Computer supported estimation of input data for transportation models

Control and management of transportation systems frequently rely on optimization or simulation methods based on a suitable model. Such a model uses optimization or simulation procedures and correct input data. The input data define transportation infrastructure and transportation flows. Data acquisition is a costly process and so an efficient approach is highly desirable. The infrastructure can be recognized from drawn maps using segmentation, thinning and vectorization. The accurate definition of network topology and nodes position is the crucial part of the process. Transportation flows can be analyzed as vehicle’s behavior based on video sequences of typical traffic situations. Resulting information consists of vehicle position, actual speed and acceleration along the road section. Data for individual vehicles are statistically processed and standard vehicle characteristics can be recommended for vehicle generator in simulation models

Design of Automatic Software Test Set for Freescale Touch Product

Import 22/07/2015Náplní práce je návrh a realizace souboru testů ověřující správnou funkčnost softwarových knihoven, které tvoří základ produktu Freescale Touch. Tento produkt spojuje výhody softwarové technologie kapacitního měření a různých detekčních algoritmů s hardwarovými možnostmi mikrokontrolérů firmy Freescale z řady Kinetis. Výsledkem tohoto spojení je systém umožňující snadno vytvářet koncové aplikace, které poskytují ovládání dotykem prstu, ať už ve formě klávesnice nebo různých posuvných a otočných prvků. Soubor testů, který má být navržen, musí pokrývat všechny softwarové vrstvy a funkce poskytované v rámci Freescale Touch.The scope of work is the design and implementation of the set of tests to verify the correct functioning of software libraries that form the basis of product Freescale Touch. This product combines the benefits of software technology capacitive measurement and detection algorithms with different hardware options microcontrollers from Freescale Kinetis Series. The result is a system that allows to easily create end-use applications that provide fingertip control, whether in the form of a keyboard or various sliding and revolving elements. A set of tests to be designed to cover all software layers and functions provided within Freescale Touch.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

AUTOMATIC VECTORIZATION OF INPUT DATA FOR MODELS OF TRANSPORTATION SYSTEMS

Digitálne (vektorové) údaje tvoria základ pre modelovanie dopravných systémov a riešenie optimalizačných úloh. V praxi sú tieto vstupné údaje často reprezentované rastrovými mapami a náčrtkami, ktoré je potrebné vektorizovať. Manuálna a poloautomatická vektorizácia je pomerne časovo a finančne náročná a tak sa otvára priestor pre automatizáciu tohto procesu. V dnešnej dobe existuje pomerne veľa nástrojov pre rozpoznávanie objektov a vzorov v rastrových obrázkoch spadajúcich do problematiky digitálneho spracovania obrazu. Aj keď sa v prípade máp s dopravnou infraštruktúrou jedná o pomerne rozsiahlu problematiku, v prípade kreslených máp je možné pomerne presne definovať základné vlastnosti a požiadavky, ktoré má proces automatickej vektorizácie spĺňať. V tomto príspevku je ukázaný postup procesu automatickej vektorizácie máp s dopravnou infraštruktúrou.Digital (vector) data is essential for modeling of transportation systems and solving of optimization problems. This input data is often represented by raster maps and drawings in practice and it is necessary to vectorize them. Manual and semi-automatic vectorization is expensive and time consuming and that’s why the room for automation of this process is opened. Today there are many tools for pattern recognition in raster images which are part of the digital image processing tasks. Although vectorization of maps with transportation infrastructure is complex problem, in case of drawing maps it is possible to define the main features for the process of automatic vectorizaion. In this paper process of automatic vectorization of maps with transportation infrastructure is shown

Highly Chemoselective Catalytic Photooxidations by Using Solvent as a Sacrificial Electron Acceptor

Catalyst recovery is an integral part of photoredox catalysis. It is often solved by adding another component-a sacrificial agent-whose role is to convert the catalyst back into its original oxidation state. However, an additive may cause a side reaction thus decreasing the selectivity and overall efficiency. Herein, we present a novel approach towards chemoselective photooxidation reactions based on suitable solvent-acetonitrile acting simultaneously as an electron acceptor for catalyst recovery, and on anaerobic conditions. This is allowed by the unique properties of the catalyst, 7,8-dimethoxy-3-methyl-5-phenyl-5-deazaflavinium chloride existing in both strongly oxidizing and reducing forms, whose strength is increased by excitation with visible light. Usefulness of this system is demonstrated in chemoselective dehydrogenations of 4-methoxy- and 4-chlorobenzyl alcohols to aldehydes without over-oxidation to benzoic acids achieving yields up to 70 %. 4-Substituted 1-phenylethanols were oxidized to ketones with yields 80–100 % and, moreover, with yields 31-98 % in the presence of benzylic methyl group, diphenylmethane or thioanisole which are readily oxidized in the presence of oxygen but these were untouched with our system. Mechanistic studies based on UV-Vis spectro-electrochemistry, EPR and time-resolved spectroscopy measurements showed that the process involving an electron release from an excited deazaflavin radical to acetonitrile under formation of solvated electron is crucial for the catalyst recovery

The NORMAN Association and the European Partnership for Chemicals Risk Assessment (PARC): let’s cooperate! [Commentary]

The Partnership for Chemicals Risk Assessment (PARC) is currently under development as a joint research and innovation programme to strengthen the scientific basis for chemical risk assessment in the EU. The plan is to bring chemical risk assessors and managers together with scientists to accelerate method development and the production of necessary data and knowledge, and to facilitate the transition to next-generation evidence-based risk assessment, a non-toxic environment and the European Green Deal. The NORMAN Network is an independent, well-established and competent network of more than 80 organisations in the field of emerging substances and has enormous potential to contribute to the implementation of the PARC partnership. NORMAN stands ready to provide expert advice to PARC, drawing on its long experience in the development, harmonisation and testing of advanced tools in relation to chemicals of emerging concern and in support of a European Early Warning System to unravel the risks of contaminants of emerging concern (CECs) and close the gap between research and innovation and regulatory processes. In this commentary we highlight the tools developed by NORMAN that we consider most relevant to supporting the PARC initiative: (i) joint data space and cutting-edge research tools for risk assessment of contaminants of emerging concern; (ii) collaborative European framework to improve data quality and comparability; (iii) advanced data analysis tools for a European early warning system and (iv) support to national and European chemical risk assessment thanks to harnessing, combining and sharing evidence and expertise on CECs. By combining the extensive knowledge and experience of the NORMAN network with the financial and policy-related strengths of the PARC initiative, a large step towards the goal of a non-toxic environment can be taken

The NORMAN Association and the European Partnership for Chemicals Risk Assessment (PARC): let’s cooperate! [Commentary]

The Partnership for Chemicals Risk Assessment (PARC) is currently under development as a joint research and innovation programme to strengthen the scientific basis for chemical risk assessment in the EU. The plan is to bring chemical risk assessors and managers together with scientists to accelerate method development and the production of necessary data and knowledge, and to facilitate the transition to next-generation evidence-based risk assessment, a non-toxic environment and the European Green Deal. The NORMAN Network is an independent, well-established and competent network of more than 80 organisations in the field of emerging substances and has enormous potential to contribute to the implementation of the PARC partnership. NORMAN stands ready to provide expert advice to PARC, drawing on its long experience in the development, harmonisation and testing of advanced tools in relation to chemicals of emerging concern and in support of a European Early Warning System to unravel the risks of contaminants of emerging concern (CECs) and close the gap between research and innovation and regulatory processes. In this commentary we highlight the tools developed by NORMAN that we consider most relevant to supporting the PARC initiative: (i) joint data space and cutting-edge research tools for risk assessment of contaminants of emerging concern; (ii) collaborative European framework to improve data quality and comparability; (iii) advanced data analysis tools for a European early warning system and (iv) support to national and European chemical risk assessment thanks to harnessing, combining and sharing evidence and expertise on CECs. By combining the extensive knowledge and experience of the NORMAN network with the financial and policy-related strengths of the PARC initiative, a large step towards the goal of a non-toxic environment can be taken

Construction of a Microcomputer Controlled Electronic Power Meter - the Realization of Laboratory Task

Import 26/06/2013V této práci je popsána architektura mikropočítače řady MSP430 firmy Texas Instruments pro podporu výuky v laboratoři, tvorba měřící aplikace a její vyzkoušení při praktickém měření. A tvorba měřící aplikace pomocí architektury AVR.This work describes the architecture of the MSP430 microcontroller series from Texas Instruments to support teaching in the laboratory, creating measurement and its application in the practical test measurements. A production test applications using the AVR architecture.450 - Katedra kybernetiky a biomedicínského inženýrstvívelmi dobř

Tensor Implementation of Monte-Carlo Tree Search for Model-Based Reinforcement Learning

Monte-Carlo tree search (MCTS) is a widely used heuristic search algorithm. In model-based reinforcement learning, MCTS is often utilized to improve action selection process. However, model-based reinforcement learning methods need to process large number of observations during the training. If MCTS is involved, it is necessary to run one instance of MCTS for each observation in every iteration of training. Therefore, there is a need for efficient method to process multiple instances of MCTS. We propose a MCTS implementation that can process batch of observations in fully parallel fashion on a single GPU using tensor operations. We demonstrate efficiency of the proposed approach on a MuZero reinforcement learning algorithm. Empirical results have shown that our method outperforms other approaches and scale well with increasing number of observations and simulations