This thesis describes development of autonomous car for Carrera 143 racing track. Main objective of a car is to stop when traffic light shows red, or when there is an obstacle infront of a car. This paper also describes electric schemes used to control the car and their placement on the car. Algorithms developed for image processing are developed for processing unit Raspberry Pi Zero and are written in C/C++ programming language. OpenCV library is used for image processing. All source codes were developed in Microsoft Visual Studio 2019.

This thesis deals with the design and subsequent implementation of a unit inspecting a printed logos on the pen surface. A line-scan camera is used to capture the object. Whole the unit including acquited data processing is controlled by Raspberry Pi 4 platform extended by perifery board. The control of the hardware parts is implemented in C++, the detection algorithms in Python using OpenCV and TensorFlow libraries. The unit has a graphical user interface for control of the inspection process. In the end of the thesis test of the unit reliability is shown.

The aim of this master's thesis thesis is to enable evaluation of quality, or the type of product in industrial applications using artificial neural networks, especially in applications where the classical approach of machine vision is too complicated. The system thus designed is implemented onto a specific hardware platform and becomes a subject to the final optimalisation for the hardware platform for the best performance of the system.

Aim of this thesis is modular design of control system for Golem device, which is used for drug research. Several modules with specified controlled area were designed. They communicate using CAN bus and are controlled by Raspberry Pi computer. The thesis includes PCB design and control firmware for modules, backend application for Raspberry Pi, which provides application interface, and simple protocol used for communication between modules. Designed system was tested in simulated environment.

Práca rieši možnosť zaznamenávať pozíciu osôb v zázname z kamery drona a určovať ich polohu. Absolútna pozícia sledovanej osoby je odvodená vzhľadom k pozícii kamery, teda vzhľadom k umiestneniu drona vybaveného príslušnými senzormi. Zistené dáta sú po ich spracovaní vykreslené ako príslušné cesty. Práca si ďalej dáva za cieľ využiť dostupné riešenia čiastkových problémov: detekcia osôb v obraze, identifikácie jednotlivých osôb v čase, určenie vzdialenosti objektu od kamery, spracovanie potrebných senzorových dát. Následne využiť preskúmané metódy a navrhnúť riešenie, ktoré bude v reálnom čase pracovať na uvedenom probléme. Implementačná časť spočíva vo využití akcelerátoru Intel NCS v spojení s Raspberry Pi priamo ako súčasť drona. Výsledný systém je schopný generovať výstup o polohe osôb v zábere kamery a príslušne ho prezentovať.

Stereo vision is one of the most popular elements in the field of mobile robots and significantly contributes to their autonomous behaviour. The aim of the diploma thesis was to design and implement a camera module as a hardware sensor input, which is independent, with the possibility of supplementing the system with other cameras, and to create a depth map from a pair of cameras. The diploma thesis consists of theoretical and practical part, including the conclusion of results. The theoretical part introduces the ROS framework, discusses methods of creating depth maps, and provides an overview of the most popular stereo cameras in robotics. The practical part describes in detail the preparation of the experiment and its implementation. It also describes the camera calibration and the depth map creating. The last chapter contains an evaluation of the experiment.

Práca rieši možnosť zaznamenávať pozíciu osôb v zázname z kamery drona a určovať ich polohu. Absolútna pozícia sledovanej osoby je odvodená vzhľadom k pozícii kamery, teda vzhľadom k umiestneniu drona vybaveného príslušnými senzormi. Zistené dáta sú po ich spracovaní vykreslené ako príslušné cesty v grafe. Práca si ďalej dáva za cieľ využiť dostupné riešenia čiastkových problémov: detekcia osôb v obraze, identifikácia jednotlivých osôb v čase, určenie vzdialenosti objektu od kamery, spracovanie potrebných senzorových dát. Následne využiť preskúmané metódy a navrhnúť riešenie, ktoré bude v reálnom čase pracovať na uvedenom probléme. Implementačná časť spočíva vo využití akcelerátoru Intel NCS v spojení s Raspberry Pi priamo ako súčasť drona. Výsledný systém je schopný generovať výstup o polohe detekovaných osôb v zábere kamery a príslušne ho prezentovať.

The aim of this work is to create a unified control and supervision system, SCADA, for controlling the heating and cooling of the company. The system is controlled by UniPi PLC units. Node-RED is used as control software. The user interface is created by an extension "dashboard". For communication between elements using the MQTT protocol. The InfluxDB database is used for data storage. The Grafana tool is used to visualize historical data. The majority of the created system consists of open software. The system is universal, expandable and it is possible to connect it with another system. The system can be used or adapted to similar problems of advanced heating control.

One of the challenges of utilizing higher frequencies in the RF spectrum, for any number of applications, is the hardware constraints of analog-to-digital converters (ADCs). Since mid-20th century, we have accepted the Nyquist-Shannon Sampling Theorem in that we need to sample a signal at twice the max frequency component in order to reconstruct it. Compressive Sampling (CS) offers a possible solution of sampling sub-Nyquist and reconstructing using convex programming techniques. There has been significant advancements in CS research and development (more notably since 2004), but still nothing to the advantage of everyday use. Not for lack of theoretical use and mathematical proof, but because of no implementation work. There has been little work on hardware in finding the realistic constraints of a working CS system used for digital signal process (DSP). Any parameters used in a system is usually assumed based on stochastic models, but not optimized towards a specific application. This thesis aims to address a minimal viable platform to implement compressive sensing if applied to a wireless sensor network (WSN), as well as address certain parameters of CS theory to be modified depending on the application.

This diploma thesis was focused on study of low-molecular antioxidants present in various kinds of fruits, namely berries. In theoretical part detailed characterization of antioxidants studied in presented work and their biological effects are introduced. Further, principals of processing and storage of specified species of fruits are discussed. Changes observed during storage of specific fruits are described too. In practical part changes in content of selected antioxidants in berries were followed. As studied material strawberries, raspberries, blueberries, red and black currants were used. All kind of these fruits was stored in fridge for six months using several different kind of processing. Berries were stored as whole fruits, whole osmotically processed fruits by sucrose, raw pulp and sweetened pulp. At first, starting values of selected antioxidant parameters were measured in fresh fruits (ascorbate, total antioxidant activity, total phenolics, catechins and individual flavonoids). After freezing the same parameters were measured regularly in two month periods. As the most suitable method of fruit processing, with regard to antioxidant content, storage of the whole fruits or pulp can be recommended. Content of ascorbate decreased in all forms and species of the processed fruits during all the time of storage. Oppositely, the content of other studied antioxidants exhibited fluctuations or increasing trend. Sensory analysis was proved for the whole fruits and also for whole sweetened fruits.