National Repository of Grey Literature 14 records found  1 - 10next  jump to record: Search took 0.00 seconds. 
EEG Biofeedback Application
Zapletal, Jakub ; Drahanský, Martin (referee) ; Tinka, Jan (advisor)
Tato práce je shrnutím existujících přístupů pro zpracování EEG signálu za účelem EEG biofeedbacku a dále popisuje návrh a implementaci vlastní aplikace pro EEG biofeedback se zaměřením na trénink pozornosti. Dále obsahuje případovou studii provedenou na neurotypickém studentovi a studentovi s ADHD, která zkoumá vliv implementované aplikace na měřený EEG signál subjektů.
Synthetic Fingerprint Generation from Biometric Template
Šuba, Adam ; Tinka, Jan (referee) ; Kanich, Ondřej (advisor)
The goal of this master thesis is to design and implement an approach for synthetic fingerprint generation from a biometric template. The thesis bases the solution on an existing fingerprint generator called SyFDaS developed at the Brno University of Technology, Faculty of Information Technology. Individual components of the generator had to be modified and automized to suit better the task of generating from a template. The end product enables the user to create a fingerprint without any intervention just by importing a template. The evaluation in this thesis presents results obtained by comparing the synthetic and original fingerprints using the VeriFinger algorithm. Entirely automatically created fingerprints achieved mixed results; however, manual adjustments of the parameters brought substantial improvements. Up to 72% of synthetic fingerprints reached the match by the VeriFinger. The results of the evaluation helped to identify weak points of the current solution. Based on these, the thesis proposes further steps to improve the success rate of automatic generation and the quality of other components.
Typing Using Brain Signals
Wagner, Lukáš ; Malinka, Kamil (referee) ; Tinka, Jan (advisor)
This bachelor thesis focusses on the implementation of a brain-computer interface, programmed in Python language, that would enable to communicate using EEG. The thesis investigates and evaluates existing brain-computer interface technologies for this purpose. The thesis also explores the use of machine learning applied to the technology, in particular neural networks,   which have proven to be one of the most accurate methods of EEG signal processing. Following that, 3 different systems are proposed and implemented, each on different paradigm of visually evoking EEG potential changes. These systems were tested with different signal classification approaches. Unfortunately, none of the systems proved to be useful in communication.
Controlling a Virtual Robot Using a Hybrid Brain-Computer Interface with Visual and Auditory Cues
Prášil, Matěj ; Hrubý, Martin (referee) ; Tinka, Jan (advisor)
This work deals with the control of a virtual robot using a hybrid interface between the brain and a computer in response to visual and auditory evoked potentials, EEG signal analysis and processing. OpenBCI hardware is used for scanning. I studied the methods needed for signal processing and designed applications. The output is two applications, one for controlling a virtual robot and the other for signal processing and classification. The average accuracy of signal classification on real data is low, only 22.35% 
Virtual Robot Control Using EEG
Drla, Michal ; Goldmann, Tomáš (referee) ; Tinka, Jan (advisor)
This bachelor thesis aimed to create an application where is user able to control the virtual robot with an EEG signal. The thesis contains a brief introduction that explains how BCI systems which are using EEG work. This introduction not only explains the basics of EEG analysis but also explains brain biology and shows different signals which are extractable from the brain. This thesis also explains the theory of neural networks which are used to implement the analysis. In implementation are shown scripts that were used to collect data and there is also shown the design of the neural network. Results of testing are good, the neural network was making correct decisions and the user was able to control the virtual robot. 
Person Identification and Verification Using EEG
Žitný, Roland ; Orság, Filip (referee) ; Tinka, Jan (advisor)
The aim of this work was to create a brain-computer interface that reliably identifies and verifies a person using his electroencephalographic signals. Creating a user profile and verifying it is based on processing reactions to his own face, and the face of strangers or acquaintances. Algorithms such as bandpass and noise removal using wavelet transformation are user to filter signals. The classification of reactions is performed using a convolutional neural network or linear discriminant analysis. The average accuracy of the linear discriminant analysis is 66.2 % and of the convolutional neural network is 58.7 %. The maximum achieved accuracy was with linear discriminant analysis and at 93.7 %.
Detection and Quality Improvement of Face Objects in Low-Quality Source Images
Šoltis, Richard ; Tinka, Jan (referee) ; Drahanský, Martin (advisor)
The aim of this thesis was to construct an algorithm for the detection of human face from poor quality source images and subsequently improving the image of human face. The result of the work is an application with a graphical interface which detects human face objects from the input images and then improves these inherited faces from the point of quality and size. When creating the application, current techniques and algorithms such as neuron networks were used. They formed the basis for detection and image improvement, S3FD detection and last but not least the GAN network to improve the image. Part of the thesis is testing the individual parts of the application in predefined scenarios as well as testing a comprehensive run application.
Depth-Based Determination of a 3D Hand Position
Ondris, Ladislav ; Tinka, Jan (referee) ; Drahanský, Martin (advisor)
Cílem této práce je určení kostry ruky z hloubkového obrazu a jeho následné využití k rozpoznání statického gesta. Na vstupu je hloubkový obrázek, ve kterém je nejprve detekována ruka pomocí neuronové sítě Tiny YOLOv3. Následně je obrázek zbaven pozadí a z takto předzpracovaného obrázku je určena kostra ruky v podobě 21 klíčových bodů neuronovou sítí JGR-P2O. K rozpoznání gesta z klíčových bodů ruky byla navržena technika, která porovná kostru na vstupu s uživatelem definovanými gesty. Funkcionalita systému byla otestována na vytvořeném datasetu s více než čtyřmi tisíci obrázky.
Ordering of Jobs for Pickling Lines
Plšek, Michal ; Tinka, Jan (referee) ; Kanich, Ondřej (advisor)
This work resolves the scheduling problem of multiple hoists transporting products between chemicals baths of pickling line. Harmonograms of products are calculated by modified Shifting bottleneck heuristic, which prevents product conflicts inside baths. Genetic algorithm NSGA-II is used for solution-space search. Web application built over the optimization process allows user to manage/edit products, hoists, baths, configuration parameters and optimization results. Applying proposed heuristic to smaller optimization tasks boosts production effectivity up to 30-45 % (comparing to naive harmonograms). The result of this work is application on the basis of which full-fledged C++ application might be programmed. Then it might be used for solving larger-scale problems.
Mapping of the Pedestrian Movement Trajectory in a Video Recording Captured by a Drone
Šťastný, Filip ; Tinka, Jan (referee) ; Orság, Filip (advisor)
This master's thesis deals with pedestrian detection using neural networks in a video record captured by drone. Pedestrians are tracked, and their GPS coordinates are calculated using digital elevation models and mapped based on their identity and an information provided by the drone.

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