Theses (Department of Electrical Engineering and Mechatronics)
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Theses collection of the Department of Electrical Engineering and Mechatronics. The collection was started in 2023.
At the University of Debrecen, in accordance with the 2022 amendment to the 2011 Higher Education Act, student theses are only accessible from devices connected to the University's Eduroam WiFi network or from a university IP address.
“The thesis or diploma work of a student who has successfully passed the final examination shall be stored in full in the academic system of the higher education institution, and a record shall be maintained thereof. The stored theses and diploma works – with the exception of parts classified as confidential in accordance with the relevant legislation – must be made accessible and searchable without restriction through the academic system.” Further info on the National Higher Education Act in Hungarian: Felsőokt. tv. (új) - 2011. évi CCIV. törvény a nemzeti felsőoktatásról - Hatályos Jogszabályok Gyűjteménye.
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Tétel Korlátozottan hozzáférhető 24 GHz Fractal MPA Antenna Development For EMI Testing In RF Spectrum MeasurementDobariya, Kashish; Istvan, Balajti; DE--Műszaki KarSquare Fractal antenna designing using AutoCAD and MATLAB softwares. It resonates at a frequency of 24 GHz. The poposed antenna is designed with different number of iterations, the PCB antenna ready for fabrication is shown and the simulated results are compared with various types of fractal antenna.Tétel Korlátozottan hozzáférhető 4Ch VNA-Based In-Situ Measurement Techniques For Phased Array AntennasAli, Syed Arbaz; István, Balajti; DE--Műszaki KarThe thesis explores the use of a 4-channel Vector Network Analyzer (4ChVNA) for in-situ measurement of phased array antennas, crucial in wireless technology. It emphasizes the importance and challenges of in-situ measurement tools like VNA for accurate, immediate assessments. The research focuses on characterizing antenna elements in phased arrays, enhancing understanding of their behavior for IoT sensor antenna performance requirements. This involves employing specialized tools and devising effective measurement strategies. Additionally, it evaluates the frequency response of "L" band phased arrays, offering insights into their operating bandwidth and resonance. Ultimately, this work enhances existing knowledge and sets the stage for future developments in the field.Tétel Korlátozottan hozzáférhető A Star Algorithm Simulation Vs Real World Obstacle AvoidanceIheanacho , Destiny; Almusawi , Husam; DE--Műszaki KarIn this thesis the A star algorithm is simulated in a grid based MATLAB environment then it is implemented in a real world grid environment using a built mobile robot to carry this out. The Algorithm is analysed in both scenarios to see the disadvantages and benefits. The algorithm is also compared alongside other algorithms to see its strength and weakness in different environments. Through the analysis some improvements and ideas are propsed.Tétel Korlátozottan hozzáférhető Advanced Health Monitoring System with Mobile ApplicationSayed, Hana Hesham Ibrahim; István, Balajti; DE--Műszaki KarThis paper outlines the development of a prototype health monitoring system. This system integrates five specifically chosen sensors based on their plethora of connections and low cost, which address cost-efficiency concerns. The five sensors are heart rate, pulse oximetry, temperature, galvanic skin response (GSR), and blood glucose for diabetes monitoring. The Raspberry Pi 5 was selected as the controller for this system. This addresses accessibility concerns since it’s an open-source platform. In today's world, artificial intelligence represents a significant breakthrough. As a result, machine learning algorithms were utilised for this project. Several machine learning models were tested to choose the most suitable and appropriate for anomaly identification and data analysis. This paper also presents the integration of a mobile application for visualising the data and offering insights into the user’s health status. This system aims to monitor a person’s health data in real time, facilitating early detection of potential health issues.Tétel Korlátozottan hozzáférhető Advanced Radar Signal Processing for Enhanced Object Detection in ADASWadud, Amin Ul; István, Balajti; DE--Műszaki KarThis dissertation is concerned with the enhancement of object recognition in an autonomous vehicle through advanced radar signal processing. It also explains the radar’s sequencing in importance as it is not adversely affected by weather conditions as is the case with LiDAR and cameras. Some of the developments that made this possible are the adoption of the 77 GHz frequency band, adaptive filtering and the use of machine learning. The work resolves some of the issues that include presence of interference, poor detection performance in complex environments, and high installation cost. It underscores the significance of sensing from multiple sources by describing the fusion of radar with LiDAR and cameras for a greater understanding of the environment. To this end, a series of simulations in MATLAB have been performed to support the proposed techniques and optimize the safety and reliability of AVs.Tétel Korlátozottan hozzáférhető AI-Powered Drone Integration for Obstacle AvoidanceAlTamimi, Jebril; Korsoveczki, Gyula; DE--Műszaki KarThis article discusses how drones are transforming various industries, such as agriculture and surveillance, yet they still face difficulties in autonomously navigating complex environments. To address this, the article introduces a method that combines artificial intelligence (AI) with drone systems to enhance their ability to avoid obstacles. The approach involves the use of deep reinforcement learning (DRL) techniques to better the navigation skills of drones. The authors detail their integrated hardware and software strategy, emphasizing their preliminary findings and the obstacles they encountered. The primary aim of this research is to further the use of autonomous drones in real-life situations.Tétel Embargó alatt ANALYSIS AND CONTROL STRATEGY DEVELOPMENT OF AN ELECTRIC VEHICLE REGENERATIVE BRAKING SYSTEMStocco Richter, Camila; Almusawi, Husam Abdulkareem; DE--Műszaki KarThis thesis investigates the development of a temperature-dependent regenerative braking system for electric vehicles, describing the implementation of fuzzy logic control to allocate braking force between regenerative and conventional braking techniques. The control strategy employs thermal feedback techniques that improve energy recovery dynamically, while simultaneously mitigating the risks associated with overheating and battery degradation. Simulations conducted with MATLAB/Simulink demonstrate a 10% improvement in driving range and a 16.7% extension in operational cycles. The proposed system exhibited efficiencies of 0.58 in POWER mode and 0.73 in regenerative mode, while simultaneously achieving a reduction in energy losses associated with the motor and controller by 63.6%. The findings indicate the significance of regenerative braking within the context of electric vehicle research and development.Tétel Embargó alatt ANALYSIS AND CONTROL STRATEGY DEVELOPMENT OF AN ELECTRIC VEHICLE REGENERATIVE BRAKING SYSTEMStocco Richter, Camila; Almusawi, Husam Abdulkareem; DE--Műszaki KarThis thesis investigates the development of a temperature-dependent regenerative braking system for electric vehicles, describing the implementation of fuzzy logic control to allocate braking force between regenerative and conventional braking techniques. The control strategy employs thermal feedback techniques that improve energy recovery dynamically, while simultaneously mitigating the risks associated with overheating and battery degradation. Simulations conducted with MATLAB/Simulink demonstrate a 10% improvement in driving range and a 16.7% extension in operational cycles. The proposed system exhibited efficiencies of 0.58 in POWER mode and 0.73 in regenerative mode, while simultaneously achieving a reduction in energy losses associated with the motor and controller by 63.6%. The findings indicate the significance of regenerative braking within the context of electric vehicle research and development.Tétel Korlátozottan hozzáférhető Application of Collaborative Robot in Automotive IndustryIssenomanov, Sanzhar; Almusawi, Husam Abdulkareem; DE--Műszaki KarThe thesis investigates how the collaborative robot ("UR5e cobot" with "OnRobot RG gripper") can be used in the parts sorting process, an important part of the preparation for assembling operations. This process has been described as demanding lots of physical work within tight time limits. The goal was to automate this sorting process with cobots so that it increases output and reduces the physical efforts of human workers.Tétel Korlátozottan hozzáférhető Applying Optimization Algorithms for Robot Path PlanningHassan, Mohamed; Al Musawi , Husam; DE--Műszaki KarThis thesis presents a hybrid optimization algorithm combining Genetic Algorithm and Particle Swarm Optimization to improve robot path planning in static and dynamic environments. The algorithm was tested in ROS 2 and Gazebo simulations using a two-wheeled robot with a LiDAR sensor. Results showed that the hybrid approach outperformed individual GA and PSO by generating shorter paths, better obstacle avoidance, and smoother motion. While effective, future work could enhance the algorithm by incorporating machine learning for real-time decision-making and obstacle prediction in complex environments.Tétel Korlátozottan hozzáférhető Assessing Radar Performance in Automotive IndustryKhan, Fardeen Ahmed; Istvan, Balajti; DE--Műszaki KarThe research is focused on developing phased array antennas and automotive radars operating on different frequencies and simulating them in various weather conditions to compare their performance.Tétel Korlátozottan hozzáférhető Automobile Radar Sensing with AI-Enhanced Collision AvoidanceFarhat, Georges; Balajti, Istvan; DE--Műszaki KarIn enhancing driving safety, this investigation employs a combination of artificial intelligence (AI) and advanced radar systems to proactively predict and prevent collisions, thereby elevating overall vehicle safety. The study delves into the pivotal role played by radar-based systems in preempting crashes, highlighting key concepts that advocate for the collaborative use of AI and radar to bolster road safety, drawing insights from pertinent literature. Going beyond theoretical considerations, the research takes an empirical approach, conducting a thorough risk evaluation, identifying potential obstacles, and formulating safety protocols. Utilizing carefully designed measurement setups for processing radar signals, the study aims to facilitate effective braking and smooth cruising, outlining four crucial measurement steps and techniques for data interpretation and evaluation. Through systematic operation and testing initiatives, the research critically examines the effectiveness of collision prevention through simulations and theoretical foundations. Furthermore, the paper explores future directions by investigating how the integration of technology into smart transportation systems could further enhance road safety. This endeavor represents a significant stride in advancing vehicle safety by integrating AI and radar technologies. The research aspires to cultivate an environment where individuals feel safer whether driving personal vehicles or utilizing public transit. Through the provision of proactive collision avoidance services and competence development programs, the goal is to foster an atmosphere conducive to safety for all individuals involved.Tétel Korlátozottan hozzáférhető Automotive Radar Array Antennas Enhancing Car Safety and AutonomyKuteich, Alaan; Balajti, Istvan; DE--Műszaki KarThis thesis explores sensor fusion techniques and hardware solutions to advance self-driving cars. It provides a historical overview, conducts a literature review, and analyzes various sensors (cameras, LiDAR, radar, IMUs) in terms of cost and performance. The research introduces advanced sensor fusion algorithms and cost-effective hardware solutions to seamlessly integrate data from different sensors, addressing challenges in existing systems. Real-world testing and simulations validate the proposed system's effectiveness under diverse driving conditions, contributing valuable insights to the autonomous vehicle industry and paving the way for further advancements in self-driving technology.Tétel Embargó alatt AUTONOMOUS NAVIGATION OF MOBILE ROBOT IN DYNAMIC ENVIRONMENTS USING DEEP REINFORCEMENT LEARNINGMayorga Mayorga, Oscar Agustin; Dr. PhD. Almusawi , Husam Abdulkareem; DE--Műszaki KarThe aim of this work is to develop an autonomous navigation algorithm based on artificial intelligence called Deep Reinforcement Learning for human-robot mobile interaction. The development of the robot has been considered in dynamic environments, which have been scanned by means of a 2D Lidar sensor, taking into account the sudden change of direction of objects. The proposed research has been exploratory and experimental, carrying out a search of the state of the art in terms of autonomous navigation and human-robot mobile interaction. Artificial intelligence has been used as the main technique, specifically the union of deep neural renders with Reinforcement Learning. This allows the robot to learn based on the reward or penalty according to the results sent by the deep neural network.Tétel Korlátozottan hozzáférhető Budget antenna pattern In Situ measurements of 24 GHz and 76-81 GHz mmWaveAliyev, Kamran; Balajti, Istvan; DE--Műszaki KarIndustry 4.0 is revolutionizing the automotive industry by enabling real-time decision making, enhanced productivity, flexibility, and agility. The automotive industry is a pioneer in radar use in civil matters, such as collision avoidance and autonomous driving. This study focuses on developing tools for in situ measurement of mmWave radars operating on the millimetre wave frequency range. The investigation aims to imitate real-life scenarios and analyze antenna patterns under different environmental conditions, particularly sunlight's impact on signal-to-noise ratio (SNR) attenuation and interference. The developed in situ measurement equipment allows for experiments to obtain and analyze data for improvements in automobile mmWave radar systems in the frequency bands of 24 GHz and 76-81 GHz.Tétel Korlátozottan hozzáférhető CA MODELLING AND IMPROVED ALGORITHM: SAFE AND EFFICIENT DECISION MAKING FOR AUTONOMOUS DRIVING SYSTEMTao, Yufei; Almusawi, Husam Abdulkareem; DE--Műszaki KarThis study proposes a multi-model combinatorial congestion mitigation approach to solve the traffic problem of autonomous cars. The proposed model framework consists of three parts: a road modelling module based on meta cellular automata, a traffic flow speed prediction module consisting of an optimised long and short-term memory algorithm using a sparrow search algorithm, and a following distance prediction module that determines the optimal safe following distance using an adaptive cruise control algorithm. In simulation, this intelligent traffic model for autonomous cars can be applied to various complex traffic scenarios to improve traffic efficiency while reducing the collision risk of autonomous cars.Tétel Embargó alatt COMPACT SMART SENSOR DESIGN FOR HOUSEHOLD USAGEKabesh, Ahmed; Mikuska, Robert; DE--Műszaki KarThis thesis presents the design and implementation of a smart home system focused on improving energy efficiency and enhancing security. It leverages the ESP32 microcontroller for wireless communication and low power consumption. The system integrates key components like the DHT22 sensor, PIR motion sensor, RFID module, and relay-controlled devices, all mounted on a custom two-layer PCB. Users can control and monitor the system remotely using the Blynk app. The goal is to offer an affordable, scalable, and user-friendly smart home solution.Tétel Korlátozottan hozzáférhető COMPARISON OF SIMPLIFIED AND COMPLEX MODELS OF A THREE-PHASE PERMANENT MAGNET BRUSHLESS DIRECT CURRENT MOTOR IN ELECTRIC VEHICLESGhareeb, Abdullah Waheeb Jaffer Omer; Babangida, Aminu; DE--Műszaki KarOne of the most viable options presented for eliminating environmental concerns while keeping energy efficiency in the field of mobility and transportation, is electrical vehicles (EV). The overall performance of the electrical vehicles can be determined by their electric motors. The utilization of the permanent magnet brushless direct current (PMBLDC) motors as an applicable selection for the electrification of the vehicles. In this research, a deep examination and performance evaluation of the use of PMBLDC motors in electric vehicles, in precise a comparison of a simplified and complex models of a three-phase, 4-pole Y-connected PMBLDC motor was performed. Performance evaluation and enhancement and assessment of behavior under ambient temperatures and various controlling algorithms, was done by creating the models using MATLAB/Simulink environment. The research resulted in optimizing the overall performance of the motor as well as the full electric powertrain.Tétel Embargó alatt Comparisons of artifical intelligence algorithms for collision avoidance in the field of Collaborative robotsJagoda Don, Danidu Rushmika Jagoda; Peter, David Nasser; DE--Műszaki KarV. SUMMARY The main objective of this thesis was to compare artificial intelligence algorithms for collision avoidance in the field of collaborative robots. Moreover, the main task was to identify algorithms that are used for collision avoidance and use these methods as a part of a neural network, to test and understand which algorithm is the most efficient, accurate, and fastest to incorporate learning. Apart from the algorithms we dived into the implementing a neutral neural network architecture which doesn’t complement any algorithm. Benefit of using the proposed Feed-Forward Neural Network is that learning happens unidirectionally and allows the algorithms themselves to make their own modifications in order to obtain the best-predicted results. Apart from this, the main challenge was to design a couple of digit fields where the algorithm can be tested for the collaborative robot. 4 environments were created, and every single environment had different amounts of dynamic and static objects. The environments surrounding from 1-4 were built to test the KUKA LBR4+. The pretext under the design of these environments was that the difficulty should arise from the first to the last one. The 3rd simulation environment consisted of the most objects, and this resulted in showcasing unexpected results. Conjointly, environment 4 was built to be the most unpredictable, trying to replicate a real-life scenario. Moreover, all four, Levenberg-Marquardt, Resilient Backpropagation, Scaled Conjugate Gradient, and Bayesian Regularisation were simulated in all the environments. A total of 64 simulations were conducted, the breakdown of the 64 is as follows; 4 environments to test, 4 algorithms proposed, and repeating the simulation 4 times to obtain an average and observe more accurate results. The creation of a neural network to implement an AI solution was the main purpose of this thesis.Tétel Korlátozottan hozzáférhető Control and data processing of creep test machineMadbak, Hanna; Almusawi, Husam; DE--Műszaki KarThe Department of Mechanical Engineering was assigned an industrial project that involves measuring the deformation of pipelines over a period of 42 days under constant stress. In response to this requirement,a creep test machine was designed and constructed. However, given the need to take multiple measurements at specific intervals, it is challenging for an individual to be present throughout the entire duration. Therefore, Automating the entire process was the ideal solution.