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Image data segmentation using deep neural networks
Hrdý, Martin ; Myška, Vojtěch (referee) ; Kiac, Martin (advisor)
The main aim of this master’s thesis is to get acquainted with the theory of the current segmentation methods, that use deep learning. Segmentation neural network that will be capable of segmenting individual instances of the objects will be proposed and created based on theoretical knowledge. The main focus of the segmentation neural network will be segmentation of electronic components from printed circuit boards.
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Wireless robot control using mobile platform
Matuška, Jakub ; Kiac, Martin (referee) ; Přinosil, Jiří (advisor)
This bachelor’s thesis deals with design and implementation of an application for robot’s omnidirectional movement control using mobile platform. Implementation includes an Android application, used as user interface, as well as robot–side Python program for controlling movement and sending RTP stream to Android application. User can control robot’s movement using two virtual joysticks. Raspberry Pi was used as the control unit. The application has security module. Pipeline-based multimedia framework named GStreamer was used to implement RTP steaming. This paper describes necessary theory first and then introduces basic building blocks used in creation process of the application.
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IoT system for gardening
Mlčák, Petr ; Kiac, Martin (referee) ; Caha, Tomáš (advisor)
The thesis deals with the design and creation of a weather station suitable for gardeners. The created device is able to measure temperature, pressure, humidity, amount of precipitation, wind speed and direction, UV index and also temperature and soil moisture at several depths. The weather station is powered by a battery with auxiliary charging from a photovoltaic panel. The thesis is divided into several parts. The theoretical part describes the individual physical principles of measurement of the considered physical quantities. Subsequently, a comparison of available sensors is made and then a final selection is made. The third part deals with the design and implementation of the hardware circuitry including the creation of the PCB. In this section, the holders of each sensor are also designed for printing on a 3D printer, which are then printed. The fourth section deals with software design issues, which is described in more detail. Finally, the whole weather station is assembled, wired and the functionality of all components is verified by sending the measured data to Thingspeak.
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Real-time voice command recognition system
Šíbl, Evžen ; Kiac, Martin (referee) ; Přinosil, Jiří (advisor)
The bachelor thesis deals with the development of a system for voice command recognition. The classifier of this system was created using a neural network. In this thesis you will learn about the history and problems of speech recognition. A system has been created that detects a section in a recording containing a speech signal, which then uses the classifier to decide what word from the word table it is. Three models with the same architecture but with different training data were created. These models were then compared with each other. A simple user interface was created for the resulting system.
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Intelligent beekeeping system
Hrubý, Jan ; Zeman, Václav (referee) ; Kiac, Martin (advisor)
The aim of this thesis is to design and develop an intelligent beekeeping system that can measure the frequency in the colony, the weight of the hive to monitor the loss or to inform the beekeeper if the bees are carrying honey. Furthermore, the security of the hive against theft is also being considered. Communication between multiple intelligent beekeeping systems is important for the functionality. This is why part of the work focuses on choosing the best possible communication, taking into consideration battery consumption and reliability. In this work, a many-to-one communication system of modules is used, which means that the number of hives can be freely expanded without affecting the functionality of the system. The resulting system is powered by a combination of battery and solar panels.
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Mobile application for an intelligent beekeeping system
Pecár, Martin ; Myška, Vojtěch (referee) ; Kiac, Martin (advisor)
The aim of this thesis is to design and create an application which will allow beekeepers to manage their hives with a mobile phone.The reason for this is centralisation and clarification of all colected data from visits to the hive, where this data could be later used to create statistics.Furthermore, this app contains ways to notify the beekeeper that there is a need of intervention with the hive using their own alerts and statistics of selected properties of a hive. The result of this work is the previously described application.
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Intelligent hatchery of poultry
Kejík, Jan ; Číka, Petr (referee) ; Kiac, Martin (advisor)
The aim of this thesis was to design and build an intelligent hatchery for poultry. The first part deals with the description of poultry hatching processes. Furthermore, the bestselling hatcheries from different manufacturers are compared and the proposed hatchery management system is described. In other parts of the work there is described the practical construction of the hatchery as it was constructed. The mechanical part of the hatchery uses largely the components printed on a 3D printer, the electronic equipment uses the components of the Arduino platform. A significant part is the description of embedded software implemented in the object-oriented programming language C++. The resulting hatchery is equipped with an user-friendly interface with the ability of control by mobile applications. As the practical use of the hatchery requires continuous reliable operation for several weeks, the hatchery had been tested for several months. During this time, practical experience was gained to help with the solving of some problematic components and to debug the resulting software.
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Object detection in video using neural networks and Android application
Mikulec, Vojtěch ; Kiac, Martin (referee) ; Myška, Vojtěch (advisor)
This master’s thesis deals with the implementation of functional solution for classifying road users using mobile device with Android operating system. The goal is to create Android application which classifies vehicles in real time using rear-facing camera and saves timestamps of classification. Testing is performed mostly with own, diversely modificated dataset. Five models are trained and their performance is measured in dependence on hardware. The best classification performance is from pretrained MobileNet model where transfer learning with 6 classes of own dataset is used – 62,33 %. The results are summarized and a method for faster and more accurate traffic analysis is proposed.
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Applications for image processing for Android OS
Kiac, Martin ; Říha, Kamil (referee) ; Krajsa, Ondřej (advisor)
This work deals with an issue of image processing and detection of predefined objects from the camera on Android platform. This work uses image processing tools and methods from OpenCV library. The work consists of a theoretical part, in which the Android platform itself is described, the structure of the operating system and Android Studio development environment. The theoretical part also contains a description of OpenCV library and describes the theory of image processing using this library. The practical part of this work describes implementation of OpenCV library to development environment Android Studio. The practical part also describes techniques for image processing by OpenCV library. Subsequently this work contains analysis and searching for contours, the use of Hough transformation and segmentation of the processed image. This work also describes realization of graphical user interface and subsequently work with the application's database. The conclusion is about final evaluation of the work and achieved results.
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Advanced image analysis using deep neural networks
Hynek, Vojtěch ; Přinosil, Jiří (referee) ; Kiac, Martin (advisor)
This bachelor thesis deals with the problem of object detection in images using a convolutional neural network. The result of this work is a custom dataset, a neural network model YOLOv4 and a script used to process the resulting model data. The dataset contains 8080 images on which 14 objects are annotated. The neural network model was reduced in depth, which significantly increased the speed of the detection itself. The script processing the resulting data calculates the 3D and GPS coordinates of the detected object in space. The paper concludes by summarizing the results of the model and at the same time suggesting how the quality of the dataset could be improved.
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