The aim of this master's thesis was to design, develop and test a method for lossless image compression. The theoretical part includes a description of selected exiting methods such as RLE, MTF, adaptive arithmetic coding, color models used in LOCO-I and JPEG 2000, predictors MED, GAP and laplacian pyramid. The conclusion includes a comparison of various combinations of chosen approaches and overall efficiency compared with PNG and JPEG-LS.

This work deals with the usage of neural network for the purpose of stock market prediction. A basic stock market theory and trading approaches are mentioned at the beginning of this work. Then neural networks and their application are discussed with their deeper description. Similar approaches are referred and finally two new prediction systems are designed. These systems are utilized by proposed trading model and tested on selected data. The results are compared to human and random trading models and new development steps are devised at the end of this work.

Traffic monitoring using computer vision is becoming the desired system in practice. It allows nondestructive installation and also is very useful in many applications. This thesis focuses on automatic detection of vehicles approaching to a crossroads. This work also includes description of selected methods for detecting moving vehicles and the way of tracking them. On the basis of these methods is designed application that is implemented and tested in different lighting and weather conditions and various direction of approaching vehicles.

This diploma thesis delas with algoritmic trading using neural networks. In the first part, some basic information about stock trading, algorithmic trading and neural networks are given. In the second part, data sets of historical market data are used in trading simulation and also as training input of neural networks. Neural networks are used by automated strategy for predicting future stock price. Couple of automated strategies with different variants of neural networks are evaluated in the last part of this work.

This doctoral thesis focuses on the problems of IGBT failure prediction in pulse converters using measurable changes of selected parameters (so--called trending variables) being influenced of transistor degradation during aging. Firstly the state--of--the--art in this field is presented in the dizertation. The description of designed and constructed automated measurement stand follows, enabling monitoring and recording of switching processes during accelerated aging. Further the problems of high--bandwidth measurement of electrical quantities during IGBT switching are described. Especially the problems of current sensing are analyzed and the most suitable sensor is selected. The data recorded using the developed apparatus served to identify potential trending variables allowing the failure prediction. Here the dependence of trending variables on aging and on parasitic influences (current, temperature, voltage) had to be distinguished. Finally the evaluation of trending variables is performed. Their insignificant sensitivity on accelerated aging is shown which complicates their practical implementation for the purpose of failure prediction.

This master's thesis describes creation of prediction system. This system predicts future market development based on stock exchange data and twitter messages analysis. Tweets from two different sources are analysed by mood dictionaries or via recurrent neural networks. This analysis results and technical analysis of stock exchange data results are used in multilayer neural network for prediction. A business strategy is created and tested based on results of this prediction. Design and implementation of prediction system is described in this thesis. This system achieved revenue increase more than 25 % of some business strategies by tweets analysis. However this improvement applies for certain data and timeframe.

The thesis is devoted to an analysis of ice hockey matches results in the highest Czech league competition in seasons 1999/2000 to 2014/2015 and to prediction of the following matches. We describe and apply Kalman filter theory where forms of teams represent an unobservable state vector and results of matches serve as measurements. Goal differences are identified as a suitable transformation of a match result. They are used as a dependent variable in a linear regression to find significant predictors. For a prediction of a match result we construct an ordinal model with those predictors. By using generalized Gini coefficient, we compare a diversifica- tion power of this model with betting odds, which are offered by betting companies. At the end, we combine knowledge of odds before a match with other predictors to make a prediction model. This model is used to identify profitable bets. 1

Improving the short-term results of kidney transplantation did not result in improving the long-term function and survival of kidney allograft. Organ shortage and increasing number of marginal donors remains the key problem in transplant today. The quality of donor organ is critical for graft function development and survival. The aim is to improve understanding to ischemia/reperfusion injury and its consequences, predict delayed graft function and rejection, improve organ allocation strategy and identify patients suitable for safe drug minimization or complete withdrawal of immunosuppressive therapy. Analysis of donor kidneys identified poor tubular cell quality and low survival factor, Netrin-1 expression levels, to be associated with delayed graft function. We confirmed that reperfusion phase of ischemia/reperfusion injury leads to minimal morphological but significant molecular abnormalities. Dissociation observed in histology and molecular pathology finding calls for an integrated approach in donor quality organ evaluation and allocation for transplantation. Significant heterogeneity within donors with expanded criteria was shown and subgroup of organs at low risk of delayed graft function was identified. We suggested donor biopsies to be performed as a routine praxis in all kidneys...

"It's terrible. You can't trust anyone anymore." - Dawn Butler In this dissertation I concern myself with a phenomenon of betrayal within field of psychology. Despite the fact that examples of violations or betrayals of trust are manifold, relatively little theory exists regarding dynamics of trust, context-specific perception of betrayal or ability to predict violation of trust be- fore it actually happens. Overall there is significant gap in our knowledge about betrayal, despite its obvious prevalence in human behaviour. There are several approaches to betrayal ranging from evolutionary psy- chology to organizational psychology. Nevertheless this area lacks some unified approach and grasp of the problem. In my work I try to explore and build upon all these theories to answer following questions: (1) Is betrayal context-specific? Does certain context trigger perception of certain behaviour to be viewed as betrayal? (2) What role does intentionality play in perception of betrayal? In what contexts is intention even relevant? (3) Are we able to predict betrayal? Are there some emotional or communicational clues of upcoming betrayal? In the research part of this thesis I further examine betrayal in four differ- ent ways. I ask about colour-association to betrayal to examine their prevailing emotion...

Stabilní proteiny nacházejí široké uplatnění v řadě medicínských a biotechnologických aplikacích. Přírodní proteiny se vyvinuly tak, aby fungovaly převážně v mírných podmínkách uvnitř buněk. V důsledku toho vzniká zájem o stabilizaci proteinů za účelem jejich širšího uplatnění také v průmyslovém prostředí. Obor proteinového inženýrství se v posledních letech rozvinul do úrovně umožňující modifikovat proteiny pro různá využití, ačkoliv identifikace stabilních mutací je stále zatížená drahou a časově náročnou experimentální prací. Výpočetní metody se proto uplatňují jako atraktivní alternativa, která dovoluje prioritizovat potenciálně stabilizující mutace pro laboratorní práci. Během posledních let bylo vyvinuto velké množství výpočetních strategií: i) výpočty energie pomocí silových polí, ii) evoluční metody, iii) strojové učení a iv) kombinace více přístupů. Spolehlivost a využití nástrojů jsou často limitovány predikcí pouze jednobodových mutací, které mají malý dopad na stabilitu proteinů, zatímco sofistikovanější metody pro predikci multibodových mutací vyžadují větší množství práce na straně uživatele. Hlavním záměrem této práce je poskytnout uživatelům plně automatizované metody, umožňující návrh vysoce stabilních vícebodových mutantů bez potřeby pokročilých znalostí bioinformatických nástrojů a zkoumaného proteinu. V této práci jsou prezentovány následující nástroje a databáze:  FireProt je plně automatizovaná metoda pro návrh stabilních vícebodových mutantů z kategorie tzv. hybridních přístupů. Ve svém výpočetním jádře spojuje jak energetické tak i evoluční metody, přičemž evoluční informace jsou užívány především jako filtry pro časově náročné výpočty energií. Kromě detekce potenciálně stabilizujících mutací se FireProt rovněž snaží spojit tyto mutace do jednoho vícebodového mutanta s minimalizací rizika vzniku antagonistických efektů. FireProt-ASR je plně automatizovaná platforma pro rekonstrukci ancestrálních sekvencí, která dovoluje uživatelům využít tuto strategii bez nutnosti velkého objemu manuální práce a hluboké znalosti zkoumaného proteinu. FireProt-ASR řeší všechny kroky ancestrální rekonstrukce, včetně sběru biologicky relevantních sekvencí, konstrukce zakořeněného fylogenetického stromu a rekonstrukce ancestrálních sekvencí.HotSpotWizard je nástroj pro návrh mutací a mutačních knihoven za účelem zlepšení stability a aktivity zkoumaných proteinů. Nástroj dovoluje provést i širší analýzu za využití čtyř různých strategií běžně používaných v oboru proteinového inženýrství: i) identifikace evolučně variabilních pozic v blízkosti katalytických kapes a tunelů, ii) identifikace pohyblivých regionů, iii) výpočet sekvenčního konsensu a iv) identifikace korelovaných pozic.FireProt-DB je databáze dostupných experimentálních dat popisujících stabilitu proteinů. Hlavním účelem této databáze je standardizovat data v oblasti proteinové stability, poskytnout uživatelům platformu k jejich snadnému ukládání a umožnit intuitivní vyhledávání, které by mohly být využité k trénování nových nástrojů s využitím technik strojového učení.