The master’s thesis deals with the aggregate reviews of the economic situation of one joint-stock company, primarily using statistical methods. The goal will be to analyse the data by these means, to compare them, to make the conclusions and suggestions for improvement. From the knowledge of historical data and forecasting preconditions for the future the company will gain clearer image about its development and future directions.

Master‘s thesis deals with analysis solutions and construction of the devices for data collection. At the beginning, they describe the basic types of devices for data collection. In other parts of the work is compared few commercial devices for data collection, under which the requirements will be selected on the proposed device for data collection. In other chapters of the work devoted to the design of schemes and selecting components for their manufacture. The last chapters are devoted to describing the design of device for data collection.

V rámci této práce byl vytvořen popis bezdrátového komunikačního protokolu Wireless M-Bus, zaměřující se zejména na datovou část tohoto protokolu, strukturu jeho informačních polí, režimy komunikace a další specifika linkové a aplikační vrstvy. Na základě tohoto výzkumu byl vytvořen softwarový nástroj v jazyce Java, sloužící jako generátor dat zmíněného protokolu. Pomocí grafického i textového uživatelského rozhraní program umožňuje uživateli vytvořit Wireless M-Bus telegramy s velmi vysokou úrovní detailu a ty následně s využitím některého z podporovaných hardwarových zařízení periodicky odesílat do Wireless M-Bus sítě. Dále byla navržena dvě kompletní řešení, využívající buď samotného bezdrátového IQRF modulu nebo jeho spojení s řídicí deskou UniPi Neuron. Oba návrhy byly zrealizovány, otestovány a jsou v práci detailně popsány.

This master thesis is focused on the marketing research of apartment houses project for the company VAKABRNOCZ s.r.o. at Dolni Morava. The main goal of the master thesis was to determine required information for a successful realization of the project. Gathered data was evaluated and based on the results recommendations and proposals were made for a successful realization of the project.

Subject of this effort is development and realization of one-channel data acquisition unit of mechanical quantities and it’s PC software application. Usage of this data acquisition unit is aimed on applications, where resistive strain gages wired as Wheatstone bridge with AC excitation are used. Output voltage of Wheatstone bridge is amplięed, demodulated with excitation voltage, filtered and level shifted for analog to digital conversion via AD converter. Converted digital data are sent via serial bus into microprocessor which sends them into PC for further meaningful representation in software application. Besides sensing bridge output voltage, data acquisition unit also generates AC excitation of Wheatstone bridge. This excitation voltage is generated via DA converter which communicates with microprocessor and converts incoming digital data into analog sine waveform. There is precise voltage reference circuitry for both of these converters. PC application’s task is to visualize measured data in meaningful form and to give tools for further work with measured data. It’s programmed in Python language and it has one independent thread which does reading and computing of incoming data and there is also class which builds graphical user interface and plots measured data into live graph.

The Master’s thesis deals with the precision of machine tools, especially with the three-axis vertical milling machine. It is structurally divided into a theoretical and practical part. While the theoretical part focuses on problems of geometric errors and measuring devices, the practical one is devoted to the machine tool being tested. In this case, the data processing and the proposed statistical analysis are essential, and at the end of the thesis another possible way of developing the data processing is outlined.

This diploma thesis deals with the analysis, description and usage of sensor data from an intelligent home system. This term also describes the intelligent system BeeeOn, which provides a sensor data and the possibility of extending this system to automation tasks. This is followed by the analysis of all the measured physical quantities, their properties and their influence on humans. The results from the measured data were used to create a classifier based on deep neural networks to detect current events at home. Detected events can be used for the following automation system to help improve living conditions. At the end of this thesis are discussed the results and options to continue with this project.

The goal of this diploma thesis is to analyze process of migration data within the scope of SAP systems. Based on this analysis suitably desing and implement migration framework that will be used for migration of data between SAP systems. Entire migration framework and migration proces is built on technological platform SAP NetWeaver and is implemented in programming language ABAP. Functionality of the framework is presented by the case study that is represented by proper data set.

This thesis deals with real-time human activity recognition (eg, running, walking, driving, etc.) using sensors which are available on current mobile devices. The final product of this thesis consists of multiple parts. First, an application for collecting sensor data from mobile devices. Followed by a tool for preprocessing of collected data and creation of a data set. The main part of the thesis is the design of convolutional neural network for activity classification and subsequent use of this network in an Android mobile application. The combination of previous parts creates a comprehensive framework for detection of user activities. Finally, some interesting experiments were made and evaluated (eg, the influence of specific sensors on detection precision).

This thesis deals with possibilities of automatic segmentation of biomedical images. For the 3D image segmentation, a deep learning method has been proposed. In the work problems of network design, memory optimization method and subsequent composition of the resulting image are solved. The uniqueness of the method lies in 3D image processing on a GPU in combination with augmentation of training data and preservation of the output size with the original image. This is achieved by dividing the image into smaller parts with the overlay and then folding to the original size. The functionality of the method is verified on the segmentation of human brain tissue on magnetic resonance imaging, where it overcomes human accuracy when compared a specialist vs. specialist, and cell segmentation on a slices of the Drosophila brain from an electron microscope, where published results from the impacted paper are overcome.