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: Computer science & Fracture mechanics. 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.

Optical trapping of nanoparticles and microparticles by a Gaussian standing wave.

Abstract: The optical trapping of nanoparticles and microparticles by a Gaussian standing wave is experimentally demonstrated for the first time to the authors' knowledge. The standing wave is obtained under a microscope objective as a result of the interference of an incoming laser beam and a beam reflected on a microscope slide that has been coated with a system of reflective dielectric layers. Experimental results show that three-dimensional trapping of nanoparticles (100-nm polystyrene spheres) and one or more vertically aligned micro-objects (5-mum polystyrene spheres, yeast cells) can easily be achieved by use of even highly aberrated beams or objectives with low numerical apertures.

PMSM Model Predictive Control With Field-Weakening Implementation

Abstract: Permanent-magnet synchronous machine (PMSM) drives have become popular for motion control applications due to their performance and high torque-to-weight ratio. The complex task of PMSM control in high-performance applications is currently usually resolved with classical vector control. Modern control techniques such as model predictive control (MPC) can provide significant benefits over field-oriented control, especially with straightforward controller tuning and constraints handling. Unfortunately, these new algorithms usually suffer from problems with their computational complexity. In this paper, we present a PMSM control method based on an explicit MPC with a novel linearization and constraints handling method, allowing natural field weakening. The algorithm was designed with respect to computationally feasible implementation in the control hardware. The proposed control algorithm has been proved and successfully verified in both simulation and implementation on a real motor.

Decision Support Framework for Parkinson’s Disease Based on Novel Handwriting Markers

Abstract: Parkinson’s disease (PD) is a neurodegenerative disorder which impairs motor skills, speech, and other functions such as behavior, mood, and cognitive processes. One of the most typical clinical hallmarks of PD is handwriting deterioration, usually the first manifestation of PD. The aim of this study is twofold: (a) to find a subset of handwriting features suitable for identifying subjects with PD and (b) to build a predictive model to efficiently diagnose PD. We collected handwriting samples from 37 medicated PD patients and 38 age- and sex-matched controls. The handwriting samples were collected during seven tasks such as writing a syllable, word, or sentence. Every sample was used to extract the handwriting measures. In addition to conventional kinematic and spatio-temporal handwriting measures, we also computed novel handwriting measures based on entropy, signal energy, and empirical mode decomposition of the handwriting signals. The selected features were fed to the support vector machine classifier with radial Gaussian kernel for automated diagnosis. The accuracy of the classification of PD was as high as 88.13%, with the highest values of sensitivity and specificity equal to 89.47% and 91.89%, respectively. Handwriting may be a valuable marker as a diagnostic and screening tool.

Developing a Speech Activity Detection System for the DARPA RATS Program.

Abstract: This paper describes the speech activity detection (SAD) system developed by the Patrol team for the first phase of the DARPA RATS (Robust Automatic Transcription of Speech) program, which seeks to advance state of the art detection capabilities on audio from highly degraded communication channels. We present two approaches to SAD, one based on Gaussian mixture models, and one based on multi-layer perceptrons. We show that significant gains in SAD accuracy can be obtained by careful design of acoustic front end, feature normalization, incorporation of long span features via data-driven dimensionality reducing transforms, and channel dependent modeling. We also present a novel technique for normalizing detection scores from different systems for the purpose of system combination.

Virtual reconfigurable circuits for real-world applications of evolvable hardware

Abstract: The paper introduces a new method for the design of real-world applications of evolvable hardware using common FPGAs (Field Programmable Gate Arrays). In order to avoid "reconfiguration problems" of current FPGAs a new virtual reconfigurable circuit, whose granularity and configuration schema exactly fit to requirements of a given application, is designed on the top of an ordinary FPGA. As an example, a virtual reconfigurable circuit is constructed to speed up the software model, which was utilized for the evolutionary design of image operators.