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        <title>Acta Electrotechnica et Informatica Feed</title>
        <link>https://reference-global.com/journal/AEI</link>
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        <copyright>All rights reserved 2026, Technical University of Košice</copyright>
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            <title><![CDATA[Design and Experimental Validation of Compact NFC Antenna and Embedded SiP RFID Module Based on LTCC and PCB Technologies]]></title>
            <link>https://reference-global.com/article/10.2478/aei-2026-0011</link>
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            <pubDate>Wed, 17 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This paper presents the design, fabrication, and experimental validation of compact NFC antenna structures and a miniaturized RFID module based on System-in-Package (SiP) integration. Planar spiral antennas operating at 13.56 MHz are implemented on LTCC and PCB substrates using two-layer and three-layer configurations. The influence of substrate material, geometry, and tuning strategy on inductance, resonance frequency, reflection coefficient, and Q-factor is experimentally evaluated using vector network analysis. A compact RFID module is subsequently realized using a cavity-based SiP architecture with face-down chip integration and anisotropic conductive film (ACF) interconnection. The results demonstrate that substrate selection and integration strategy significantly affect electrical performance, coupling efficiency, and miniaturization limits, providing a practical basis for the development of embedded SiP RFID/NFC modules.]]></description>
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            <title><![CDATA[Enhancing the Ampacity of a 400 KV Overhead Transmission Line Under Real Weather Conditions: A Case Study Using the CIGRE 601 Methodology]]></title>
            <link>https://reference-global.com/article/10.2478/aei-2026-0006</link>
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            <pubDate>Wed, 17 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[A steady-state dynamic line rating (DLR) assessment was conducted for a 400 kV overhead transmission line in Slovakia using the standardized CIGRE Technical Brochure 601 methodology. The study was performed on the V427 corridor equipped with aluminium conductor steel-reinforced (ACSR) 243-AL1/39-ST1A conductors to evaluate the ampacity potential under realistic environmental conditions. Hourly meteorological data for the year 2024 were obtained from the Open Meteo API and included ambient temperature, wind speed and direction, and global tilted irradiance. The ampacity model accounted for Joule heating, solar gain, convective and radiative cooling, and the current limits were derived from the thermal equilibrium condition. The results showed that the average DLR exceeded the conventional static line rating (SLR) by 69.3 %, with exceedance observed during 99.4 % of the evaluated hours. The influence of weather parameters, particularly wind speed and angle of attack, on conductor performance was also quantified. These findings confirmed the feasibility of implementing DLR to enhance grid utilization, especially under conservative operating assumptions.]]></description>
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        <item>
            <title><![CDATA[The Evolution of Web Services: History and Systematization]]></title>
            <link>https://reference-global.com/article/10.2478/aei-2026-0007</link>
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            <pubDate>Wed, 17 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The evolution of web services has fundamentally transformed the way software components interact over the Internet. They enable distributed computing, interoperability, and the development of scalable web applications. This article aims to systematically analyse the historical milestones, technical paradigms, and conceptual frameworks that have shaped web service development. The motivation for this study is to provide a comprehensive, structured overview of web service evolution, which is crucial for understanding both legacy integration and contemporary architectural choices. Findings highlight the trajectory from basic data exchange to highly efficient, scalable, and flexible services – providing insights into integration strategies, interoperability models, and service-oriented architectures. The study contributes to the scientific understanding of web service history and offers a conceptual foundation for future implementation-focused research in distributed systems and enterprise application development.]]></description>
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        <item>
            <title><![CDATA[Compressed Sensing for ECG and PPG: A Review of Recent Developments]]></title>
            <link>https://reference-global.com/article/10.2478/aei-2026-0008</link>
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            <pubDate>Wed, 17 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The increasing integration of information and communication technologies in healthcare has enabled advanced patient monitoring and personalized treatment. Efficient data management is crucial in such systems, as the high volume of biomedical signals requires optimized compression techniques. Compressed sensing has emerged as an approach within lossy compression methods, leveraging signal sparsity to achieve high compression ratios (CR) without significant loss of critical information. This paper provides a concise review of the most recent developments in CS for electrocardiogram (ECG) and photoplethysmogram (PPG) signals, summarizing key advances in standard CS methods, dictionary-based approaches, and emerging frameworks incorporating artificial intelligence. The review aims to highlight current trends and outline directions for future research in biomedical signal compression.]]></description>
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        <item>
            <title><![CDATA[Capacitor-Based Active Cell Balancing and Balancer Design for Battery Management Systems]]></title>
            <link>https://reference-global.com/article/10.2478/aei-2026-0005</link>
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            <pubDate>Wed, 17 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This paper presents the development of a capacitor-based active cell balancing system and an accompanying control module tailored for lithium-ion battery packs used in electric vehicles. The system integrates multiple balancing topologies, including switched capacitors (SC), double-tiered switched capacitors (DTSC), and modular switched capacitors (MSC), offering flexibility for experimental evaluation. A control module equipped with an LCD interface, real-time clock (RTC), and data storage capabilities facilitates efficient monitoring and operation. Simulation results in previous work demonstrate the system’s effectiveness in achieving balanced cell voltages and maintaining operational flexibility, paving the way for enhanced battery management in electric vehicle applications. By incorporating this experimental setup for the battery pack and balancer, we want to confirm the simulation results in future work.]]></description>
            <category>ARTICLE</category>
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        <item>
            <title><![CDATA[Application of an Initial Neural Network Model for Validation of Partial Discharge Classification on Stator Coils]]></title>
            <link>https://reference-global.com/article/10.2478/aei-2026-0010</link>
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            <pubDate>Wed, 17 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This paper presents the application of an initial neural network model for the validation of partial discharge classification in stator coils. The aim is to verify the model’s ability to distinguish between different types of partial discharges in a stator coil based on signal processing and their representation using scalograms. The results highlight the potential of artificial neural networks in the classification of insulation systems of electrical machines, with the proposed model serving as a foundation for further development and optimization of more accurate classification methods.]]></description>
            <category>ARTICLE</category>
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        <item>
            <title><![CDATA[Sequential Direct Speed Predictive Control Of Pmsm With Two-Step Compensation]]></title>
            <link>https://reference-global.com/article/10.2478/aei-2026-0012</link>
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            <pubDate>Wed, 17 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Recent progress in model predictive control of permanent magnet synchronous machines (PMSMs) shows that sequential direct speed predictive control (S-DSPC) offers a promising alternative of eliminating weighting factors in the cost function. This paper presents a simulation-based validation of an S-DSPC strategy with computational delay compensation, where cost functions are evaluated sequentially to simplify weight tuning and reduce complexity. A linear Kalman filter–based load torque observer provides the torque estimation required for state prediction. To study the effects of delay, a computational delay is introduced, and a compensation algorithm is proposed. The method addresses real-time predictive control delays by advancing state predictions to the k + 2 sampling step, ensuring that the applied switching state reflects actual system conditions. The approach was tested in MATLAB/Simulink under three cases: delay without compensation, delay with compensation, and an ideal delay-free case. Results show that uncompensated delay causes distortion in dq-axis currents and increases the total harmonic distortion (THD) of phase currents. With the two-step compensation (TSC) method, THD was reduced by approximately 35% compared to the uncompensated case, achieving performance close to the ideal scenario. Speed tracking and transient responses remained largely unaffected, confirming that TSC effectively improves current quality in S-DSPC.]]></description>
            <category>ARTICLE</category>
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        <item>
            <title><![CDATA[Minimal Dataset Size For Benford’s Law Application]]></title>
            <link>https://reference-global.com/article/10.2478/aei-2026-0009</link>
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            <pubDate>Wed, 17 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Benford’s law describes the effect of specific first significant digit probability distribution in natural datasets. In the case of non-natural or artificial intervention within such datasets, the first digit probability distribution tends to deviate from the theoretical distribution. Benford’s law-based methods are useful in detecting unnatural changes in datasets indicating artificial manipulation of the original data. In our article, we first shortly describe the theory behind this law with the overview of Benford’s law properties. Then we focus on conformity tests for Benford’s law as methods for data change detection com-pared with original dataset. In our research, the datasets were collected from electricity consumption metering devices. We provide the results of conformity with Benford’s law for affected datasets within a series of simulations with extremely small datasets. This size of datasets violates some of the standard conformity rules for Benford’s law.]]></description>
            <category>ARTICLE</category>
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        <item>
            <title><![CDATA[Facial Biometric Authentication Enhanced by Emotional and Demographic Contexts]]></title>
            <link>https://reference-global.com/article/10.2478/aei-2026-0003</link>
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            <pubDate>Sat, 21 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This paper presents a cloud-based facial biometric authentication system integrating demographic and emotional attributes to enhance verification robustness under compression and distortion. A prototype web platform was implemented using cloud-based services, combining facial recognition with demographic and emotional analysis. The system was experimentally evaluated under multiple image degradation scenarios, including lossy and lossless compression, blurring, and block partitioning. Results show that recognition accuracy remains above 98% under mild compression, but decreases significantly under severe distortions, with match rates dropping below 25% in extreme cases. Additional experiments demonstrate reliable gender classification (95%) and moderate performance for age and emotion estimation. The contribution of this work lies in demonstrating the practical feasibility of combining identity verification with contextual biometric features in resource-constrained environments, while also outlining the limitations and security implications of such systems.]]></description>
            <category>ARTICLE</category>
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        <item>
            <title><![CDATA[Concept of Virtual Protection]]></title>
            <link>https://reference-global.com/article/10.2478/aei-2026-0001</link>
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            <pubDate>Sat, 21 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This article presents the design and validation of a virtual protection system concept utilizing a Python-based software environment. Unlike traditional hardware-centric relays, this approach leverages software-defined logic to enhance flexibility and scalability in distribution networks. The study focuses on the development of a virtual protection architecture, including a custom-built input signal generator to simulate fault conditions without physical hardware. Specific protection functions, including ANSI-59PGQ (Overvoltage) and ANSI-27 (Undervoltage), were implemented and tested. The system demonstrated a processing latency of 5–20 ms during simulation cycles, validating the algorithmic efficiency. While the current prototype relies on soft real-time simulation, the results confirm that the proposed virtual protection logic is capable of detecting faults with reaction times comparable to commercial digital relays, paving the way for future hardware-integrated implementations.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Transmission of Digitized Invoices to Accounting Software]]></title>
            <link>https://reference-global.com/article/10.2478/aei-2026-0002</link>
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            <pubDate>Sat, 21 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The primary objective of this work was to automate the transfer of digitized invoices into an accounting software system. Traditionally, manual invoice processing is time-consuming, prone to errors, and increases operational costs, hindering timely financial reporting. The work analyzes various solutions for invoice digitization, their export methods, and the technologies utilized for system development and data transmission. The key outcome is the successful implementation of a system for transferring digitized invoices into accounting software. The system leverages a modular design with a microservices-oriented architectural approach, with C# chosen as the primary programming language. To ensure reliable asynchronous communication, AWS Simple Queue Service (SQS) was utilized, and AWS DynamoDB was used for persistent data storage. The system was designed to eliminate manual intervention in the invoice processing workflow, thereby enhancing efficiency, accuracy, and overall operational speed. This work significantly contributes to centralizing invoices in a digital format with a unified design, streamlining accounting processes, and laying the groundwork for advanced financial analytics or machine learning applications in accounting.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[A Comparative Study of Machine Learning Models on Cryptocurrency Prices]]></title>
            <link>https://reference-global.com/article/10.2478/aei-2026-0004</link>
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            <pubDate>Sat, 21 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The volatility of cryptocurrencies poses challenges for accurate price forecasting. This study compares traditional machine learning models (Linear Regression, Support Vector Regression), ensemble methods (Random Forest, Gradient Boosting), and deep learning architectures (LSTM, GRU) in predicting the daily prices of Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), Ripple (XRP), and Polkadot (DOT). Using historical data from CoinGecko, a sliding window, and normalisation, we assess models by Root Mean Square Error (RMSE) and relative percentage error. Results show that LSTM and GRU achieve the best overall accuracy, while Linear Regression remains competitive for stable assets such as BTC, ADA, and DOT. Ensemble methods performed moderately, whereas SVR consistently underperformed. The findings underline the importance of matching prediction models to the characteristics of specific cryptocurrencies.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Hybrid FSO/RF Networks with Neural Prediction of RSSI and Weather]]></title>
            <link>https://reference-global.com/article/10.2478/aei-2025-0015</link>
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            <pubDate>Wed, 25 Feb 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This paper investigates neural network models for predicting weather parameters and received signal strength indicator (RSSI) to enable adaptive handover in hybrid free space optics (FSO)/radio frequency (RF) systems. The most correlated parameters were visibility, temperature (measured from three independent sensors), and particle concentration. The work was exclusively focused on predicting correlated atmospheric effects and subsequently predicting RSSI parameter derived from them using a long short-term memory (LSTM) model, minute by minute over 24 hours. The predicted values can in the future serve as input for initiating handover decisions – for example, through visualization in Simulink. The threshold value of -30 dBm was taken from peer-reviewed articles and is used as a reference limit for link quality.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Improving Kanice Mini: A Study on Hyperparameter Tuning and Optimization]]></title>
            <link>https://reference-global.com/article/10.2478/aei-2025-0014</link>
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            <pubDate>Wed, 25 Feb 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This paper investigates and extends the capabilities of KANICE Mini, a hybrid neural architecture that integrates the Kolmogorov – Arnold Network framework with Interactive Convolution Elements. While the original implementation achieved 99.35% accuracy on the MNIST dataset, we improve the model’s performance through a refined training pipeline, enhanced regularization techniques, and structured hyperparameter optimization. Our optimized KANICE Mini achieves 99.56% accuracy on MNIST, surpassing the original result. Furthermore, we evaluate its generalization capability on more complex real-world data by applying it to the Invasive Ductal Carcinoma classification task, where it reaches 85.79% accuracy. These results demonstrate that, with careful tuning, KANICE Mini can rival significantly larger architectures in performancewhile preserving advantages in efficiency, modularity, and interpretability.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Fuzzy Systems in Motor Control Solutions: A Comparative Analysis of Fuzzy Logic Vs. PI Regulators in Dynamic Load Scenarios]]></title>
            <link>https://reference-global.com/article/10.2478/aei-2025-0016</link>
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            <pubDate>Wed, 25 Feb 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This paper presents a comprehensive comparative analysis of fuzzy logic and conventional PI controllers in motor control applications, focusing on dynamic load scenarios. We investigate the integration of fuzzy logic controllers within field-oriented control (FOC) architectures, particularly examining their performance in speed regulation, oscillation reduction, and transient response. Through systematic experimentation with a sensorless PMSM drive system, we demonstrate how fuzzy controllers can outperform traditional PI regulators in handling nonlinearities and varying operating conditions. The study includes detailed analysis of control surface optimization, revealing how strategic modifications to membership functions can significantly reduce speed oscillations while maintaining responsiveness. Our results show that the implemented fuzzy speed controller achieves 28.57% faster settling time compared to its PI counterpart, albeit with a 2% overshoot trade-off. Furthermore, we identify key subsystems within FOC architectures where fuzzy logic implementation offers the most substantial benefits, including open-loop start-up sequences and flux control. The paper concludes with practical insights into computational requirements for embedded implementations and discusses future directions for intelligent control systems in motor drive applications.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Deep Learning with Optical Flow for Automated Lung Sliding Detection]]></title>
            <link>https://reference-global.com/article/10.2478/aei-2025-0013</link>
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            <pubDate>Wed, 25 Feb 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This study explores the application of optical flow techniques for the automated classification of lung sliding in lung ultrasound—a key indicator for assessing lung function and diagnosing pneumothorax. Optical flow is used to capture subtle pleural motion between frames. We evaluate both traditional machine learning models (Random Forest, Gradient Boosting) and deep learning architectures (CNN, ResNet-18), using various preprocessing methods to optimize flow representation. Among deep models, the CNN achieved the highest overall accuracy (80.8%) and F1-score (73.7%), while the more complex ResNet-18 reached a high recall (94.5%) but suffered from lower precision (62.6%), indicating a tendency to over-predict the positive class. These findings highlight the trade-offs between model complexity and generalization in limited-data scenarios. The results underline the importance of model selection and tailored preprocessing in improving diagnostic reliability.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Shielding Properties and Radiation Modification of Magnetic Nanoparticle-Functionalized Textiles]]></title>
            <link>https://reference-global.com/article/10.2478/aei-2025-0009</link>
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            <pubDate>Mon, 08 Sep 2025 00:00:00 GMT</pubDate>
            <description><![CDATA[This study contributes to the development of multifunctional shielding textiles effective against both electromagnetic interference (EMI) and ionizing radiation. Wool-blend fabrics were modified with Fe₃O₄ magnetic nanoparticles to create flexible composites with strong frequency-dependent absorption and reflection of electromagnetic fields, along with protection against alpha and beta particles. Magnetic susceptibility analysis revealed that, in some cases, radiation exposure unexpectedly enhanced the material’s magnetic properties, suggesting beneficial structural changes in the nanoparticles. These findings open new possibilities for adaptive “smart” textiles that not only withstand radiation but may improve under it—paving the way for use in wearable electronics, aerospace, and next-generation protective clothing.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Post-Quantum Encryption Algorithms]]></title>
            <link>https://reference-global.com/article/10.2478/aei-2025-0011</link>
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            <pubDate>Mon, 08 Sep 2025 00:00:00 GMT</pubDate>
            <description><![CDATA[The most used asymmetric encryption algorithm nowadays is RSA. It may become insecure regarding advances in the field of quantum computers. It is the reason why the National Institute of Standards and Technologies introduces the challenges of choosing a new post-quantum encryption standard. Initially, NIST received 82 submissions comprising key encapsulation mechanisms and digital signature schemes. However, only 69 of them were formally accepted after an initial review. In July 2022, NIST selected some algorithms for standardization. For the key encapsulation mechanism, Kyber was selected, and for digital signatures, Dilithium Falcon and SPHINCS+. After the third round concluded, NIST indicated it would continue to evaluate some of the alternative algorithms that were not selected as finalists in the third round. This ongoing evaluation is informally referred to as a "fourth round." Initially, there were four participants - BIKE, Classic McEliece, SIKE, and HQC. However, the SIKE downfall with the Castryck-Decru attack was introduced in July 2022, and the HQC algorithm was chosen for standardization in March 2025. In our research, we examine all functions of BIKE, Classic McEliece, and the HQC from the point of view of time and memory consumption. The results obtained will help us during the implementation of the BIKE algorithm on ESP32.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Real-Time Dynamic Gesture Recognition: A Novel Approach for Efficient Human-Computer Interaction]]></title>
            <link>https://reference-global.com/article/10.2478/aei-2025-0012</link>
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            <pubDate>Mon, 08 Sep 2025 00:00:00 GMT</pubDate>
            <description><![CDATA[Dynamic gesture recognition systems face persistent challenges in achieving real-time performance and high recognition efficiency. This paper presents a novel framework integrating computer vision techniques with machine learning algorithms to address these issues. The approach utilizes OpenCV for dynamic gesture detection, employing gesture contour extraction using skin color feature segmentation, fingertip detection for static gesture recognition, and dynamic gesture tracking through Hidden Markov Models (HMM). Experimental results demonstrate high recognition accuracy of 95.8 ± 1.4% across various gesture types, with individual rates ranging from 95.2 ± 1.6% to 98.5 ± 0.8%. Real-time performance is achieved with average processing time of 82.71 ± 3.2 ms per frame (12.1 FPS). Our method demonstrates 1.15× to 1.49× speed improvements over state-of-the-art approaches while maintaining superior accuracy. Validation on the DHG-14/28 public benchmark confirms generalizability with 93.4 ± 1.6% accuracy. The framework shows robust performance under challenging conditions: low-light environments (92.4 ± 2.1%), fast motion (91.8 ± 1.9%), and complex backgrounds (93.2 ± 1.7%). Statistical significance was confirmed through comprehensive evaluation across diverse demographic groups. This research has implications for human-computer interaction applications, including smart home systems, augmented reality, and industrial automation.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Extending the Containers of the Data Processing Platform on the Edge of the Network]]></title>
            <link>https://reference-global.com/article/10.2478/aei-2025-0010</link>
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            <pubDate>Mon, 08 Sep 2025 00:00:00 GMT</pubDate>
            <description><![CDATA[This article describes the expansion of an existing data processing platform. This work aimed to analyze the current state of the platform, identify possible shortcomings, and propose new containers. These containers were later implemented and connected with the rest of the platform. The extensions described in this article provide new ways to load data from different databases or video sources, new processing tasks, and an email notification system utilizing Gmail. The results of this work provide a great stepping stone in creating the fully featured platform, which we plan on creating. It also serves as a confirmation of the ease of implementing new containers into our framework.]]></description>
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