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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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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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A Humanoid Robot on the Basis of Modules Controlled Through a Serial Half-duplex UART Bus
Matoušek, Sebastian ; Maršálek, Roman
Just a few decades ago the concept of humanoid robots assisting us at work would be something better suited for a movie plot than as a business plan. However, nowadays tests of such robots are already being performed, with big companies exploring the possibilities of such robots assisting in warehouses, robotic assistants roaming around airports directing lost passengers and more. In the future, such robots could very well be employed in many more areas of work – thanks to their construction being similar to humans, they integrate well into our everyday environment and make cooperation more intuitive. This thesis is the result of an attempt at building a small-scale humanoid robot. It expands upon the thesis ”Bipedal Walking Robot” [1] from last year, which described the process of building a two-legged robot capable of walking, at that time still lacking arms and a head, however. This thesis focuses on the expansion of the robot, mainly the addition of the upper limbs and a head missing in the previous project, thus finally creating a humanoid robot. Furthermore, a custom ecosystem of modules on the basis of the ESP32-C3FH4 microcontroller is discussed; communicating through a serial half-duplex UART bus, these modules were used to construct the upper body, smoothen the movement of the robot, and collect additional data about the robots movement. Such modules can be further used in other projects requiring the usage of numerous interconnected, yet independent, electronics parts.
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Innovative Control Systém for Water Supply Management at a Farm
Doležal, Aleš
The thesis deals with the implementation of an innovative control system for the management and supply of water to the farm. The innovation focuses on all areas of the control system such as hardware part as well as software part. In the hardware part, it is mainly about the communication between the PLC and the pumps in the boreholes Where contact switching has been replaced by fiber optic communication. Furthermore, the non-compliant components were replaced by components that are more suitable for this placation. In the software area, tools have been used to simplify both the programming and the actual operation of the control system. The use of structures, the use of equations, and, more efficient sampling of measured data. A PLC from Unitronics was selected to control the entire application. The program for the PLC was written in the UniLogic programming environment from Unitronics.
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Android Tracking Application Based on LTE Timing Advance
Michálek, Jakub
This article introduces a novel Android application developed in the Kotlin programming language, which enables users to tracking the location of their devices through the implementation of a custom algorithm. The focal point of this innovation lies in leveraging the Timing Advance parameter derived from the Long-Term Evolution (LTE) network to ascertain the most optimal track for location tracking. The technical overview describes the technologies used, while the tracking application chapter offers a detailed look at the architecture and implementation details. Special emphasis is placed on the use of Timing Advance and its role in the position tracking algorithm. The testing results confirm the efficiency and accuracy of the proposed solution. The article further deals with possible problems and proposed future extensions of the application. This work provides a comprehensive view of an innovative approach to location tracking in mobile devices.
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