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Lidar systems testing considerations for field use
Picmausová, Helena ; Farlík, Jan ; Eichhorn, Marc ; Kieleck, Christelle
The aim of this paper is to offer a perspective on testing a commercially available lidar system in order to determine its suitability for various practical tasks including mapping, object recognition, and the potential in its coupling with another sensor, in this case a camera. Several mapping missions were carried out over the course of the experiment, with both the lidar and the camera mounted on an Unmanned Aircraft System. Aside from mapping ordinary objects like trees, vehicles, people, and ground elevation, a standardized test target was designed for the purposes of the experiment, and placed in detection area. Influence of external factors on system performance was evaluated, e.g. atmospheric conditions and material properties of scanned surfaces, especially their reflectivity. Post processing of obtained data was carried out, demonstrating the potential of overlaying multiple sensor data for easier object recognition, and an optimal use case for the system is suggested.
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Experimental audio effect based on dynamic signal filtering
Leitgeb, David
This paper deals with an audio effect that utilizes multiple types of digital signal processing to creatively produce various sound colors with musical signal as its input. Used signal processing techniques include: frequency filters, delay line with signal interpolation, low-frequency oscillators. The paper includes description of the structure of the proposed audio effect, approaches used during the implementation process and several examples of the functionality of its individual features. The proposed effect was developed using Matlab and its Audio Toolbox extension.
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NANOBLAST: Python Tool for Raw Nanopore Signal Processing
Suriak, Martin ; Nykrýnová, Markéta
Oxford Nanopore Technologies’ sequencers enable direct real-time DNA/RNA sequencing. While numerous tools aid in analyzing the nanopore output data, offering functions such as visualizing raw signals with highlighted nucleotide positions, none provide a complete solution for exporting analyzed data into a clear, comprehensive file. In response, a Python tool has been developed to streamline various tasks. This includes searching for specific nucleotide sequences using BLAST, plotting raw signals with detected nucleotide bases, and generating a comprehensive file containing all essential information. The tool integrates components for handling raw nanopore data, extracting crucial information from the basecalling process using SAM file handlers, and utilizing a BLAST search engine. Employing a comprehensive algorithm, it can handle both the old FAST5 and the novel POD5 formats, enabling the identification of any nucleotide sequence and its corresponding signal.
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Steady-state Thermal Analysis of Fault-tolerant PMSM During the Open-phase Mode of Operation
Sizonenko, Vitaliy
In this paper, the initial outline of lumped-parameter thermal network and steady-state thermal finite element analysis for the fault-tolerant permanent magnet synchronous machine is demonstrated. The calculations show that the faulty operational mode with two open phases leads to substantial overheating of the machine when the need for the constant or overload torque is present. This challenge can be overcome by utilizing the machine’s restricted operating time, with implementing transient thermal finite element analysis. Alternatively, the machine has the option to operate continuously at a reduced torque.
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Optical ray propagation through turbulent underwater space
Bartoň, Jan
This paper is aimed at the description of an optical ray propagating through underwater turbulent environment. In the first part, the description of the properties of underwater turbulence and its impact on the optical beam is presented. In the second part of the paper, method based upon Snell’s law is presented. In the third part, an approach using Ray tracing matrices is shown. The outcome of this thesis is a procedure for calculating the propagation of the optical beam through a turbulent underwater environment.
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Comparative Analysis of Gaussian Process Regression Modeling of an Induction Machine: Continuous vs. Mixed-Input Approaches
Bílek, Vladimír
This paper investigates the application of machine learning technique for modeling continuous and mixed-input parameters of electrical machines. The design of electrical machines typically requires the consideration of certain parameters as integer values due to their physical significance, including the number of stator/rotor slots, stator wires, and rotor bars. Traditional machine learning methods, which predominantly treat input parameters as purely continuous, may compromise modeling accuracy for such applications. To address this challenge, models capable of handling mixed-input parameters were used for the case study. Two training datasets were generated: one with purely continuous inputs and another with both continuous inputs and a categorical parameter, specifically, the number of stator conductors. Gaussian process regression was employed to build three models: two with continuous kernels, trained on both datasets, and one with a mixed kernel, trained only on the dataset containing a categorical parameter. A comparative analysis, demonstrated on a 1.5 kW induction machine - though applicable to a wide range of machines - illustrates the differences between the proposed approaches. The results highlight the importance of selecting an appropriate model for the Multi- Objective Bayesian optimization of electrical machines.
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Modelling of Magnetic Films: A Scientific Perspectives
Misiurev, Denis ; Vladimír, Holcman
Modeling magnetic thin films represents a dynamic intersection of scientific inquiry and technological progress, at the forefront of materials science exploration. Researchers use various computational methods, such as Monte Carlo simulations and molecular dynamics, to understand magnetism and thin-film growth on different surfaces. Recent advancements in multiscale modeling and machine learning have improved predictive abilities, leading to a better understanding of thin-film dynamics over different spatial and temporal scales. This interdisciplinary approach, coupled with advanced experimental techniques like in situ microscopy, promises significant advancements in magnetic materials. These advancements have wide-ranging implications in areas such as magnetic data storage, spintronics, and magnetic sensors. The integration of computational modeling and experimental validation marks a new era of scientific rigor, providing deep insights into the real-time behavior of magnetic films and enhancing the accuracy of predictive models. As researchers navigate unexplored territory, the field of magnetic thin-film modeling holds great promise for unlocking new possibilities in materials science and engineering. Through a combination of theoretical exploration and empirical validation, magnetic thin-film modeling is poised to drive innovation and revolutionize various industries in the future.
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Localization Accuracy of Autonomous Mobile Robots: A Comprehensive Evaluation of KISS-ICP Odometry
Cihlář, Miloš
This paper emphasizes the importance of accurate localization for the appropriate behavior of autonomous mobile robots. In particular, it provides a rigorous evaluation of the accuracy of the KISS-ICP algorithm, a lightweight lidar-based pose estimation algorithm known for its simplicity with minimal setup parameters. The algorithm works only with lidar data; unlike other more sophisticated SLAM algorithms, it does not use IMUs. The algorithm is shown to work under a variety of conditions and sensors. To comprehensively evaluate how the algorithm performs under varying conditions, several sensors were used in the experiments. The evaluation included a series of 3D lidars, namely Ouster OS0, OS1, and OS2. These lidars are characterized by different field-of-view settings and operating modes with different point per row. In order to facilitate this evaluation, a data set with extensive robot traversals over a distance of more than 9 km in two different environments, each equipped with different sensor types, was prepared.
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Perceptual Omnidirectional Image Quality: Subjective Ratings by Diverse User Aspects
Šimka, Marek ; Polak, Ladislav
This paper deals with an unconventional experiment of subjective quality assessment of omnidirectional images. Within the subjective tests with 36 volunteers, the study was carried out in terms of different aspects of each user. Based on the assessments of 100 images by each respondent, it offers a new source of results for further investigation and development of virtual reality (VR) content or quality of experience (QoE). It focuses on the subjective ratings of various types of users such as people using dioptric glasses, with VR experience, or users with vision impairments. The output is thus the results of the subjective scores, including an analysis of their correlations with several objective quality metrics. The initial results of this work suggest that, for example, subjects with corrected vision using dioptric glasses exhibit similar subjective perceptions of the quality of omnidirectional images as average respondents without visual impairment.
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Application of Auditory Masking based Speech Denoising in Automotive Environments
Malucha, Jan
This paper presents an application experiment of denoising speech in the automotive field. An algorithm based on the auditory masking phenomenon was programmed and deployed for this purpose. Synthetic composite recordings of speech and vehice interior noise were used for three types of vehicles equipped with internal combustion engines - truck, jeep and sports car. The final results after denoising process are evaluated by four speech quality metrics. The trend of quality improvement depending on the SNR of the input noisy signal is examined. A possibility of using denoised speech signals for further speech features analysis is briefly discussed.
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