Elektronika ir Elektrotechnika https://eejournal.ktu.lt/index.php/elt <div><em>ELEKTRONIKA IR ELEKTROTECHNIKA</em> (ISSN 1392-1215) is a peer-reviewed open access bimonthly research journal of Kaunas University of Technology.</div> <p>The research journal <em>ELEKTRONIKA IR ELEKTROTECHNIKA</em> publishes original research papers on featuring practical developments that might have a significant impact in the field of <em>electronics and electrical engineering</em>, and focuses on automation, robotics &amp; control; automotive electronics; electric vehicles; electrical engineering; electronic measurements; electronics; high frequency technologies, microwaves; micro &amp; nano-electronics; power electronics; renewable energy; signal technologies; telecommunications engineering. It is aimed not only to researchers of certain field , but also to the wider public.</p> <p><strong><em>WoS</em></strong><strong><em>: </em></strong><em>Impact</em> <em>Factor</em><em> 0.707 (</em><em>2019); </em><em>5-Year </em><em>Impact</em> <em>Factor</em> <em>0.656 (</em><em>2019) </em><strong><em>Scopus</em></strong><strong><em>:</em></strong> <em>SCImago</em> <em>Journal</em> <em>Rank</em><em> 0.18 (2019)</em></p> Kaunas University of Technology en-US Elektronika ir Elektrotechnika 1392-1215 <p>The copyright for the paper in this journal is retained by the author(s) with the first publication right granted to the journal. The authors agree to the <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="noopener">Creative Commons Attribution 4.0 (CC BY 4.0)</a> agreement under which the paper in the Journal is licensed.</p> <p>By virtue of their appearance in this open access journal, papers are free to use with proper attribution in educational and other non-commercial settings with an acknowledgement of the initial publication in the journal.</p> Editorial Board https://eejournal.ktu.lt/index.php/elt/article/view/31707 Elektronika ir Elektrotechnika Copyright (c) 2022 Elektronika ir Elektrotechnika 2022-06-28 2022-06-28 28 3 2 3 Airport Wildlife Hazard Management System https://eejournal.ktu.lt/index.php/elt/article/view/31418 <p>Aviation reports indicate that between the years of 1988 and 2019 there were 292 human fatalities and 327 injuries that had been reported due to wildlife strikes with airplanes. To minimize these numbers a new approach to airport Wildlife Hazard Management (WHM) is presented in the following article. The proposed solution is based on the data fusion of thermal and vision streams which are used to improve the reliability and adaptability of the real-time WHM system. The system is designed to operate in all environmental conditions and provides an advance information of the fauna presence at the airport's runway.</p> <p>The proposed sensor fusion approach was designed and developed using user driven design methodology. Moreover, the developed system has been validated in real case scenarios and previously installed at an airport. Performed tests proved detection capabilities during day and night of dog sized animals up to 300 meters. Moreover, by using machine learning algorithms during daylight the system was able to classify person sized objects with over 90% efficiency up to 300 meters and dog sized objects up to 200 meters. The overall threat level accuracy based on the three safety zones, was 94%.</p> Damian Dziak Dawid Gradolewski Szymon Witkowski Damian Kaniecki Adam Jaworski Michał Skakuj Wlodek J. Kulesza Copyright (c) 2022 Damian Dziak, Dawid Gradolewski, Szymon Witkowski, Damian Kaniecki, Adam Jaworski, Michał Skakuj, Wlodek J. Kulesza 2022-06-28 2022-06-28 28 3 45 53 10.5755/j02.eie.31418 Retinal Vessel Segmentation Based on the Anam-Net Model https://eejournal.ktu.lt/index.php/elt/article/view/30594 <p>Accurate segmentation of retinal blood vessels can help ophthalmologists diagnose eye-related diseases such as diabetes and hypertension. The task of segmentation of the vessels comes with a number of challenges. Some of the challenges are due to haemorrhages and microaneurysms in fundus imaging, while others are due to the central vessel reflex and low contrast. Encoder-decoder networks have recently achieved excellent performance in retinal vascular segmentation at the trade-off of increased computational complexity. In this work, we use the Anam-Net model to accurately segment retinal vessels at a low computational cost. The Anam-Net model consists of a lightweight convolutional neural network (CNN) along with bottleneck layers in the encoder and decoder stages. Compared to the standard U-Net model and the R2U-Net model, the Anam-Net model has 6.9 times and 10.9 times fewer parameters. We evaluated the Anam-Net model on three open-access datasets: DRIVE, STARE, and CHASE_DB. The results show that the Anam-Net model achieves better segmentation accuracy compared to several state-of-the-art methods. For the DRIVE, STARE, and CHASE DB datasets, the model achieved {sensitivity and accuracy} of {0.8601, 0.9660}, {0.8697, 0.9728}, and {0.8553, 0.9746}, respectively. On the DRIVE, STARE, and CHASE_DB datasets, we also conduct cross-training experiments. The outcome of this experiment demonstrates the generalizability and robustness of the Anam-Net model.</p> Khursheed Aurangzeb Syed Irtaza Haider Musaed Alhussein Copyright (c) 2022 Khursheed Aurangzeb, Syed Irtaza Haider, Musaed Alhussein 2022-06-28 2022-06-28 28 3 54 64 10.5755/j02.eie.30594 Deep Learning in Analysing Paranasal Sinuses https://eejournal.ktu.lt/index.php/elt/article/view/31133 <p>Deep neural network-based diagnostic tools have gained state-of-the-art performance in the medical field in recent years. Diagnostic accuracy has become very critical for medical treatments. This paper proposes a simple and novel deep learning-based system for the analysis of paranasal sinuses conditions. In this work, we focus on analysing the paranasal sinuses on CT images automatically, providing physicians with high-accuracy diagnosis. The proposed system enables one to reduce the number of images to be searched in a CT scan for a patient automatically, and also it provides automatic segmentation for marking and cropping the paranasal sinuses region. Thus, the proposed system significantly decreases the data required in the training phase with a gain in computational efficiency while maintaining high-accuracy performance. The proposed algorithm also makes the required segmentation automatically without manual cropping and yields outstanding performance on detecting abnormalities in the sinuses. The proposed approach has been tested on real CT images and achieved an accuracy rate of 98.52 % with a sensitivity of 100 %.</p> Serkan Ozbay Orhan Tunc Copyright (c) 2022 Serkan Ozbay, Orhan Tunc 2022-06-28 2022-06-28 28 3 65 70 10.5755/j02.eie.31133 Optimization of Interval Type-2 Fuzzy Logic Controller Using Real-Coded Quantum Clonal Selection Algorithm https://eejournal.ktu.lt/index.php/elt/article/view/31148 <p>In recent years, quantum computing has gained immense popularity with the production of real quantum computers. Researchers have developed quantum-inspired evolutionary algorithms (QIEAs) to solve combinatorial optimization problems and have obtained successful results. As a special case of QIEAs, real-coded quantum evolutionary algorithm (RCQEA) is used in the optimization of high-dimensional complex problems. In this study, a novel mechanism of the quantum rotation gate (QRG) that is used to determine the rotation angle of the qubit in the RCQEA is introduced and implemented to accelerate the evolutionary process and increase the possibility of finding the optimal solution. Moreover, the skeleton of RCQEA is modified by using a clonal selection mechanism, and the real-coded quantum clonal selection algorithm (RCQCSA) is developed. Our proposed QRG accelerates the convergence speed of the algorithm. The main purpose of this study is to present a more effective algorithm that inspires quantum computing principles for optimizing the interval type-2 fuzzy logic controller (IT2FLC) membership functions (MFs). In this study, four different comparisons are made with these two different algorithms that have the original version of QRG and our proposed QRG. Optimized IT2FLC provides stabilization of the inverted pendulum system. The results show that the RCQCSA having our proposed QRG outperforms RCQEA in stabilizing the inverted pendulum system by optimizing the IT2FLC parameters.</p> Esra Satir Ekrem Baser Copyright (c) 2022 Esra Satir, Ekrem Baser 2022-06-28 2022-06-28 28 3 4 14 10.5755/j02.eie.31148 Design of a Dynamic Demand Response Model Through Intelligent Clustering Algorithm Based on Load Forecasting in Smart Grid https://eejournal.ktu.lt/index.php/elt/article/view/30596 <p>The development of smart metering technology empowers power reforms, which allows effective implementation of demand response programs to effectively operate the power grid. The systematic analysis of smart meter data plays a vital role for both consumers and utilities to reduce their costs and improve the efficiency of power management. In this paper, a machine learning algorithm is proposed to recommend the appropriate Demand Response (DR) program for the consumer in a real-time environment, tailored with dynamic pricing. The systematic recommendation can be made by integrating time series forecasting, consumer clustering, and DR analysis. The smart meter data of the 28 consumers for 108 weeks are recorded and applied to the ARIMA time series forecast algorithm. The smart meter data and ARIMA time series forecast data are combined and fed to the Agglomerative Hierarchical clustering algorithm to cluster consumers based on their usage and demand pattern. Clusters are analysed to identify a suitable DR program for the consumer. The results show that the proposed machine learning method effectively clusters consumers and implements the DR program in the smart grid environment.</p> Priya Lakshmanan Venugopal Gomathi B Copyright (c) 2022 Priya Lakshmanan, Venugopal Gomathi B 2022-06-28 2022-06-28 28 3 15 23 10.5755/j02.eie.30596 Multi-Criteria Optimization of Vehicle-to-Grid Service to Minimize Battery Degradation and Electricity Costs https://eejournal.ktu.lt/index.php/elt/article/view/31238 <p>Increased use of renewable energy sources in energy sector as well as improvements and electrification in transportation sector significantly contribute to reduction of green-house gasses emissions and mitigation of problems with fossil fuel dependency. Optimal integration of electric vehicles (EVs) into the grids and their charging/discharging schedules have to be realized in accordance with electricity demand, day-ahead electricity market prices and intermittency of photovoltaic and wind generators electricity production. A microgrid that includes non-deferrable loads, renewable energy sources, EV fleet and its charging station is analyzed in this paper. Its Vehicle-to-Grid (V2G) service is optimized with the aim of minimizing the operational costs and obtaining peak load shaving and valley filling of the load curve, which is especially effective in the case of EVs fleet with occupational time intervals known in advance.</p> <p>Optimized schedule of EVs charging and discharging is obtained as a result of the procedure that uses multi-criteria optimization function. These criteria include minimization of microgrid electricity costs as the local aggregator’s benefit, maximization of the flattening of total microgrid demand curve as main grid operator’s benefit, and minimization of battery degradation (due to a number of charging/discharging cycles) as EVs owner’s benefit which is the novelty of this paper. Experimental analysis is performed on several scenarios and program Lingo is used to solve the optimization problem.</p> Dario Javor Nebojsa Raicevic Dardan Klimenta Aleksandar Janjic Copyright (c) 2022 Dario Javor, Nebojsa Raicevic, Dardan Klimenta, Aleksandar Janjic 2022-06-28 2022-06-28 28 3 24 29 10.5755/j02.eie.31238 Title https://eejournal.ktu.lt/index.php/elt/article/view/31706 Elektronika ir Elektrotechnika Copyright (c) 2022 Elektronika ir Elektrotechnika 2022-06-28 2022-06-28 28 3 1 1 Analysis of LoRaWAN Transactions for TEG-Powered Environment-Monitoring Devices https://eejournal.ktu.lt/index.php/elt/article/view/31265 <p>Long-Range (LoRa) transmission technology is potentially a suitable solution in abundant applications such as smart cities, smart industries, smart health, and others, although it is challenging and complex to implement. LoRa is a non-cellular modulation technology for Long-Range Wide-Area Networks (LoRaWAN) and is suitable for Internet of Things (IoT) solutions through its long-range and low-power consumption characteristics. The present paper provides a comprehensive analysis of LoRa wireless transactions through several measurements, which differ in LoRa parameter configuration. The results showed dependency of the power consumed by the transaction on the selected Effective Isotropic Radiated Power (EIRP). The quantity of energy consumed by the transaction also significantly depends on the selected data rate (combination of the spread factor and bandwidth) and payload.</p> Michal Prauzek Tereza Paterova Martin Stankus Miroslav Mikus Jaromir Konecny Copyright (c) 2022 Michal Prauzek, Tereza Paterova, Martin Stankus, Miroslav Mikus, Jaromir Konecny 2022-06-28 2022-06-28 28 3 30 36 10.5755/j02.eie.31265 Evaluation of a Long-Distance IEEE 802.11ah Wireless Technology in Linux Using Docker Containers https://eejournal.ktu.lt/index.php/elt/article/view/31146 <p>Wireless technologies are essential for modern people to maintain uninterrupted connection to the Internet. The most popular standards for wireless technologies are standards of the IEEE 802.11 family. Currently, data transmission rate achievable by IEEE 802.11ac or 802.11ax standards can reach up to 10 Gbit/s. Different IEEE standards have specific data transmission rates. For example, the IEEE 802.11ah standard or Wi-Fi HaLow (code name) operates in the 900 MHz band, which is an unlicensed frequency band below 1 GHz, and is called the “Sub-1-GHz” range. In theory, this standard can provide coverage range of up to 543 meters indoors and data transfer rate of up to 347 Mbit/s (using a maximum of four spatial streams and 16 MHz channel bandwidth). The great benefit of the 802.11ah standard is low energy consumption, which enables communication between devices from the Internet of Things (IoT) over long distances without using a lot of energy. The Wi-Fi HaLow standard is being studied by the authors of the presented article in the ns-3 network simulation program with the 802.11ah module installed and implemented in Docker containers, VirtualBox Virtual Machines (VMs) with a running Linux operating system. During the simulations, results were obtained for the Docker containers simulation with a limited number of stations over different simulation times. These results have been studied in different scenarios. In the scenarios, the results of the Wi-Fi HaLow network simulation were converted into another simulation time, and thus were compared with each other.</p> Daniils Aleksandrovs-Moisejs Aleksandrs Ipatovs Elans Grabs Dmitrijs Rjazanovs Copyright (c) 2022 Daniils Aleksandrovs-Moisejs, Aleksandrs Ipatovs, Elans Grabs, Dmitrijs Rjazanovs 2022-06-28 2022-06-28 28 3 71 77 10.5755/j02.eie.31146 A Novel Metaheuristic Optimization for Throughput Maximization in Energy Harvesting Cognitive Radio Network https://eejournal.ktu.lt/index.php/elt/article/view/31245 <p>In this article, a novel technique is proposed, namely rank-based multi-objective antlion optimization (RMOALO), and applied to optimize the performance of the energy harvesting cognitive radio network (EHCRN). The original selection method in multi-objective antlion optimizer (MOALO) is suitably changed to improve the algorithm, thus reaching the optimal solution for the problem. The proposed technique shows considerable performance improvement over the method used in the multi-objective antlion optimizer (MOALO). The performance of the proposed RMOALO is demonstrated on five benchmark mathematical functions and compared to multi-objective particle swarm optimization (MOPSO), multi-objective moth flame optimization (MOMFO), MOALO-Tournament, and MOALO-Roulette. The simulation results show an improved convergence of RMOALO and find the optimal solution to the throughput maximization problem. We show that RMOALO provides 16.33 % improved average throughput with the optimal value of sensing duration for the varying amount of harvested energy compared to MOPSO, MOMFO, MOALO-Roulette, and MOALO-Tournament.</p> Shalley Bakshi Surbhi Sharma Rajesh Khanna Copyright (c) 2022 Shalley Bakshi, Surbhi Sharma, Rajesh Khanna 2022-06-28 2022-06-28 28 3 78 89 10.5755/j02.eie.31245 Evaluating Similarity of Spectrogram-like Images of DC Motor Sounds by Pearson Correlation Coefficient https://eejournal.ktu.lt/index.php/elt/article/view/31041 <p>Three main approaches on how audio signals can be used as input to a deep learning model are: extracting hand-crafted features from audio signals, mapping audio signals into appropriate images such as spectrogram-like ones, and using directly raw audio signals. Among these approaches, the usage of spectrogram-like images represents a compromise regarding the bias enforced by the processing (seen in hand-crafted features) and computational demands (seen in raw audio signals). When any of the spectrogram-like images is used as a deep learning model input, then different techniques for image processing become available and can be implemented. They include techniques for assessing the image similarity, implementing image matching, and image recognition. The topic of this paper is similarity of spectrogram-like images obtained from DC motor sounds. In that respect, relevant measures of image similarity are first reviewed, and then one of them - the Pearson correlation coefficient - is applied for evaluating the similarity within the same class and between two classes of different spectrogram-like images.</p> Dejan G. Ciric Zoran H. Peric Marko Milenkovic Nikola J. Vucic Copyright (c) 2022 Dejan G. Ciric, Zoran H. Peric, Marko Milenkovic, Nikola J. Vucic 2022-06-28 2022-06-28 28 3 37 44 10.5755/j02.eie.31041