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> Image and Texture Independent Deep Learning Noise Estimation Using Multiple Frames https://eejournal.ktu.lt/index.php/elt/article/view/30586 <p>In this study, a novel multiple frame based image and texture independent Convolutional Neural Network (CNN) noise estimator is introduced. Noise estimation is a crucial step for denoising algorithms, especially for ones that are called “non-blind”. The estimator works for additive Gaussian noise for varying noise levels. The noise levels studied in this work have a standard deviation equal to 5 to 25 increasing 5 by 5. Since there is no database for noisy multiple images to train and validate the network, two frames of synthetic noisy images with a variety of noise levels are created by adding Additive White Gaussian Noise (AWGN) to each clean image. The proposed method is applied on the most popular gray level images besides the color image databases such as Kodak, McMaster, BSDS500 in order to compare the results with the other works. Image databases comprise indoor and outdoor scenes that have fine details and richer texture. The estimator has an accuracy rate of 99 % for the classification and favourable results for the regression. The proposed method outperforms traditional methods in most cases. And the regression output can be used with any non-blind denoising method.</p> Hikmet Kirmizitas Nurettin Besli Copyright (c) 2022 Hikmet Kirmizitas, Nurettin Besli 2022-12-21 2022-12-21 28 6 42 47 10.5755/j02.eie.30586 A New Machine Vision Method for Target Detection and Localization of Malleable Iron Pipes: An Experimental Case https://eejournal.ktu.lt/index.php/elt/article/view/33004 <p>Malleable iron pipes are widely used in construction, manufacturing, aerospace, and many other fields. Cast malleable iron pipes need to be treated flat to meet the needs of different shapes and sizes. This process is usually completed manually, which is low efficiency and is subject to potential safety risks. To solve this problem, a machine vision method is proposed to detect and localize malleable iron pipes. Point cloud images of malleable iron pipes are obtained by the Random Sample Consensus (RANSAC) algorithm, and precise matching is completed by the Iterative Closest Point (ICP) algorithm to obtain more accurate positions, so as to realize robot grasping. The grasping experiments of malleable iron pipes with the same and different specifications were carried out using a specially designed experimental platform. The results show that malleable iron pipes can be identified effectively and that the corresponding grasping success rate is more than 85 %. The target detection and localization method can obtain the three-dimensional (3D) position of malleable iron pipes to improve grasping efficiency, which provided a certain theoretical basis and guiding significance to improve production efficiency in practice.</p> Zhongqiang Pan Dong Zhang Copyright (c) 2022 Zhongqiang Pan, Dong Zhang 2022-12-21 2022-12-21 28 6 48 55 10.5755/j02.eie.33004 Title https://eejournal.ktu.lt/index.php/elt/article/view/33026 Elektronika ir Elektrotechnika Copyright (c) 2022 Elektronika ir Elektrotechnika 2022-12-21 2022-12-21 28 6 1 1 Development of a Low-Cost Stationary Laser Scanning System for Generation of Building Information Models https://eejournal.ktu.lt/index.php/elt/article/view/31374 <p>This paper presents a method for developing a 3D point cloud map of any indoor and outdoor vicinities using an indigenously developed stationary scanning system comprising of a single low cost 2D laser scanner. The data logging of scanner and required inertial measurement units (IMUs) has been carried out using a Robot Operating System (ROS). Multiple divergent environments have been scanned and 3D point clouds have been developed, which have been found accurate when compared to the ground truth. In addition, the Building Information Model (BIM) of the surveyed vicinities have been developed using generated point clouds. Compared to available surveying solutions present in the local market, the developed system has been found accurate, faster, economical, and user-friendly to generate structural results of the surveyed vicinities in detail.</p> Syed Riaz un Nabi Jafri Muhammad Owais Ali Siddiqui Faraz Akbar Abdul Basit Sheraz Shamim Saad Ahmed Copyright (c) 2022 Syed Riaz un Nabi Jafri, Muhammad Owais Ali Siddiqui, Faraz Akbar, Abdul Basit, Sheraz Shamim, Saad Ahmed 2022-12-21 2022-12-21 28 6 12 20 10.5755/j02.eie.31374 A 0.49 mm2 CMOS Low-Power TVCO Achieving FoM of 190.36 dBc/Hz for 5G New Radio Application https://eejournal.ktu.lt/index.php/elt/article/view/30836 <p>This paper describes the implementation of low-power, low-phase-noise (PN), and robust startup tailless class-C voltage-controlled oscillator (TVCO) for 5G new radio (NR) technology. It features dual gate voltage control source biasing to generate fast startup and differential signal amplitude balancing, thus eliminating the requirement of the conventional tail current source, which introduces more parasitic capacitance that affects the oscillation frequency, phase noise, and power consumption. The TVCO is fabricated in 180 nm complementary metal-oxide semiconductor (CMOS) technology, oscillating at 2.59 GHz 5G NR carrier frequency with an output voltage swing of 1.7 V and low-phase-noise of -122 dBc/Hz at 1 MHz offset with supply voltage headroom of 0.7 V. Without additional features added, the TVCO consumes very low-power and a small die area of 0.98 mW and 0.49 mm<sup>2</sup>, respectively. The achieved figure of merit (FoM) is 190.36 dBc/Hz.</p> Pravinah Shasidharan Selvakumar Mariappan Li Yizhi Jagadheswaran Rajendran Mark Wong Copyright (c) 2022 Pravinah Shasidharan, Selvakumar Mariappan, Li Yizhi, Jagadheswaran Rajendran, Mark Wong 2022-12-21 2022-12-21 28 6 21 26 10.5755/j02.eie.30836 Editorial Board https://eejournal.ktu.lt/index.php/elt/article/view/33027 Elektronika ir Elektrotechnika Copyright (c) 2022 Elektronika ir Elektrotechnika 2022-12-21 2022-12-21 28 6 2 3 Robust Non-Linear Controller Design for DC-DC Buck Converter via Modified Back-Stepping Methodology https://eejournal.ktu.lt/index.php/elt/article/view/31487 <p>This paper introduces two improved control algorithms for DC-DC converters. The first one is called “Non-Adaptive Modified Back-Stepping Control” (M-BSC) and the second one is called “Adaptive Modified Back-Stepping Control” (AM-BSC). Both the proposed control schemes allow one to increase the robustness to load and input voltage variations and make the DC-DC converter less sensitive to disturbances concerning the control algorithms available in the literature. The control aims to keep the output voltage at the desired value despite any changes that may occur during its operation. As a case study, the proposed control techniques have been applied to a DC-DC Buck converter. To validate the theoretical results and evaluate the performance of the proposed control algorithms, numerical simulations with four different scenarios have been analyzed: nominal operating conditions, load variations, output voltage tracking, and input voltage variations. The simulation results highlight the good performance of the proposed control algorithms compared to other classical algorithms, improving both the stationary error and the response time.</p> Okba Boutebba Samia Semcheddine Fateh Krim Billel Talbi Alberto Reatti Fabio Corti Copyright (c) 2022 Okba Boutebba, Samia Semcheddine, Fateh Krim, Billel Talbi, Alberto Reatti, Fabio Corti 2022-12-21 2022-12-21 28 6 4 11 10.5755/j02.eie.31487 A Novel Approach for Polyphase Filter Bank Design Using ABC Algorithm https://eejournal.ktu.lt/index.php/elt/article/view/31234 <p>Polyphase filter banks (PFBs) are the most preferred multirate structures for subband coding in Digital Signal Processing (DSP) and communication. For PFB design, there are many important design parameters such as filter length and frequency selectivity. Also, to realize the desired frequency response in designs, stopband and passband attenuation are of considerable importance. In PFB design, researchers and practitioners frequently use iterative and meta-heuristic optimization methods. Heuristic techniques have a significant problem-solving ability in continuous and discrete solution space. Therefore, they give better results than other suggested methods, and their performance depends on the control parameters. In this study, Artificial Bee Colony (ABC) algorithm was employed for suggested design problem of PFB. In the first stage, the control parameters of the ABC algorithm were examined to improve the performance of the proposed PFB problem. In the second stage, the analysis was carried out by changing filter lengths (8-256) and filter band frequencies (0.3-0.7/0.4-0.6). All results obtained were also compared with the Particle Swarm Optimization algorithm (PSO) and the Genetic algorithm (GA). Finally, a DSP application of PFB was carried out according to best results achieved by the ABC algorithm for filter lengths and frequencies.</p> Ahmet Logoglu Serdar Kockanat Nurhan Karaboga Copyright (c) 2022 Ahmet Logoglu, Serdar Kockanat, Nurhan Karaboga 2022-12-21 2022-12-21 28 6 27 34 10.5755/j02.eie.31234 Sea State Characterization Using Experimental Synthetic Aperture Radar Raw Data in Two-Dimensions and the Modified Fractal Signature Method https://eejournal.ktu.lt/index.php/elt/article/view/31502 <p>This paper presents a novel method for the characterization of the sea state using a set of raw experimental Synthetic Aperture Radar (SAR) data in two dimensions, i.e., in the “fast time” and “slow time” directions (as explained in the text) and the so-called “Modified Fractal Signature” (MFS) method. That is, experimental SAR radar signatures in the above two dimensions (i.e., “raw data” in the time domain) were provided to our research group by the Norwegian Institute of Defence (FFI Institute), Norway, which we processed and analyzed using the MFS method in a novel way, as presented in detail in this paper. The numerical results obtained here show an easy categorization of the sea surface as “calm sea” or “turbulent sea”, thus establishing a very promising technique for the characterization of sea state in real time, as described in detail in the text.</p> Apostolos Kotopoulis Basil Massinas Georgios Pouraimis Panayiotis Frangos Copyright (c) 2022 Apostolos Kotopoulis, Basil Massinas, Georgios Pouraimis, Panayiotis Frangos 2022-12-21 2022-12-21 28 6 35 41 10.5755/j02.eie.31502