Fakulta elektrotechniky a informatiky / Faculty of Electrical Engineering and Informatics
Stálý URI pro tuto komunituhttps://hdl.handle.net/10195/3847
Práce obhájené před rokem 2008 jsou uloženy pouze v kolekci Vysokoškolské kvalifikační práce
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Článekpeer-reviewedpostprint Omezený přístup Survey of Point Cloud Registration Methods and New Statistical Approach(MDPI, 2023) Marek, Jaroslav; Chmelař, PavelThe use of a 3D range scanning device for autonomous object description or unknown environment mapping leads to the necessity of improving computer methods based on identical point pairs from different point clouds (so-called registration problem). The registration problem and three-dimensional transformation of coordinates still require further research. The paper attempts to guide the reader through the vast field of existing registration methods so that he can choose the appropriate approach for his particular problem. Furthermore, the article contains a regression method that enables the estimation of the covariance matrix of the transformation parameters and the calculation of the uncertainty of the estimated points. This makes it possible to extend existing registration methods with uncertainty estimates and to improve knowledge about the performed registration. The paper's primary purpose is to present a survey of known methods and basic estimation theory concepts for the point cloud registration problem. The focus will be on the guiding principles of the estimation theory: ICP algorithm; Normal Distribution Transform; Feature-based registration; Iterative dual correspondences; Probabilistic iterative correspondence method; Point-based registration; Quadratic patches; Likelihood-field matching; Conditional random fields; Branch-and-bound registration; PointReg. The secondary purpose of this article is to show an innovative statistical model for this transformation problem. The new theory needs known covariance matrices of identical point coordinates. An unknown rotation matrix and shift vector have been estimated using a nonlinear regression model with nonlinear constraints. The paper ends with a relevant numerical example.Článekpeer-reviewedpostprint Otevřený přístup Speaker Verification Using Autoregressive Spectrum of Speech Signal in Composite Vector Stochastic Processes Model Representation(Institute of Mechanics of Continua and Mathematical Sciences, 2019) Chmelařová, Natalija; Tykhonov, Vyacheslav A.; Bezruk, Valerij M.; Chmelař, Pavel; Rejfek, LubošThis paper deals with the speaker verification system similar to a fingerprint or an eye scanner. For these purpose a long-term words' model and its spectral characteristics were used. The speaker verification method uses the word's sound parametric spectrum factorization in composite vector stochastic process representation based on the multiplicative autoregressive model. The developed method enables to receive the words' features with stable characteristics for the same speaker and differ for different speakers. During the training phase speaker's etalon frequencies has to be estimated for a pronounced word repeated several times. In the verification phase a speaker pronouncing the same word, word's frequencies are estimated and compared with the etalon frequencies database to find the best match or his deny. The results presented in the paper showed the high correct identification probability.Článekpeer-reviewedpublished version Otevřený přístup Neural Networks Application for Processing of the Data from the FMICW Radars(MDPI, 2019) Rejfek, Luboš; Nguyen, Tan N.; Chmelař, Pavel; Beran, Ladislav; Phuong, Tran T.In this paper the results of the Neural Networks and machine learning applications for radar signal processing are presented. The radar output from the primary radar signal processing is represented as a 2D image composed from echoes of the targets and noise background. The Frequency Modulated Interrupted ContinuousWave (FMICW) radar PCDR35 (Portable Cloud Doppler Radar at the frequency 35.4 GHz) was used. Presently, the processing is realized via a National Instruments industrial computer. The neural network of the proposed system is using four or five (optional for the user) signal processing steps. These steps are 2D spectrum filtration, thresholding, unification of the target, target area transforming to the rectangular shape (optional step), and target board line detection. The proposed neural network was tested with sets of four cases (100 tests for every case). This neural network provides image processing of the 2D spectrum. The results obtained from this new system are much better than the results of our previous algorithm.Článekpeer-reviewedpublished Otevřený přístup Position estimation of robotic platform using optical flow(2018) Beran, Ladislav; Rejfek, Luboš; Chmelař, Pavel; Matoušek, DavidThis paper deals with a base research of an alternative type of navigation for our project ARES (Autonomous Research Exploration System). This system is focused on exploration of unknown areas. The navigation of this platform is based on fusion of several navigation methods. The first method is based on the visual odometry using SURF (Speeded Up Robust Feature). The second navigation method is based on Hector Slam and Lidar sensor. The third method is based on optical flow. The implementation of third method based on the Lucas-Kanade method is descripted in this paper.Článekpeer-reviewedpublished Otevřený přístup Filtration of the FMICW radar output signals by the advanced windows(2017) Rejfek, Luboš; Chmelařová, Natalija; Beran, Ladislav; Chmelař, PavelThis paper deals with the special types of windows application on the two dimensional spectrum obtained using the FMICW radar. This processing will improve the signal interpretation for the user. We want to implement this signal processing in our SW for the automatic detection of targets. The obtained results are described in detail with the recommendation for achieving the best processing result. We developed several windows, which can be used, for our algorithm.Článekpeer-reviewedpublished Otevřený přístup Point Cloud Plane Visualization by Using Level Image(2018) Chmelař, Pavel; Rejfek, Luboš; Beran, Ladislav; Chmelařová, Natalija; Dobrovolný, MartinThis paper presents the point cloud plane visualization by using the level image. A created level image indicates the point presence in a space at a specific level. By knowledge its origin position in a space, the physical pixel size and the detected rotation angle we can easily visualize planes in an analyzed point cloud. The connection of image processing methods with the physical measurement points offers besides the visualization also obtaining important properties about a scanned space. The described algorithm includes also a color point cloud visualization. Presented results showing the level images advantages for point clouds processing.Článekpeer-reviewedpostprint Otevřený přístup Comparison of Digisonde and CDSS measurement for the monitoring of the existence of the Ionospheric communication channel(Institute of Advanced Science Extension, 2016) Rejfek, Luboš; Mošna, Zbyšek; Beran, Ladislav; Chmelař, Pavel; Chmelařová, Natalija; Dobrovolný, Martin; Rozsíval, PavelThis paper describes systems for detection of the availability of the Ionospheric communication channel. The Digisonde and the Continuous Doppler Sounding System for the detection of the availability are used and are compared by means of here. Their precision, correctness and benefits of these systems are tested at the observatory Pruhonice in Czech Republic. The last part of this paper deals with ambiguities of both systems.Článekpeer-reviewedpostprint Otevřený přístup Analysis of radar signals by PSD methods(Institute of Advanced Science Extension, 2016) Rejfek, Luboš; Beran, Ladislav; Chmelař, Pavel; Chmelařová, Natalija; Pek, Viktor; Fišer, OndřejThe article is targeted on an application of selected Power Spectral Density (PSD) methods to analyze the rador signals with frequency modulated pulses. First, simulated signals to test the power spectral density methods themselves are used. The second step was the test of the methods applied on real measurement. The primary aim of the work was to focus on the analysis of the long targets by parametric power spectral density methods. The involved methods are Periodogram, Multiple Signal Classification (MUSIC) method and Eigen value method
