This thesis attempts to find an alternative method for biometric identification using retinal images. First part is focused on the introduction to biometrics, human eye anatomy and methods used for retinal biometry. The essence of neural networks and deep learning methods is described as it will be used practically. In the last part of the thesis a chosen identification algorithm and its implementation is described and the results are presented.

This work deals with regulation and procedures that can be used for this. The theoretical part therefore presents the basic concepts of automation and controllers. The main part is the practical part. The purchased system consists of a regulated system, which must be set up correctly. Using system identification and known procedures, sample examples of how to best set the controller are compiled. The result is a sample laboratory task that automation students can try and gain experience with methods of setting up the controller.

This thesis focuses on the possibility of using current methods in the field of computer vision to re-identify graffiti tags. The work examines the possibility of using convolutional neural networks to re-identify graffiti tags, which are the most common type of graffiti. The work experimented with various models of convolutional neural networks, the most suitable of which was MobileNet using the triplet loss function, which managed to achieve a mAP of 36.02%.

In this doctoral thesis, the design of algorithms enabling the implementation of a fully automatic system for vertebrae segmentation in 3D computed tomography (CT) image data of possibly incomplete spines, in patients with bone metastases and vertebral compressions is presented. The proposed algorithm consists of several fundamental problems: spine detection and its axis determination, individual vertebra localization and identification (labeling), and finally, precise segmentation of vertebrae. The detection of the spine, specifically identifying its ends, and determining the course of the spinal canal, combines several advanced methods, including deep learning-based approaches. A novel growing circle method has been designed for tracing the spinal cord canal. Further, the innovative spatially variant filtering of brightness profiles along the spine axis leading to intervertebral disc localization has been proposed and implemented. The discs thus obtained are subsequently identified via comparing the tested vertebrae and model of vertebrae provided by a machine-learning process and optimized by dynamic programming. The final vertebrae segmentation is provided by the deformation of the complete-spine intensity model, utilizing a proposed multilevel registration technique. The complete proposed algorithm has been validated on testing databases, including also publicly available datasets. This way, it has been proven that the newly proposed algorithms provide results at least comparable to other author’s algorithms, and in some cases, even better. The main strengths of the algorithms lie in high reliability of the results and in the robustness to even strongly distorted vertebrae of oncological patients and to the occurrence of artifacts in data; moreover, they are capable of identifying the vertebra labels even in incomplete spinal CT scans. The strength is also in the complete automation of the processing and in its relatively low computational complexity enabling implementation on standard PC hardware. The system for fully automatic localization and labeling of distorted vertebrae in possibly incomplete spinal CT data is presented in this doctoral thesis. The design of algorithms enabling the implementation utilizes several novel approaches, which were presented at international conferences and published in the journal Jakubicek et al. (2020). Based on the results of the experimental validation, the proposed algorithms seem to be routinely usable and capable of providing fully acceptable input data (identified and precisely segmented vertebrae) as needed in the subsequent automatic spine bone lesion analysis.

This diploma thesis deals with description of the principle of Li-ion cells, literature search on the topic of mathematical models of Li-ion cells and the creation of a selected mathematical model in MATLAB, which is able to simulate the course of voltage and state of charge as a function of time for different ambient conditions, such as various aging of battery .The creation of both the model and the procedure of identification of parameters necessary for the creation of the model are described here as well as different options of identification of parameters. The selected Thevenin model is then compared with the real course and the accuracy of the model is evaluated with respect to the measured course.

The diploma thesis deals with legislative purposes for the operation of machinery in the Czech Republic and the European Union. It deals with the analysis of technical standards of machinery, specific machining centers. The analysis of the risky machining center NHX 5500 DMG Mori is based on this. The result of the work is the minimization of ongoing risks and meeting the legal requirements of the state of the operated machining center.

Tato práce se zabývá vytvořením simulačního modelu dvojčinného pneumatického pístu s mechanickou sestavou, včetně modelů snímačů, s následujícím odhadem parametrů a aproximací chování demonstračního zařízení. Dalším cílem je prezentace různých přístupů prediktivní údržby na datové sadě měřené na demonstračním zařízení. Na měřený datový soubor se aplikovaly signal-based techniky bez použití simulačního modelu a model-based metody, které vyžadují použití simulačního modelu. Výsledkem této práce je ověření možnosti monitorování stavu zařízení pomocí nainstalovaných senzorů a vyhodnocení efektivity senzorů z hlediska přesnosti a finančních nákladů.

Current systems for counting cars on parking lots usually use specialized equipment, such as barriers at the parking lot entrance. Usage of such equipment is not suitable for free or residential parking areas. However, even in these car parks, it can help keep track of their occupancy and other data. The system designed in this thesis uses the YOLOv4 model for visual detection of cars in photos. It then calculates an embedding vector for each vehicle, which is used to describe cars and compare whether the car has changed over time at the same parking spot. This information is stored in the database and used to calculate various statistical values like total cars count, average occupancy, or average stay time. These values can be retrieved using REST API or be viewed in the web application.

The diploma thesis is focused on the identification of degradation products of organic dye Saturn Red L4B resulting by degradation in diaphragm discharge, using separation techniques with mass spectrometric detection.

The purpose of this work was realization and implementation of two applications for the management of Radio Data System (RDS) messages. These applications will be subsequently used in the Czech Radio Ostrava to support two RDS services, namely the Traffic - Announcement Identification and Radiotext. Both applications are secured against unauthorized use by incompetent users through a password - protected access, access rights and encryption of passwords in initialization files. The text of this work is divided into three parts. The first describes the system RDS and services provided by it and implementation of RDS in the Czech Radio Ostrava. The second part deals with the realization of both applications and the last part describes the implementation itself. The work shows all applications windows. Work also includes all the flow charts. The list of all broken down by subroutines is given in the appendix.