Global ETD Search

41	Autonomní jednokanálový deinterleaving / Autonomous Single-Channel Deinterleaving Tomešová, Tereza January 2021 (has links) This thesis deals with an autonomous single-channel deinterleaving. An autonomous single-channel deinterleaving is a separation of the received sequence of impulses from more than one emitter to sequences of impulses from one emitter without a human assistance. Methods used for deinterleaving could be divided into single-parameter and multiple-parameter methods according to the number of parameters used for separation. This thesis primarily deals with multi-parameter methods. As appropriate methods for an autonomous single-channel deinterleaving DBSCAN and variational bayes methods were chosen. Selected methods were adjusted for deinterleaving and implemented in programming language Python. Their efficiency is examined on simulated and real data.
42	Získávání znalostí z multimediálních databází / Knowledge Discovery in Multimedia Databases Málik, Peter January 2011 (has links) This master"s thesis deals with the knowledge discovery in multimedia databases. It contains general principles of knowledge discovery in databases, especially methods of cluster analysis used for data mining in large and multidimensional databases are described here. The next chapter contains introduction to multimedia databases, focusing on the extraction of low level features from images and video data. The practical part is then an implementation of the methods BIRCH, DBSCAN and k-means for cluster analysis. Final part is dedicated to experiments above TRECVid 2008 dataset and description of achievements.
43	Snížení paměťové náročnosti stavového zpracování síťového provozu / Memory Reduction of Stateful Network Traffic Processing Hlaváček, Martin January 2012 (has links) This master thesis deals with the problems of memory reduction in the stateful network traffic processing. Its goal is to explore new possibilities of memory reduction during network processing. As an introduction this thesis provides motivation and reasons for need to search new method for the memory reduction. In the following part there are theoretical analyses of NetFlow technology and two basic methods which can in principle reduce memory demands of stateful processing. Later on, there is described the design and implementation of solution which contains the application of these two methods to NetFlow architecture. The final part of this work summarizes the main properties of this solution during interaction with real data.
44	Modul pro shlukovou analýzu systému pro dolování z dat / Cluster Analysis Module of a Data Mining System Hlosta, Martin January 2010 (has links) This thesis deals with the design and implementation of a cluster analysis module for currently developing datamining system DataMiner on FIT BUT. So far, the system lacked cluster analysis module. The main objective of the thesis was therefore to extend the system of such a module. Together with me, Pavel Riedl worked on the module. We have created a common part for all the algorithms so that the system can be easily extended to other clustering algorithms. In the second part, I extended the clustering module by adding three density based clustering aglorithms - DBSCAN, OPTICS and DENCLUE. Algorithms have been implemented and appropriate sample data was chosen to verify theirs functionality.
45	Analysis and Development of a Lower Extremity Osteological Monitoring Tool Based on Vibration Data Veta, Jacob E. 28 July 2020 (has links) No description available. Mechanical Engineering Biomedical Engineering Biomechanics Osteopathic Medicine system identification vibration osteoporosis dbscan human model bone lead vibration model bone shock absorption structural health monitoring human monitoring smart wearables model reconstruction bone damping
46	Clustering on groups for human tracking with 3D LiDAR Utterström, Simon January 2023 (has links) 3D LiDAR people detection and tracking applications rely on extracting individual people from the point cloud for reliable tracking. A recurring problem for these applications is under-segmentation caused by people standing close or interacting with each other, which in turn causes the system to lose tracking. To address this challenge, we propose Kernel Density Estimation Clustering with Grid (KDEG) based on Kernel Density Estimation Clustering. KDEG leverages a grid to save density estimates computed in parallel, finding cluster centers by selecting local density maxima in the grid. KDEG reaches a remarkable accuracy of 98.4%, compared to HDBSCAN and Scan Line Run (SLR) with 80.1% and 62.0% accuracy respectively. Furthermore, KDEG is measured to be highly efficient, with a running time similar to state-of-the-art methods SLR and Curved Voxel Clustering. To show the potential of KDEG, an experiment with a real tracking application on two people walking shoulder to shoulder was performed. This experiment saw a significant increase in the number of accurately tracked frames from 5% to 78% by utilizing KDEG, displaying great potential for real-world applications. In parallel, we also explored HDBSCAN as an alternative to DBSCAN. We propose a number of modifications to HDBSCAN, including the projection of points to the groundplane, for improved clustering on human groups. HDBSCAN with the proposed modifications demonstrates a commendable accuracy of 80.1%, surpassing DBSCAN while maintaining a similar running time. Running time is however found to be lacking for both HDBSCAN and DBSCAN compared to more efficient methods like KDEG and SLR. / <p>Arbetet är gjort på plats i Tokyo på Chuo Universitet utan samverkan från Umeå Universitet såsom utbytesprogram eller liknande.</p><p>Arbetet är delvis finansierat av Scandinavia-Japan Sasakawa Foundation.</p><p>Arbetet gick inte under vanlig termin, utan började 2023/05/01 och slutade 2023/08</p> Computer Vision Computer Science AI Machine Learning clustering Kernel Density Clustering tracking LiDAR 3D LiDAR tracking human pedestrian real time Datavetenskap Dataseende clustering SLR CVC KDEG KDE Kernel Density Clustering HDBSCAN DBSCAN LiDAR point cloud tracking human pedestrian Computer Sciences Datavetenskap (datalogi)

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