This thesis deals with detection of the road adjacent to an autonomous vehicle. The road is recognition is based on the Velodyne LiDAR laser radar data. An existing solution is used and extended by machine learning - a Support Vector Machine with online learning. The thesis evaluates the existing solution and the new one using a KITTI dataset. The reliability of the road recognition is then computed using F-measure.

Main goal of classifying proteins into families is to understand structural, functional and evolutionary relationships between individual proteins, which are not easily deducible from available data. Since the structure and function of proteins are closely related, determination of function is mainly based on structural properties, that can be obtained relatively easily with current resources. Protein classification is also used in development of special medicines, in the diagnosis of clinical diseases or in personalized healthcare, which means a lot of investment in it. I created a new hierarchical tool for protein classification that achieves better results than some existing solutions. The implementation of the tool was preceded by acquaintance with the properties of proteins, examination of existing classification approaches, creation of an extensive data set, realizing experiments and selection of the final classifiers of the hierarchical tool.

Link label prediction is the problem of predicting the missing labels or signs of all the unlabeled edges in a network. For signed networks, these labels can either be positive or negative. In recent years, different algorithms have been proposed such as using regression, trust propagation and matrix factorization. These approaches have tried to solve the problem of link label prediction by using ideas from social theories, where most of them predict a single missing label given that labels of other edges are known. However, in most real-world social graphs, the number of labeled edges is usually less than that of unlabeled edges. Therefore, predicting a single edge label at a time would require multiple runs and is more computationally demanding. In this thesis, we look at link label prediction problem on a signed citation network with missing edge labels. Our citation network consists of papers from three major machine learning and data mining conferences together with their references, and edges showing the relationship between them. An edge in our network is labeled either positive (dataset relevant) if the reference is based on the dataset used in the paper or negative otherwise. We present three approaches to predict the missing labels. The first approach converts the label prediction problem into a standard classification problem. We then, generate a set of features for each edge and then adopt Support Vector Machines in solving the classification problem. For the second approach, we formalize the graph such that the edges are represented as nodes with links showing similarities between them. We then adopt a label propagation method to propagate the labels on known nodes to those with unknown labels. In the third approach, we adopt a PageRank approach where we rank the nodes according to the number of incoming positive and negative edges, after which we set a threshold. Based on the ranks, we can infer an edge would be positive if it goes a node above the threshold. Experimental results on our citation network corroborate the efficacy of these approaches. With each edge having a label, we also performed additional network analysis where we extracted a subnetwork of the dataset relevant edges and nodes in our citation network, and then detected different communities from this extracted sub-network. To understand the different detected communities, we performed a case study on several dataset communities. The study shows a relationship between the major topic areas in a dataset community and the data sources in the community.

Construction of motor models is the main topic of this project. Mathematical characterization of AC machine, permanent magnet synchronous motor, separately-excited DC motor, series-wound DC motor, permanent magnet DC motor, switched reluctance motor is also described. Design of models is based on mathematical description of particular motors. Models are created in Matlab Simulink. Each model is implemented in continuous and also in discrete time variant. Selected models are implemented also on processor from Freescale 56F800E Hybrid Controller family. Each model has individual graphic user interface. Besides motor models, there is description and easy algorithm of Space Vector Modulation. Model of this method is also created. Models are build-up into a library, which can be used for simulations and tests of control structures. Results of models simulations are presented at the end of this project. Simulation of models that are implemented on processor is also made in Matlab Simulink environment and is compared to simulation of models that are implemented directly in Matlab Simulink.

This thesis contains basic theoretical information about SVM-based image segmentation and data classification. Basic information about 3D Slicer software are presented. Aspects of medical images segmentation are described. Workplan and implemetation of SVM method for MRI segmentation in 3D Slicer sofware as extension module is created. SVM method is compared with simple segmentation algorithms included in 3D Slicer. Quality of segmentation, based on SVM, tested on real subjects is experimentaly demonstrated.

Tato diplomová práce popisuje návrh a implementaci nástroje pro automatickou kategorizaci webových stránek. Cílem nástroje je aby byl schopen se z ukázkových webových stránek naučit, jak každá kategorie vypadá. Poté by měl nástroj zvládnout přiřadit naučené kategorie k dříve nespatřeným webovým stránkám. Nástroj by měl podporovat více kategorií a jazyků. Pro vývoj nástroje byly použity pokročilé techniky strojového učení, detekce jazyků a dolování dat. Nástroj je založen na open source knihovnách a je napsán v jazyce Python 3.3.

This thesis deals with spam in mobile and social networks. It focuses on spam in SMS messages and web service Twitter. Theoretical part provides brief overview of those two media, informations about what spam is, how to defend against it and where does it comes from. There is also a list of methods for spam detection, many of them have their roots in filtration of email communication. The rest of thesis is about design, implementation of application  for spam detection in SMS and Twitter messages and evaluation of its performance.

This thesis deals with text mining. It focuses on problems of document classification and related techniques, mainly data preprocessing. Project also introduces the SVM method, which has been chosen for classification, design and testing of implemented application.

This master's thesis prepared the programme which is able to follow the trajectories of the movement of people and based on this to create various statistics. In practice it is an effective marketing tool which can be used for instance for customer flow analyses, optimal evaluation of opening hours, visitor traffic analyses and for a lot of other benefits. Histograms of oriented gradients, SVM classificator and optical flow monitoring were used to solve this problem. The method of multiple hypothesis tracking was selected for the association data. The system's quality was evaluated from the video footage of the street with the large concentration of pedestrians and from the school's camera system, where the movement in the corridor was monitored and the number of people counted.

The topic of this thesis is Hand Gesture Recognition and Hand Tracking for user interface applications. 3 systems were produced, as well as datasets for recognition and tracking, along with UI applications to prove the concept of the technology. These represent significant contributions to resolving the hand recognition and tracking problems for 2d systems. The systems were designed to work in video only contexts, be computationally light, provide recognition and tracking of the user's hand, and operate without user driven fine tuning and calibration. Existing systems require user calibration, use depth sensors and do not work in video only contexts, or are computationally heavy requiring GPU to run in live situations. A 2-step static hand gesture recognition system was created which can recognize 3 different gestures in real-time. A detection step detects hand gestures using machine learning models. A validation step rejects false positives. The gesture recognition system was combined with hand tracking. It recognizes and then tracks a user's hand in video in an unconstrained setting. The tracking uses 2 collaborative strategies. A contour tracking strategy guides a minimization based template tracking strategy and makes it real-time, robust, and recoverable, while the template tracking provides stable input for UI applications. Lastly, an improved static gesture recognition system addresses the drawbacks due to stratified colour sampling of the detection boxes in the detection step. It uses the entire presented colour range and clusters it into constituent colour modes which are then used for segmentation, which improves the overall gesture recognition rates. One dataset was produced for static hand gesture recognition which allowed for the comparison of multiple different machine learning strategies, including deep learning. Another dataset was produced for hand tracking which provides a challenging series of user scenarios to test the gesture recognition and hand tracking system. Both datasets are significantly larger than other available datasets. The hand tracking algorithm was used to create a mouse cursor control application, a paint application for Android mobile devices, and a FPS video game controller. The latter in particular demonstrates how the collaborating hand tracking can fulfill the demanding nature of responsive aiming and movement controls.