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Data Analysis and Clasification from the Brain Activity Detector
Ullrich, Petr ; Šůstek, Martin (referee) ; Szőke, Igor (advisor)
This thesis deals with the issue of recording brain activity, implementation of its processing, analysis and classification. The OpenBCI hardware is used for recording. I have studied and described necessary information about recording brain activity and OpenBCI project. Design for data set, data processing and thoughts classification was created. Created system allows classification based on recorded brain activity. The neural network was used for classfication, but the success of the recognition of designed classes was not high.
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Command Classification from EMG Using Neural Network
Zauška, Ján ; Šůstek, Martin (referee) ; Szőke, Igor (advisor)
This work deals with classification of 15 commands (short words), from small dataset recorded by sEMG electrodes placed on face and neck of speaker. Two types of speech are differentiated in recordings - audible speech, what is classic speech and silent speech, hence speech, in which sound output is suppressed. This work describes EMG signal processing, feature extraction, classifier design and classification results. The convolutional neural network architecture was used as a classifier. There are a lot of experiments in this work that compare the classification accuracy of silent and audible speech.
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Demontration System for Speaker Recognition
Šústek, Martin ; Šebesta, Vladimír (referee) ; Sigmund, Milan (advisor)
My diploma theses deals with the problem of the speaker recognition. The basic theory of this problem is described in the text as well as model and implementation of the system for speaker recognition. The scope of the system is to recognize up to three speakers. The theory is based on calculation parameters for speaker recognition and processing of voice. Program is made in Matlab as a independent application and it has got Czech and English interface.
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GUI for Active Learning of Image Detection and Classification
Bureš, Tomáš ; Šůstek, Martin (referee) ; Rozman, Jaroslav (advisor)
With active learning, domain expert doesn't need to annotate the whole dataset, but only those which will allow incremental training of a given model. An example of active learning could be detection and removal of wrong annotations. Another example is detection and expansion of training data which model fails to predict. Description of libraries, frameworks and programs which can be used to integrate with active learning is included in this work. The main part of this work is the design and description of a user interface for active learning. The application allows user to browse dataset, sort annotations and images by multiple criteria and modify annotations generated by active learning model. The application's graphical user interface was implemented with the Vue.js framework and Paper.js library. In conclusion, functionality and future application expansion are discussed.
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Demonstrational Program for IZU Course
Hreha, Tomáš ; Šůstek, Martin (referee) ; Zbořil, František (advisor)
This bachelor thesis deals with the design of application for visualization of fundamental algorithms of artificial intelligence. The first part describes theoretical part of implemented topics and methods, next part briefly describes used technologies, reasons why they were used and their practical usage in context of result application. The next part is dedicated to user interface, its main components and describes ways how application interacts with user and how user can interact with application. The last part contains comparison with original demo applications and summarize results of application testing.
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Shared Experience in Reinforcement Learning
Mojžíš, Radek ; Šůstek, Martin (referee) ; Hradiš, Michal (advisor)
The aim of this thesis is to use methods of transfer learning for training neural network on a reinforcement learning tasks. As test environment, I am using old 2D console games, such as space invaders or phoenix. I am testing the impact of re-purposing already trained models for different environments. Next I use methods for domain feature transfer. Lastly i focus on the topic of multi-task learning. From the results we can gain insight into possibilities of using transfer learning for reinforcement learning algorithms.
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Search of Metric State Space with Obstacles
Lukáč, Jakub ; Rozman, Jaroslav (referee) ; Šůstek, Martin (advisor)
This thesis is focused on search of metric state space with obstacles. The thesis selects four methods based on state space search and presents two new algorithms, which will try to consider an obstacles in metric space. The selected methods and the new designed algorithms are implemented as an application in programming language Java, application is also part of the thesis. The thesis presents experiments on metric spaces with various kind of obstacles for comparison of individual methods.
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Neural Network Based Image Modifications
Maslowski, Petr ; Zbořil, František (referee) ; Šůstek, Martin (advisor)
This thesis deals with image colorization and image super-resolution using neural networks. It briefly explains neural networks principles and summarizes current approaches in this domain. It also describes the design, implementation and training of various neural network architectures. The best implemented architecture can colorize images, in particular, works well with outdoor areas. The architecture for image super-resolution with residual blocks that was trained with a perceptual loss function performs a double increase in image resolution (4x more pixels in total). Part of this thesis is also an implementation of a web application that uses trained models for image modification.
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Mobile Application for Recommending and Managing Cooking Recipes
Lončík, Andrej ; Šůstek, Martin (referee) ; Zbořil, František (advisor)
The goal of the submitted thesis is the creation of mobile application for devices using the Android operation system. The main purpose of the application is the discovery and administration of food recipes and meal planning. The functions of the application include voice control and search by a photo or an image. This work describes the whole process of app -creation, beginning from the original idea, followed by the competition analysis, draft of the user interface, its implementation and concluding with the testing and final publication to the Google Play. In addition, the final version of the application offers the feature of creating new recipes or searching for already published ones on the internet based on the ingredients the user possesses. The ingredients can be written in, entered by the user's voice, or recognized from an uploaded image. The photo and image recognition is provided by the Firebase ML Kit Image Labeling tool. Thanks to the Google account authentization , the application is also able to save the user's content in Firebase Realtime Database. Mobile application is published on the Google Play store and is officially named Recipio .
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Movie Recommender System
Janko, Pavel ; Zbořil, František (referee) ; Šůstek, Martin (advisor)
This thesis primarily addresses various methods of constructing a system for movie recommendations. Both basic and advanced techniques required for creating a recommender system are also covered in the thesis. The core of the thesis is designing, implementing and experimenting with a system for movie recommendations based upon the data originating from publicly accessible datasets. In order to predict ratings that the user would give to movies after watching them, the system utilizes a factorization model based on collaborative filtering. This thesis also describes the relation between model hyperparameter configuration and prediction accuracy, experiments that were conducted in order to further improve the model accuracy and finally compares the implemented model with existing solutions.
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