273 SummaryThe objective of this study was to determine the possibility of soil contamination with propagative stages of intestinal endoparasites at different public places (public parks, playgrounds, sandpits, sidewalks, road sides). In a oneyear-study, totally 578 dog's faecal samples from 8 towns (Košice, Trebišov, Veľké Kapušany, Prešov, Snina, Levoča, Zvolen and Trenčín) and 3 villages (Dlhé Stráže, Dravce, Valaliky) were examined for the presence of parasitic germs. 29.9 % of faecal samples were positive. Eight different species of intestinal parasites with following incidence were detected: Toxocara canis (11.9 %), Trichuris vulpis (8.5 %), Ancylostomatidae (8.1 %), Taenia spp. varied between the urban and rural environments. The highest number of Toxocara spp. was found in village Valaliky (33.3 %) whereas cities showed lesser prevalence (Prešov -10 %, Košice -7.4 %, Trenčín -5.6 %, Zvolen -5.3 %, Veľké Kapušany -4.2 %). The epidemiological aspects and health risk factors are also discussed.

The SCORPIO is a small-size mini-teleoperator mobile service robot for booby-trap disposal. It can be manually controlled by an operator through a portable briefcase remote control device using joystick, keyboard and buttons. In this paper, the speech interface is described. As an auxiliary function, the remote interface allows a human operator to concentrate sight and/or hands on other operation activities that are more important. The developed speech interface is based on HMM-based acoustic models trained using the SpeechDatE-SK database, a small-vocabulary language model based on fixed connected words, grammar, and the speech recognition setup adapted for low-resource devices. To improve the robustness of the speech interface in an outdoor environment, which is the working area of the SCORPIO service robot, a speech enhancement based on the spectral subtraction method, as well as a unique combination of an iterative approach and a modified LIMA framework, were researched, developed and tested on simulated and real outdoor recordings.

An efficient acoustic events detection system EAR-TUKE is presented in this paper. The system is capable of processing continuous input audio stream in order to detect potentially dangerous acoustic events, specifically gunshots or breaking glass. The system is programmed entirely in C++ language (core math. functions in C) and was designed to be self sufficient without requiring additional dependencies. In the design and development process the main focus was put on easy support of new acoustic events detection, low memory profile, low computational requirements to operate on devices with low resources, and on long-term operation and continuous input stream monitoring without any maintenance. In order to satisfy these requirements on the system, EAR-TUKE is based on a custom approach to detection and classification of acoustic events. The system is using acoustic models of events based on Hidden Markov Models (HMMs) and a modified Viterbi decoding process with an additional module to allow continuous monitoring. Cepstral Mean Normalization (CMN) and our proposed removal of basic coefficients from feature vectors to increase robustness. This paper also presents the development process and results evaluating the final design of the system.

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