This thesis describes playing draughts game by 6 axis manipulator Lynxmotion AL5D controlled by ARM Cortex M3 MCU. It involves algorithms for image recognition, artificial intelligence and algorithms for controlling 6 axis manipulator in real-time.

The master’s thesis is focused on the UHF RFID reader EXIN-1 signal processing. The first part describes the concept of the EXIN-1 front end, its basic testing and possible communication interfaces for reader control and for receiving and transmitting baseband signals. The second part of this work is aimed to a simple description of EPCglobal Class-1 Generation-2 UHF RFID Protocol, especially to used modulations and codings. In the last part, a block connection between the front end and an ARM Cortex-M4 microcontroller discovery board is designed. The microcontroller is used for generating of all required signals and also for receiving incoming signals and processing them for identification numbers of RFID cards (tags), which are in the reading range of the reader. A decoding algorithm is designed in MATLAB software and implemented to the selected microcontroller. Obtained identification data are displayed on an LCD display and also sent to a PC through a serial communication.

This Master's project deals with implementation of uC/OS-II real-time operating system on FITkit 3 platform, its testing and proving its functionality with simple examples. Describes an example of uC/OS-II application for displaying images on a E-ink display and application of ECCA method for increasing fault tolerance of the system.

The master thesis deals with the techniques to prevent computer system from computational overloading due to excessive frequency of interruptions. The goal is to document the effect of interupts on a selected computing platform containing the ARM Cortex-M4 processor core. The work describes and implements possible software techniques that reduce the impact of consequences of overload due to excessive interruption frequency. At the same time the work verifies and compares the effectiveness of the particular implemented techniques by appropriate set of experiments.

The aim of this work is the design and implementation of a sound level meter for emdedded systems. It is designed sound level meter sensor. This sensor includes a microphone, microphone preamplifier and ADC. This sensor is connected to a development kit STM32F4 Discovery, where sound is processed. It is processed filtration, time weighting, calculation of sound leve and loudness weighting. The sound level is then sent via the serial communication USART. This thesis includes a theoretical analysis of noise and its human perceptions. It also describes the development platform used.

This thesis examines the energy efficiency of Advanced Encryption Standard (AES) encryption across various modes of operation (ECB, CBC, CFB, OFB, CTR, GCM, and CCM) on ARM Cortex-A53, Cortex-A72, and Cortex-A76 processors, using Raspberry Pi models 3, 4, and 5 as the experimental platforms. The study primarily investigates the impact of key lengths (128, 192, and 256 bits) and data sizes on energy consumption during encryption tasks. Using an experimental setup with the Raspberry Pi single-board computers, energy consumption was measured and analyzed through repeated encryption operations and data collection via a power meter interfaced with a database. The results reveal only modest increases in energy consumption with larger key lengths across all tested modes and data sizes, suggesting that while key length incrementally affects energy usage, the impact remains relatively minor, thus not significantly compromising energy efficiency for enhanced security. The analysis further shows that ECB mode consistently exhibits the lowest energy consumption, with CTR and CBC not far behind, followed by OFB and then CFB being the least effective among the traditional modes, with AEAD modes like GCM and CCM demanding substantially higher energy, reflecting their more complex processing requirements. Additionally, the study highlights the influence of data size on energy efficiency, showing a decrease in energy consumption per kilobyte with increasing file size up to a certain point, beyond which the benefits diminish. This thesis contributes to a deeper understanding of the trade-offs between security features and energy efficiency in AES encryption on ARM processors, offering insights into scenarios where energy consumption is a critical concern. The findings underscore the importance of selecting appropriate encryption modes and configurations based on the specific requirements and constraints of hardware environments aimed at optimizing energy efficiency in cryptographic operations. Future research could expand on a broader array of ARM-based devices to improve the biases from the Raspberry Pi boards and enhance the reliability of the conclusions drawn from the data.