In this work I aimed at designing a universal system for testing either STM32F407/417 by STMicroelectronics and later various sensors and communication buses. The result then is a main board with many specific connectors for individual buses even with connectors making all I/O pins accessible at the same time. Thanks to advanced switching regulators the power supply is capable of accepting a wide range of sources like single Li-Ion cell, pair of alkaline cells, 12V Lead battery, common wall power adapters (both DC and AC up to 15 Vpp ), USB, laboratory power supplies with multiple outputs and finally POE (Power Over Ethernet). Supply voltages are supervised by voltage comparators with an optical signalisation. (It is possible to determine the sick branch without a measuring instrument and blowing components in the most cases.) Another important parameter was a robustness of the supply and communication lines. There is a number of TVSs, chokes, and big and low ESR capacitors A PC software is intended for a basic functionality demonstration only.

This thesis deals with an electronic circuit and a PCB of a modular RGB LED display with the Ehernet interface. Firstly, author describes a RGB colour model, features of RGB LED displays, ways of control them. The next chapter contains a short description of the Ethernet interface, UDP and TCP protocols and a lwIP TCP/IP stack. The last theoretical chapter is an introduction to ARM Cortex-M3 and Cortex-M4 based microcontrollers. The next chaper is deals with a hardware design of the modular RGB LED display. The device is designed to be modular. Individual devices can be combosed together and create a larger display. Data from the Ethernet interface will be displayed on the RGB LED matrix, resolution of the matrix is 32 × 32 (1024 diodes). A refresh frequency is 100 Hz, a color depth is High color (16 bits) and a scanning 1/16 (two rows is driven at the same time). The next chapter describes the firmware for the RGB LED display, all its logical parts including a web page. Author also created the PC application, which sends pictures using UDP protocol to individual modules.

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 development of machine learning research has provided statistical innovations and further developments within the field of time series analysis. This study seeks to investigate two different approaches on artificial neural network models based on different learning techniques, and answering how well the neural network approach compares with a basic autoregressive approach, as well as how the artificial neural network models compare to each other. The models were compared and analyzed in regards to the univariate forecast accuracy on 20 randomly drawn time series from two different time frequencies from the M4 competition dataset. Forecasting was made dependent on one time lag (t-1) and forecasted three and six steps ahead respectively. The artificial neural network models outperformed the baseline Autoregressive model, showing notably lower mean average percentage error overall. The Multilayered perceptron models performed better than the Long short-term memory model overall, whereas the Long short-term memory model showed improvement on longer prediction time dimensions. As the training were done univariately  on a limited set of time steps, it is believed that the one layered-approach gave a good enough approximation on the data, whereas the added layer couldn’t fully utilize its strengths of processing power. Likewise, the Long short-term memory model couldn’t fully demonstrate the advantagements of recurrent learning. Using the same dataset, further studies could be made with another approach to data processing. Implementing an unsupervised approach of clustering the data before analysis, the same models could be tested with multivariate analysis on models trained on multiple time series simultaneously.

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.

Various molecular pathways in the brain shape our understanding of good and bad, as well as our motivation to seek and avoid such stimuli. This work evolves around how systemic inflammation causes aversion; and why general unpleasant states such as sickness, stress, pain and nausea are encoded by our brain as undesirable; and contrary to these questions, how drugs of abuse can subjugate the motivational neurocircuitry of the brain. A common feature of these various disease states is involvement of the motivational neurocircuitry - from mesolimbic to striatonigral pathways. Having an intact motivational system is what helps us evade negative outcomes and approach natural positive reinforcers, which is essential for our survival. During disease-states the motivational neurocircuitry may be overthrown by the molecular mechanisms that originally were meant to aid us. In study I, to investigate how inflammation is perceived as aversive, we used a behavioral test based on Pavlovian place conditioning with the aversive inflammatory stimulus E. coli lipopolysaccharide (LPS). Using a combination of cell-type specific gene deletions, pharmacology, and chemogenetics, we uncovered that systemic inflammation triggered aversion by MyD88-dependent activation of the brain endothelium followed by COX1-mediated cerebral prostaglandin E2 (PGE2) synthesis. Moreover, we showed that inflammation-induced PGE2 targeted EP1 receptors on striatal dopamine D1 receptor–expressing neurons and that this signaling sequence induced aversion through GABA-mediated inhibition of dopaminergic cells. Finally, inflammation-induced aversion was not an indirect consequence of fever or anorexia but constituted an independent inflammatory symptom triggered by a unique molecular mechanism. Collectively, these findings demonstrate that PGE2-mediated modulation of the dopaminergic circuitry is a key mechanism underlying inflammation-induced aversion. In study II, we investigate the role of peripheral IFN-γ in LPS induced conditioned place aversion by employing a strategy based on global and cell-type specific gene deletions, combined with measures of gene-expression. LPS induced IFN-ɣ expression in the blood, and deletion of IFN-ɣ or its receptor prevented conditioned place aversion (CPA) to LPS. LPS increased the expression of chemokine Cxcl10 in the striatum of normal mice. This induction was absent in mice lacking IFN-ɣ receptors or Myd88 in blood brain barrier endothelial cells. Furthermore, inflammation-induced aversion was blocked in mice lacking Cxcl10 or its receptor Cxcr3. Finally, mice with a selective deletion of the IFN-ɣ receptor in brain endothelial cells did not develop inflammation-induced aversion. Collectively, these findings demonstrate that circulating IFN-ɣ binding to receptors on brain endothelial cells which induces Cxcl10, is a central link in the signaling chain eliciting inflammation-induced aversion. In study III, we explored the role of melanocortin 4 receptors (MC4Rs) in aversive processing using genetically modified mice in CPA to various stimuli. In normal mice, robust aversions were induced by systemic inflammation, nausea, pain and kappa opioid receptor-induced dysphoria. In sharp contrast, mice lacking MC4Rs displayed preference towards most of the aversive stimuli, but were indifferent to pain. The unusual flip from aversion to reward in mice lacking MC4Rs was dopamine-dependent and associated with a change from decreased to increased activity of the dopamine system. The responses to aversive stimuli were normalized when MC4Rs were re-expressed on dopamine D1 receptor-expressing cells or in the striatum of mice otherwise lacking MC4Rs. Furthermore, activation of arcuate nucleus proopiomelanocortin neurons projecting to the ventral striatum increased the activity of striatal neurons in a MC4R-dependent manner and elicited aversion. Our findings demonstrate that melanocortin signaling through striatal MC4Rs is critical for assigning negative motivational valence to harmful stimuli. The neurotransmitter acetylcholine has been implied in reward learning and drug addiction. However, the role of cholinergic receptor subtypes in such processes remains elusive. In study IV we investigated the function of muscarinic M4Rs on dopamine D1R expressing neurons and acetylcholinergic neurons, using transgenic mice in various reward-enforced behaviors and in a “waiting”-impulsivity test. Mice lacking M4-receptors from D1-receptor expressing neurons exhibited an escalated reward seeking phenotype towards cocaine and natural reward, in Pavlovian conditioning and an operant self-administration task, respectively. In addition, the M4-D1RCre mice showed impaired waiting impulsivity in the 5-choice-serial-reaction-time-task. On the contrary, mice without M4Rs in acetylcholinergic neurons were unable to learn positive reinforcement to natural reward and cocaine, in an operant runway paradigm and in Pavlovian conditioning.  Immediate early gene expression mirrored the behavioral findings arising from M4R-D1R knockout, as cocaine induced cFos and FosB was significantly increased in the forebrain of M4-D1RCre mice, whereas it remained normal in the M4R-ChatCre mice. Our study illustrates that muscarinic M4Rs on specific neural populations, either cholinergic or D1R-expressing, are pivotal for learning processes related to both natural reward and drugs of abuse, with opposing functionality.