Computational Science and Techniques

This paper describes a concept of making interactive human state recognition systems based on smart sensor design. The token measures on proper ADC signal processing had significantly lowered the interference level. A more reliable way of measuring human skin temperature was offered by using Maxim DS18B20 digital thermometers. They introduced a more sensible response to temperature changes compared to previously used analog LM35 thermometers. An adaptive HR measuring algorithm was introduced to suppress incorrect ECG signal readings caused by human muscular activities. User friendly interactive interface for touch sensitive GLCD screen was developed to present real time physiological data readings both in numerals and graphics. User was granted an ability to dynamically customize data processing methods according to his needs. Specific procedures were developed to simplify physiological state recording for further analysis. The introduced physiological data sampling and preprocessing platform was optimized to be compatible with “ATmega Oscilloscope” PC data collecting and visualizing software.

In technical photography, there are cases with requirement to keep full similarity among the object and its reflection. Due to the aberration of optical systems used in photography tools, unambiguous and precise application of projecting geometry rules becomes impossible. The problem remains in digital photography with traditional optical devices and image processing with software tools is complicated due to the both insensitivity of image matrixes and the lack of suitable correction algorithms. The study has shown that the best results can be obtained by using stenocamera and sensitive photographic film. New digital image retrieval method, obtained by combining classical stenocamera tool with digital web camera matrix is described in this paper.

Spatial statistics is one of the fields in statistics dealing with spatialy spread data analysis. Recently, Bayes methods are often applied for data statistical analysis. A spatial data model for predicting algae quantity in the Baltic Sea is made and described in this article. Black Carrageen is a dependent variable and depth, sand, pebble, boulders are independent variables in the described model. Two models with different covariation functions (Gaussian and exponential) are built to estimate the best model fitting for algae quantity prediction. Unknown model parameters are estimated and Bayesian kriging prediction posterior distribution is computed in OpenBUGS modeling environment by using Bayesian spatial statistics methods.