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Programmable MIDI Controller
Kolařík, Martin ; Lattenberg, Ivo (referee) ; Schimmel, Jiří (advisor)
This bachelor‘s thesis analyzes a designing a programmable MIDI controller. Theoretical part describes the MIDI protocol, its structure and usage. It also focuses on attributes of used components, mainly the ATmega32-16PU processor and liquid crystal display with HD44780 controller. Practical part of the project focuses on designing an electronic scheme of MIDI controller, its printed circuit board and it describes used program. For programming was used ISP programmer Biprog and as development platform Atmel studio 6.1. Finished controller is able to send MIDI message with every users action, including system messages. It is programmable using computer generated System Exclusive MIDI messages and it contains a soft-thru function.
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Neural network generator for image similarity measurement
Hipča, Tomáš ; Kolařík, Martin (referee) ; Burget, Radim (advisor)
This thesis deals with designing an automatic generator of deep neural networks for image classification. Theoretical part clarifies what a neural network and formal neuron are. Furthermore, the types of neural network architectures are presented. The focus of this thesis is convolutional neural networks, several pieces of research from this field are mentioned. The practical part of this thesis describes information with regards to the implementation of neural network generator, possible frameworks and programming languages for such implementation. Brief description of the implementation itself is presented as well as implemented layers. Generated neural networks are tested on Google-Landmarks dataset and results are commented upon.
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A tool for generating a random configuration of a cyber arena
Matisko, Maroš ; Kolařík, Martin (referee) ; Uher, Václav (advisor)
The master's thesis is focused on the design and implementation of a tool for generating configuration named Ansible. The result of using this tool is generated configuration, which contains random values chosen according to specified parameters and it was deployed on a virtual testing infrastructure. The theoretical part describes approaches of network automation in the process of deploying and configuration of network devices called Infrastructure as code. It also describes programme Ansible, which will be using the output of the implemented tool. The practical part of the thesis is focused on designing the functionality and internal structure of the tool, implementation of the tool and testing implemented tool as well as generated configuration.
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Dataset generation for specific cases of face recognition
Kolmačka, Tomáš ; Kolařík, Martin (referee) ; Rajnoha, Martin (advisor)
The diploma thesis deals with current problems of person identification and deep learning. Furthermore, the work deals mainly with obtaining quality and diverse data that are used to train deep learning with convolutional neural networks for face recognition. There is very little public access to such data, so the practical part focuses on creating the MakeHuman plugin that will generate a database of random face images. It is possible to generate faces according to five different scenarios in which purely random faces or faces where the same can be seen with modifications such as different hair, beard, hat, glasses and more are created. The scenarios also allow you to generate faces with some expressions or faces as they age. You can set some parameters that give the appearance of the resulting database in the plugin. This can include face images from different angles of rotation, zooming and lighting.
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Deep neural network for supercomputer environments
Bronda, Samuel ; Kolařík, Martin (referee) ; Burget, Radim (advisor)
The main benefit of the work is the optimization of the hardware configuration for the calculation of neural networks. The theoretical part describes neural networks, deep learning frameworks and hardware options. The next part of the thesis deals with implementation of performance tests, which include application of Inception V3 and ResNet models. Network models are applied to various graphics cards and computing hardware. The output of the thesis is the implemented model of the network Inception V3, which examines the graphics cards and their performance, time-consuming calculations and their efficiency. The ResNet model is applied to a section that examines other impacts on neural network computing such as used disk, operating memory, and so on. Each practical part contains a discussion where the knowledge of the given part is explained. In the case of consumption measurement, a mismatch between the declaration by the manufacturer and the measured values was identified.
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Image segmentation methods with limited data sets
Horečný, Peter ; Kolařík, Martin (referee) ; Burget, Radim (advisor)
The goal of this thesis was to propose an image segmentation method, which is capable of effective segmentation process with small datasets. Recently published ODE neural network was used for this method, because its features should provide better generalization in case of tasks with only small datasets available. The proposed ODE-UNet network was created by combining UNet architecture with ODE neural network, while using benefits of both networks. ODE-UNet reached following results on ISBI dataset: Rand: 0,950272 and Info: 0,978061. These results are better than the ones received from UNet model, which was also tested in this thesis, but it has been proven that state of the art can not be outperformed using ODE neural networks. However, the advantages of ODE neural network over tested UNet architecture and other methods were confirmed, and there is still a room for improvement by extending this method.
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System for 3D data visualisation in virtual reality
Kalafut, Oliver ; Mašek, Jan (referee) ; Kolařík, Martin (advisor)
This bachelor thesis deals with the imaging of medical models, for example human body organs, in virtual reality via Oculus Go. Oculus Go is an all-in-one device with a powerful processor and high-resolution display that is ideal for this bachelor thesis. The main goal is the conversion of 2D and 3D medical data formats, including formats such as DICOM and NIfTI, into 3D formats usable for virtual reality and then create the application. The first part of this work is devoted to the theoretical introduction and introduces the issues of virtual reality and 3D modelling to the reader. Then in the practical part, there was implemented a standalone (offline) application for display and interaction of used medical models with users in the virtual reality environment. In total, eight models of different parts of the human body were processed and converted to a uniform 3D Object (.obj) format. Subsequently, they were imported into program Unity, in which I created the entire application environment called Model Preview VR. The application enables viewing, zooming, rotating, and cross-section features of individual objects and is suitable for the presentation of simple models. This application can be helpful for development of not only medical imaging, but also for getting quality photos for publishing.
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Reinforcement learning for solving game algorithms
Daňhelová, Jana ; Uher, Václav (referee) ; Kolařík, Martin (advisor)
The bachelor thesis Reinforcement learning for solving game algorithms is divided into two distinct parts. The theoretical part describes and compares the fundamental methods of reinforcement learning with special attention to the methods of active learning – Q-learning and deep learning. In the practical part the deep q-learning technique is chosen for testing and applied to the case of the Snake game. The results are presented in the form of program written in Python programming language, which consists of the game environment created in PyGame, the model of convolutional neural network designed in Keras and agent playing the game. As an output of the program there are several types of datasets in CSV format. The gained data containing the values of parameters like number of epochs, accuracy, loss or the amount of the reward can later be used for further processing.
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The effect of the background and dataset size on training of neural networks for image classification
Mikulec, Vojtěch ; Kolařík, Martin (referee) ; Rajnoha, Martin (advisor)
This bachelor thesis deals with the impact of background and database size on training of neural networks for image classification. The work describes techniques of image processing using convolutional neural networks and the influence of background (noise) and database size on training. The work proposes methods which can be used to achieve faster and more accurate training process of convolutional neural networks. A binary classification of Labeled Faces in the Wild dataset is selected where the background is modified with color change or cropping for each experiment. The size of dataset is crucial for training convolutional neural networks, there are experiments with the size of training set in this work, which simulate a real problem with the lack of data when training convolutional neural networks for image classification.
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Automated Hydroponic System
Borsuk, Adam ; Kolařík, Martin (referee) ; Číka, Petr (advisor)
The aim of the bachelor thesis is to study the design and creation of an automatic hydroponic system for plant cultivation and to solve the creation of components of the system according to the basic conditions for plant growth and subsequently to test and verify their properties, to evaluate their functionality. The second goal is to create a communication interface for sending and storing data from the system while creating a transparent display of stored and up-to-date data. The third objective is to verify the functionality and stability of the selected microcontroller as a control unit.
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