Brno University of Technology

About: Brno University of Technology is a education organization based out in Brno, Czechia. It is known for research contribution in the topics: Fracture mechanics & Filter (video). The organization has 6339 authors who have published 15226 publications receiving 194088 citations. The organization is also known as: Vysoké učení technické v Brně & BUT.

Influence of the doping method on the sensitivity of Pt-doped screen-printed SnO2 sensors

Abstract: In this work, we study the influence of the introduction method of Pt atoms on the sensitivity to traces of ethanol of Pt-doped SnO2 sensors. The tin oxide films were obtained by a screen-printing process. Two different methods were employed to introduce Pt atoms on SnO2 films. In the first one, the Pt atoms were added to the screen-printed tin oxide layer by using RF magnetron sputtering and a subsequent thermal treatment. The second method consisted of mixing SnO2 and Pt pastes before the screen-printing process. The different active layers (including un-doped tin oxide) were carefully examined relative to their sensitivity to ethanol at different working temperatures. Sensors prepared by the second method showed sensitivity to ethanol four times higher than one of the sensors prepared by the first method and 12 times higher than un-doped sensors. XPS and scanning electron microscopy (SEM) measurements showed that this behaviour could be associated with the spatial distribution of the doping elements within the tin oxide film. While in Pt-sputtered sensors most of the Pt atoms were found at the surface of the active layer, for the sensors made by mixing Pt and SnO2 pastes, a homogeneous distribution of the Pt atoms was observed. These sensors show high sensitivity and fast response time to ethanol vapours, with a detection limit in the ppb range.

Novel method for localization of common carotid artery transverse section in ultrasound images using modified Viola-Jones detector.

Abstract: This article describes a novel method for highly accurate and effective localization of the transverse section of the carotis comunis artery in ultrasound images. The method has a high success rate, approximately 97%. Unlike analytical methods based on geometric descriptions of the object sought, the method proposed here can cover a large area of shape variation of the artery under study, which normally occurs during examinations as a result of the pressure on the examined tissue, tilt of the probe, setup of the sonographic device, and other factors. This method shows great promise in automating the process of determining circulatory system parameters in the non-invasive clinical diagnostics of cardiovascular diseases. The method employs a Viola-Jones detector that has been specially adapted for efficient detection of transverse sections of the carotid artery. This algorithm is trained on a set of labeled images using the AdaBoost algorithm, Haar-like features and the Matthews coefficient. The training algorithm of the artery detector was modified using evolutionary algorithms. The method for training a cascade of classifiers achieves on a small number of positive and negative training data samples (about 500 images) a high success rate in a computational time that allows implementation of the detector in real time. Testing was performed on images of different patients for whom different ultrasonic instruments were used under different conditions (settings) so that the algorithm developed is applicable in general radiologic practice.

An Information Analysis of In-Air and On-Surface Trajectories in Online Handwriting

Abstract: This paper is aimed at analysing, from an information theory perspective, the gestures produced by human beings when handwriting a text. Modern capturing devices allow the gathering of data not only from the on-surface movements of the hand, but also from the in-air trajectories performed when the hand moves in the air from one stroke to the next. Our past research with isolated uppercase words clearly suggests that both types of trajectories have a biometric potential to perform writer recognition and that they can be effectively combined to enhance the recognition accuracy. With samples from the BiosecurID database, we have analysed the entropy of each kind of trajectories, as well as the amount of information they share, and the difference between intra- and inter-writer measures of the mutual information. The results show that when pressure is not taken into account, the amount of information is similar in both types of trajectories. Furthermore, even if they share some information, in-air and on-surface trajectories appear to be notably non-redundant.