ELEKTRONIKA IR ELEKTROTECHNIKA (ISSN 1392-1215) is a peer-reviewed open access bimonthly research journal of Kaunas University of Technology. The research journal ELEKTRONIKA IR ELEKTROTECHNIKA publishes original research papers on featuring practical developments that might have a significant impact in the field of electronics and electrical engineering, and focuses on automation, robotics & control; automotive electronics; electric vehicles; electrical engineering; electronic measurements; electronics; high frequency technologies, microwaves; micro & 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. WoS: Impact Factor 1.3 (2022); Scopus: SCImago Journal Rank 0.32 (2022)
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 Creative Commons Attribution 4.0 (CC BY 4.0) agreement under which the paper in the Journal is licensed. 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.
A Deep Learning-based Method for Production Carton Packaging Defect Detection
In the automation production packing system, appearance defects on the carton packaging films occur frequently. To solve this problem, a deep learning-based method is proposed in this work to identify film defects during the packaging process to ensure the production packaging quality. This method adopts the deformable convolution network (DCN) v2-C3 module to replace the C3 module in the Neck part of the You Only Look Once version 5 (YOLOv5s) network to extract the deep feature information of the defects in the production carton packaging, with the purpose to improve the spatial transformation ability of the detection model and the model generalization ability to different shapes of targets. Field data are used to evaluate the proposed method. The analysis results indicate that the recognition rate of the proposed method is 99.3 % for different carton packaging defects; and compared to the original YOLOv5s method, the detection accuracy of the proposed method increases by 2.7 % and the scrap rate is reduced by 1.3 %. As a result, the proposed method can meet the requirements for defect detection in the production carton packaging in practical applications.
Copyright (c) 2026 Zhimin Lu, Xueliang Yang; Zhibin Zhang; Jingqiang Jiang, Wencan Li, Weiwei Zhang, Mengling Huang
Copyright (c) 2026 Elektronika ir Elektrotechnika
Improving Control Stability for a Robot Arm Packaging System Using Fuzzy Optimization
This work addresses the issue of insufficient control stability in the robotic arm feeding system on the packaging production line by proposing an adaptive algorithm based on the fuzzy-optimized proportional-integral-derivative (PID) controller. By establishing the Denavit-Hartenberg (D-H) kinematic model of the robotic arm and combining the MATLAB Robot Toolbox with the SIMULINK simulation platform, a fuzzy controller was designed with amplitude deviation and deviation rate as inputs to dynamically adjust the PID parameters. Hardware integration was achieved based on the Mitsubishi FX2N series programmable logic controller (PLC). Simulation results show that the adjustment time of the fuzzy PID control is 0.45 s, which is reduced by 25 % compared to the traditional PID (i.e., 0.6 s), with the overshoot reduced from 6 % to 0 % and the steady-state error stabilized at 0 mm. Through 10 sets of actual picking experiments, it was verified that the average time error under fuzzy PID control is 0.37 s with a standard deviation of 0.23 s, which is a reduction of 55.4 % and 85.6 % compared to traditional PID and direct start-up, respectively, which significantly improves the stability and efficiency of the robotic arm feeding system. This study proves that fuzzy PID, through dynamic parameter optimization and disturbance compensation, can effectively address the control response delay and error accumulation of the robotic arm under non-ideal operating conditions, providing a high-precision and high-robustness solution for industrial packaging automation.
Copyright (c) 2026 Zhimin Lu, Shouquan Gao, Wei Wu, Hongli Deng, Wencan Li, Weiwei Zhang, Mengling Huang
Signal Processing and Fault Diagnosis on Structure Vibration Measurement Using a New Composed Deep Learning Model
To protect offshore jacket platforms, it is essential to carry out the structure fault diagnosis. This paper proposes a new approach to identify structural faults in offshore jacket platforms, which is based on the integration of structural vibration data, a sophisticated data fusion process, and an intelligent diagnosis algorithm. In this new approach, firstly, the TCN is adopted to solve the efficiency bottleneck of the traditional recurrent neural networks in long time series signals, and significantly enhances the ability to capture long-distance dependent features in the structural response signals through the introduction of a dilated convolutional structure. Secondly, the bidirectional gated recurrent unit (BiGRU) network fuses forward and reverse gated recurrent units, which can effectively capture the forward and backward correlated timing features in the structure vibration data. The attention mechanism then further weights and optimises the BiGRU timing outputs so that the model can automatically focus on the signal pattern that is most discriminative for the fault identification. Furthermore, the artificial lemming algorithm (ALA) is used to optimise the hyperparameters of the TCN and BiGRU to improve the model efficiency and avoid the local optimum during the model training, thereby enhancing the generalisation performance of the proposed model. The validity of the proposed ALA-TCN- BiGRU model is substantiated through simulation and experimental validation. The results indicate that the proposed model can achieve an overall detection accuracy of over 98 % for the jacket structure, which is superior to several popular diagnosis methods.
Copyright (c) 2025 Ruixiang Zhang, Yuanchao Qiu, Yuelei Zhang, Taibing Nie
Electromagnetic Signatures for Drone Detection and Recognition
Modern unmanned aerial vehicle (UAV) platforms predominantly rely on electric propulsion for controlled flight. Most current UAV propulsion systems employ brushless DC (BLDC) thrust motors. This paper demonstrates the passive detection of UAV electric propulsion systems using a dynamically changing alternating electromagnetic (EM) field created by BLDC thrust motors and power lines in the very low frequency and low frequency range. A high-performance low noise amplifier and a commercial Airspy HF+ SDR paired with a compact ferrite-core magnetic antenna, are used to capture near-field magnetic emissions from four different drones. The tested platforms include the mass-produced DJI Mini 2, DJI Mavic 2 Enterprise, DJI Air 3S, and the Eachine Tyro 109. The measured EM spectrum consistently exhibits motor phase commutation components and electronic speed controller PWM components with propulsion load-dependent sidebands, allowing extraction of time-, frequency-, and amplitude-dependent propulsion-related BLDC motor EM signatures for UAV detection and recognition. The results of the experiment show the capabilities of drone swarm detection and recognition of each flight mode of the propulsion system.
Thrust-motor electromagnetic (EM) signature
Copyright (c) 2025 Tomas Jačionis, Šarūnas Mikučionis, Vytautas Urbanavičius
Multi-Input Isolated Power Converter for Electric Vehicles with Hybrid Energy Storage
The existing converter relies on a non-isolated transformer and lacks bidirectional support in the case of multiple and regenerative inputs. The proposed work focuses on an isolated multiport bidirectional DC-DC converter topology featuring a battery and a supercapacitor for an electric vehicle (EV) of 2 kW. The proposed topology can provide uninterrupted power to the EV’s electric motor and recover braking energy, allowing bidirectional power flow. As a result, the battery discharge time can increase due to the additional source, such as a supercapacitor and regenerative action. The system integrates two single H-bridge converters at the input and a single H-bridge converter at the output, with an existing switch. The converter enables the reduction of the rated voltages of the battery and supercapacitor packs. Additionally, a power-flow management scheme and its corresponding control scheme are proposed. The performance test has been conducted on traditional and PI control methods under different operating conditions with a switching frequency of 50 kHz, such as switching power from the battery or supercapacitor to the load during driving and from the load back to the source during energy generation. Power flow capabilities and efficiency values validate the viability and effectiveness of the proposed system of 2 kW.
Copyright (c) 2026 Kandaswamy K V, Shyam D, Sivamani D
A Reliable Phasor Measurement Framework Using Artificial Intelligence to Prevent False Data Manipulation
This work introduces a novel intelligent security approach to improve the reliability of smart grid operations by protecting measurement data produced by the Phasor Measuring Unit. The increasing demand for power distribution requires continuous and accurate monitoring, which is dependent on reliable measurement data synchronized with strict timing constraints. To address the risks related to inaccurate measurements, unauthorized modification, and malicious disruption, this study develops a self-adaptive security analysis process using an artificial intelligence-based learning strategy. The system evaluates multiple operational factors and converts them into dynamic multivariate states to support accurate decision making. The proposed method integrates a learning mechanism based on nearest neighbor classification to detect abnormal measurement behavior and prevent false data events before operational failure occurs. An experimental evaluation in four scenarios demonstrates that the approach improves security and reliability metrics compared to existing solutions. The results confirm that the proposed method provides an effective pathway for reliable measurement data and resilient smart grid operation.
Copyright (c) 2026 Hariprasath Manoharan, Sasi Kumar K.
Recent Developments in Inverter Topologies for Direct Torque Controlled Induction Motors
Direct Torque Control, an industrial standard for applications requiring high dynamic performance in induction motors, has reached its performance limits due to the high torque ripple and acoustic noise generated by traditional two-level inverters. This comprehensive review study examines the technological evolution of power converter topologies developed to extend the performance limits of DTC, from classic multi-level structures such as Neutral Point Clamping and Cascaded H-Bridge to the recently prominent Reduced Switching Count and asymmetric hybrid topologies. The study also details the integration of Matrix Converters, which increase power density, and Open-Ended Winding (OEW) architectures, which enable voltage optimization, with DTC. The article compares existing topologies in terms of cost, efficiency, and harmonic performance, and provides critical projections on how SiC/GaN-based wide bandgap semiconductors and fault-tolerant designs will transform future electric drive systems, thereby offering a strategic roadmap for researchers and design engineers.
Copyright (c) 2025 Fatih Korkmaz, Mustafa Karhan
Four Times Extended Reed Solomon Codes, their Decoding and Application in EII Codes for Data Repair
Data recovery in distributed and general storage systems requires a broad range of regenerating codes with different properties such as locality, availability, scalability, etc. The main goal of this paper is to enrich this palette with four times extended Reed Solomon (RS) codes. Recently, it was shown that RS codes can be extended five times, when constructed over finite fields where and is an odd integer. These codes are almost maximum distance separable (AMDS), and they are reaching upper bounds on code distance in known tables on the best linear block codes. However, such codes have limitations for practical applications since has to be an odd integer. To overcome this limitation, four times extended RS codes are presented in this paper. These AMDS codes can be constructed over finite field where the integer is arbitrary. They can be used in numerous different constructions of product codes, Extended Product codes, Integrated Interleaved codes, Extended Integrated Interleaved codes, Staircase codes, and others, which are suitable for data recovery in storage systems.
Copyright (c) 2025 Peter Farkas, Martin Rakús, Katarína Farkasova, Frederik Pavelka, Ladislav Divinec
Tomato Ripeness Detection and Harvesting Decision Making in a Greenhouse Using BIIE-YOLOv10n
Accurate detection of tomato ripeness is crucial to improving harvesting efficiency and supporting precise picking decisions. However, occlusion and overlap of fruits, along with complex background interference, can lead to loss of local information, unstable color distribution, and increased difficulty in ripeness discrimination, thereby undermining the stability and reliability of detection. To address these challenges, this study proposes a real-time tomato ripeness detection model based on an improved YOLOv10n framework, named BIIE-YOLOv10n. The model employs an improved bidirectional feature pyramid network (IBiFPN) to achieve adaptive multi-scale feature fusion and enhanced contextual information exchange, integrates an improved iterative channel-spatial attentional feature fusion (ICSAFF) mechanism into the C2f module for effective global-local feature aggregation, and introduces the Inner-EIoU loss function to balance positive and negative samples, thereby improving bounding box regression accuracy under complex environments. Experimental results on a self-constructed tomato ripeness dataset show that the proposed model achieves an accuracy of 82.6 %, a recall of 80.5 %, an F1-score of 82.0 %, and an mAP50 of 85.4 %, representing improvements of 1.3 %, 5.2 %, 4.0 %, and 5.0 % over the baseline model, respectively. Based on the detection results, a visual-driven strategy is developed for the assessment of cluster-level ripeness and picking decision-making, providing support for automated harvesting systems in greenhouse environments. In summary, BIIE-YOLOv10n significantly enhances tomato ripeness detection performance and provides reliable decision-making support for automated harvesting and intelligent grading in greenhouse settings.
Copyright (c) 2025 Fanjia Meng, Ming Lu, Lihong Huang, Xin Wang
Machine Learning Driven Design and Optimisation of a Dual-Band SRR Metamaterial Antenna for Emerging IoT Platforms
This research introduces a machine learning (ML) technique derived from the dual-band split-ring resonator (SRR) metamaterial antenna development and optimisation. The patch antenna with an SRR design is intended for use in 5G and 6G IoT applications. The method utilizes data driven prediction models to speed up design and optimisation instead of a normal trial and error process. The antenna responses were anticipated using different regression methods, including artificial neural network (ANN), K nearest neighbour (KNN), gradient boosting (GB), and eXtreme gradient boosting (XGBoost). When it comes to the techniques utilized, the eXtreme gradient boosting model was the one that reached the highest accuracy of 87.4 % in estimating return loss and thus it was the one that stood out the most. To support the claim of the effectiveness of the method, a dual-band split-ring resonator loaded microstrip antenna was designed and produced to operate in the ranges of 2.3 GHz to 2.55 GHz and 3.95 GHz to 4.15 GHz, respectively. The close agreement between predictions, simulations, and measurements further confirms the reliability of the machine learning-based design strategy. The proposed approach not only enables fast prototyping of compact antennas suitable for IoT devices, but also for industrial automation, smart home, and smart city applications.
Copyright (c) 2026 Hemalatha Balakrishnan, Chitra Sivathanu, William Johnson, Susan Christina Xavier
Hybrid Dual-Loop Control of PV DC-DC Boost Converters Using Type-2 Fuzzy Logic and Model Predictive Control
This paper presents a hybrid dual-loop control strategy for a standalone photovoltaic energy system interfaced with a boost converter. Photovoltaic systems exhibit strong non-linear behavior and high sensitivity to irradiance, temperature, and load variations, which makes voltage regulation and dynamic performance challenging, in especially with a single-loop control configuration. To address these issues, a cascaded control architecture is proposed, where an outer voltage regulation loop based on Type-2 fuzzy logic generates a robust reference current, while an inner loop based on model predictive control ensures fast and accurate inductor current tracking. A comparative study with a Type-1 fuzzy logic-based predictive control strategy is conducted using dynamic performance indicators. Simulation results show that both strategies achieve overshoot free voltage regulation, while the proposed Type-2 fuzzy logic-based approach reduces the response and settling times by approximately twenty-one percent. In addition, the proposed fuzzy logic and predictive control architecture demonstrate a strong disturbance rejection capability under varying environmental and load conditions, confirming its effectiveness for high performance photovoltaic power conversion applications.
Copyright (c) 2025 Nesrine Cherigui, Abdelkarim Chemidi, Ahmed Tahour
Rotor Ground Capacitance Coupled Harmonic Fault Detection Boundary in Large Synchronous Generators
Focusing on the impact of rotor-to-ground capacitance on passive ground fault detection in large-scale synchronous generators, this paper proposes an error analysis method using an amplitude deviation factor. Based on the classical equivalent circuit model of the excitation system, the proposed method explicitly incorporates the rotor-to-ground capacitance parameters. It systematically reveals the non-linear mechanism by which ground capacitance affects the harmonic voltage amplitudes of faults on both the DC and the AC sides. This study addresses the limitations of existing research, which is primarily confined to small and medium capacity machines and often neglects the ground capacitance. Verified by computer simulations and field data from large-scale units, the results demonstrate that the proposed method effectively clarifies the detection boundaries and improves fault detection accuracy in high-capacitance scenarios.
Copyright (c) 2025 Yongbin Du, Wenhao Chao, Qian Zhang, Bingbing Zhao
Copyright (c) 2025 Elektronika ir Elektrotechnika
Prosthetic Arm Controller Based on the sEMG Signal and Deep Learning Network
Prosthetic hands help patients gain greater confidence in daily life, and enabling automatic control of prosthetic hands using real biosignals is an essential task. This paper proposes a prosthetic hand control system based on surface electromyography (sEMG) signals. In particular, the sEMG signals are collected in real time corresponding to each hand gesture and pre-processed to eliminate noise components. A deep learning network is then employed to recognize hand gestures from the pre-processed sEMG signals. Recognized gestures are subsequently used to control the prosthetic hand to perform the corresponding movements. The proposed deep learning network achieves a recognition accuracy of 98.15 % for hand gestures from sEMG signals. Furthermore, the experimental results demonstrate that the proposed system can control the prosthetic hand with an accuracy of up to 96.97 % and a variance of 3.2194 across multiple subjects. These results suggest that the proposed system holds great potential for real-time prosthetic hand control based on sEMG signals, thereby supporting patients in gaining confidence in social interactions.
Copyright (c) 2026 Dinh-Hung Le, Thanh-Nghia Nguyen
Wavelet-based Channel Modeling for OFDM-Integrated Pinching Antenna Systems
This paper studies wavelet-based channel modeling for orthogonal frequency-division multiplexing (OFDM) links with pinching antenna systems (PASS). A Daubechies-4 decomposition of the PASS channel response gives a 4.76 percent sparsity in the transform domain, used for compressed sensing-based channel estimation. The level-three approximation coefficients carry about 92 percent of the signal energy and mainly correspond to waveguide dispersion. The level-two and level-three detail coefficients are linked to pinching-antenna coupling, while the level-one detail coefficients capture free-space fading and noise. A relative change of up to 20 percent in the phase matching parameters changes the normalized mean-square error (NMSE) by less than 0.25 dB, so the method does not depend on exact phase matching. The wavelet representation reduces cyclic-prefix overhead from 10.2 percent to 6.1 percent, while orthogonal matching pursuit (OMP) based recovery gives about forty times lower complexity than minimum mean-square error (MMSE) estimation. Together with the 15 percent sparse-denoising gain, the net throughput improvement is about 20 percent. The 0.4 overlap factor satisfies the theoretical bound, and the rate gains are reported conservatively.
Cooperative Frequency Regulation for Thermal Power and Energy Storage Clusters via Hierarchical SOC Control
Distributed energy storage is crucial for frequency stability in low-inertia grids. However, conventional lumped models mask unit heterogeneity, causing premature depletion of low-state of charge (SOC) units known as the “barrel principle”. To address this, a hierarchical cooperative control strategy for thermal energy storage systems (thermal power and energy storage) prioritizing SOC consistency is proposed. A discrete-time state-space model is constructed to capture heterogeneous dynamics. The proposed two-layer architecture features an upper layer discrete filter for spectral-based power allocation and a lower layer adaptive consistency algorithm. A non-linear power-exponent weighting mechanism dynamically recalibrates output weights based on real-time SOC deviations. The simulation results demonstrate that the strategy effectively suppresses frequency fluctuations and ensures rapid SOC convergence. By preventing overcharge or overdischarge of the individual unit, the approach significantly enhances the effective capacity and operational robustness of the joint system.
An Automated Prediction of Plant Diseases Using Leaf Images Based on Efficient Deep Learning and Image Processing
In all nations' economies, the agricultural sector can play a vital role through crop production. The discovery of plant diseases could be one of the most significant factors in the preservation of an agriculturally productive country. Crop farmers lose a considerable sum annually since numerous diseases have affected their plants. Various plant parts could have been contaminated by fungi, viruses, and microorganisms, but in this work, the focus is on the discovery and categorization of banana leaf disease. Improved food production quality and reduced economic losses will be achieved with an accurate early prediction of plant leaf disease. Nowadays, deep learning (DL) and artificial intelligence (AI) have been widely applied for the construction of automated systems to discover and categorize banana leaf diseases. In this paper, an automated banana leaf disease prediction system is presented by proposing an enhanced deep learning model. In this work, captured banana leaf images can be pre-processed through combined filters, in which image smoothing and noise reduction are done by a Gaussian filter, followed by a de-noising process that is refined additionally using the Wiener filter scheme. Subsequently, a DL model convolutional neural network (CNN) can be exploited to extract pertinent features. In this paper, an attention and fuzzy logic-based recurrent neural network (AFLRNN) model is proposed and trained with extracted features for efficient prediction and categorization of plant leaf disease. These systems utilize image processing and computer vision techniques to analyze images of plant leaves and identify potential diseases. The proposed DL system is able to efficiently detect leaf disease in plants at an early stage. The presented scheme can learn and extract features from captured plant images to facilitate precise leaf disease discovery and categorization. This kind of early detection of leaf disease is used to provide suitable treatment and reduce crop waste. The experimental results have exposed that the proposed system has attained a above 98 % higher disease prediction accuracy that has been higher to state-of-the-art schemes.
Copyright (c) 2026 Gayathri Palanisamy, Vadivel Ponnusamy
A Portable Sensor Device for Real-Time Viscosity Measurement of Lubricating Oil
The viscosity of lubricating oil is a critical indicator for assessing both its performance and degradation, and it plays a vital role in the monitoring of equipment condition. To meet the need for portable and automated viscosity measurement of lubricant oil in engineering applications, this paper presents a novel device based on Newton’s law of viscosity and strain-based sensing principles. The device detects the minute deflection of a cantilever beam under fluid impact and converts the induced strain into an electrical signal via bonded strain gauges to measure viscosity. Then, a finite element simulation was performed to optimise the structure of the cantilever beam and determine the placement of the strain gauges. Experimental validation using glycerol-water mixtures and 4050 lubricating oil demonstrated that the device exhibited a strong linear correlation between its output and reference viscosity values, and that it maintained high measurement accuracy and strong applicability under critical temperature conditions, such as the ISO standard temperature of 40 °C, as well as in high-viscosity regimes. Finally, the comparison between the proposed device and the representative commercial products indicated that the device is well suited for on-site industrial viscosity measurement requiring portability, automation, and cost-effectiveness. This work provides a technical reference and practical foundation for viscosity measurement in engineering applications.
Copyright (c) 2026 Chengze Liu, Chenxing Sheng, Xiang Rao, Bo Zhao, Zhijun Chen
Automatic Modulation Recognition Algorithm Based on Multiscale Feature Extraction and Improved Encoder Structure
Automatic modulation recognition (AMR) technology is crucial in modern communication systems. To enhance the recognition performance of wireless communication systems for modulation signals, a method based on multiscale feature extraction and an improved encoder structure is proposed. First, in this proposed method, in the data preprocessing stage, phase transformation (PT) is used to eliminate the influence of phase offset. Second, the multiscale feature extraction (MSFE) module is utilised to capture both local details and global structural features of the signal, thereby reducing the information loss rate. Third, the autoencoder (AE) module preserves temporal information, reconstructs features, and suppresses noise, significantly improving the feature reconstruction and recognition capabilities of modulation signals while minimising reconstruction loss. Simulation results have demonstrated that the proposed algorithm can achieve an average recognition accuracy of 92.5 % in the RML2016.10A, which has an improvement of about 2 % to 11 % in comparison with other mainstream algorithms in average recognition accuracy, can obtain peak recognition accuracies of 93.5 % and 93.9 % in the RML2016.10A and RML2016.10B datasets, respectively, within a signal-to-noise ratio (SNR) range of 0 dB to 18 dB, thus demonstrating its excellent recognition accuracy and robustness.
Copyright (c) 2026 Tianfei Zhang, Yecai Guo, Jiao Ding, Haiyan Long, Lei Zhang