits of our chirally motivated coupled--channels model for meson-baryon interactions and discussed the impact of the SIDDHARTA measurement on the $\bar{K}N$ amplitudes in the free space and in nuclear medium. The kaon--nucleon amplitudes generated by the model are fully consistent with our earlier studies that used the older kaonic hydrogen data by the DEAR collaboration. The subthreshold energy dependence of the in-medium $\bar{K}N$ amplitudes plays a crucial role in $\bar{K}$--nuclear applications.Comment: 4 pages, published in Proceedings of the MESON 2012 conference, Cracow, Poland, May 31 - June 5, 201

Chiral Lagrangians and the transition amplitude for radiative muon capture

The transition operator for the radiative capture of mesons mu minus by protons is constructed starting from a chiral Lagrangian of the N-pi-rho-a_1-omega system obtained within the approach of hidden local symmetries. The transition operator is gauge invariant and satisfies exactly the CVC and PCAC equations.Comment: 11 pages, 1 figure, LaTex, feynman, submitted to Few-Body System

Handwritten Character Recognition Using Artificial Neural Networks

Práce se zabývá rozpoznáváním velkých písmen a číslic hůlkového písma pomocí neuronových sítí. Rozebírá použité algoritmy pro segmentaci textu, metody extrakce příznaků a problematiku učení sítě pomocí zpětného šíření chyb. Jsou zde také popsány provedené experimenty s různými konfiguracemi nad datovými sadami. Pro trénování nových sítí a testování jejich úspěšnosti byla vytvořena demonstrační aplikace s grafickým rozhraním s možností interaktivního rozpoznávání myší psaného textu.Thesis deals with handwritten block letters and digits recognition using artificial neural networks. Text segmentation algorithms, feature extraction methods and backpropagation learning are explained. There are also described performed experiments with variety of configurations on datasets. Application with graphical user interface and interactive mouse-written text recognition was created to train new neural networks and test their effectivity.