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.

Image Filter Design with Evolvable Hardware

Abstract: The paper introduces a new approach to automatic design of image filters for a given type of noise. The approach employs evolvable hardware at simplified functional level and produces circuits that outperform conventional designs. If an image is available both with and without noise, the whole process of filter design can be done automatically, without influence of a designer.

Study of probabilistic and Bottle-Neck features in multilingual environment

Abstract: This study is focused on the performance of Probabilistic and Bottle-Neck features on different language than they were trained for. It is shown, that such porting is possible and that the features are still competitive to PLP features. Further, several combination techniques are evaluated. The performance of combined features is close to the best performing system. Finally, bigger NNs were trained on large data from different domain. The resulting features outperformed previously trained systems and combination with them further improved the system performance.

Thermal removal of multicomponent binder from ceramic injection mouldings

Abstract: Thermal removal of polymer binder containing low-molecular-weight components from ceramic injection mouldings was studied. The effect of the shape and size of bodies on the process of binder removal was examined. Evaporation of low-molecular-weight components represented the most important process at the beginning of binder removal. It was found that a bed of activated carbon speed up removal of low-molecular-weight components. The mechanism of binder removal in a bed of activated carbon was described. Binder redistribution and evolution of porosity in the body during binder removal was investigated. A high rate of binder removal resulted in non-uniform binder distribution in the body. The formation of defects due to non-uniform binder removal was described and requirements for their elimination were proposed.

A graph-based CNN-LSTM stock price prediction algorithm with leading indicators

Abstract: In today’s society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, and many others. Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. Among them, in investment and financial management, people’s favorite product of investment often stocks, because the stock market has great advantages and charm, especially compared with other investment methods. More and more scholars have developed methods of prediction from multiple angles for the stock market. According to the feature of financial time series and the task of price prediction, this article proposes a new framework structure to achieve a more accurate prediction of the stock price, which combines Convolution Neural Network (CNN) and Long–Short-Term Memory Neural Network (LSTM). This new method is aptly named stock sequence array convolutional LSTM (SACLSTM). It constructs a sequence array of historical data and its leading indicators (options and futures), and uses the array as the input image of the CNN framework, and extracts certain feature vectors through the convolutional layer and the layer of pooling, and as the input vector of LSTM, and takes ten stocks in U.S.A and Taiwan as the experimental data. Compared with previous methods, the prediction performance of the proposed algorithm in this article leads to better results when compared directly.

Nanoscale Heterostructures Based on Fe2O3@WO3-x Nanoneedles and Their Direct Integration into Flexible Transducing Platforms for Toluene Sensing

Abstract: Nanoscale heterostructures based on WO3–x nanoneedles functionalized with Fe2O3 nanoparticles are integrated directly into flexible polymer-based transducing platforms via aerosol-assisted chemical vapor deposition. Results demonstrate that the incorporation of Fe2O3 nanoparticles at the surface of WO3–x nanoneedles enhances the electronic and sensing properties of WO3–x, providing a 6-fold increase in sensitivity to toluene and low cross-sensitivity to hydrogen and ethanol. These enhanced-sensing properties are comparable to those obtained via functionalization with precious metal (Pt) nanoparticles, which are commonly used to enhance sensor performance.