International Journal of Electronics and Telecommunication
This journal covers an extensive scope ranging from mathematical foundations to practical engineering design in the areas of electronics and telecommunications.
Electronics and Telecommunication Committee
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The Internet of Things as a Semantic Infrastructure – Integration of Sensor Data with Distributed Knowledge Systems in Edge – Cloud Environments
The rapid expansion of the Internet of Things (IoT) leads to the generation of large volumes of heterogeneous and context-dependent data, creating significant challenges in data integration, interpretation, and processing. This paper proposes a conceptual model of IoT as a semantic infrastructure that integrates sensor data with distributed knowledge systems within an edge–cloud computing environment. The approach combines semantic technologies, including RDF, OWL, and SSN/SOSA ontologies, with a multi-layer data processing architecture. The model is formalized as a sequence of transformation functions enabling structured data flow from acquisition to analysis. Edge-level preprocessing reduces latency and data volume, while cloud-based processing supports scalable analytics and semantic reasoning. The proposed solution improves interoperability, enhances scalability, and enables efficient near real-time processing. Furthermore, the integration of artificial intelligence methods supports anomaly detection, data completion, and predictive analysis. The presented model provides a unified and adaptable framework for the development of intelligent, data-driven IoT systems.
Copyright (c) 2026 International Journal of Electronics and Telecommunication https://creativecommons.org/licenses/by-nc/4.0
IoT-Based Fuzzy Logic System for Real-Time Flooded Road Detection and Decision Support
Flooding remains a critical challenge in urban environments, often disrupting transportation systems and endangering drivers who must decide whether to cross inundated roads without precise knowledge of water depth. Existing IoT-based flood monitoring systems primarily emphasize data acquisition and remote alerts but rarely support localized, real-time decision-making for road safety. This study proposes an IoT-based flood detection and decision-support system that integrates Sugeno-type fuzzy logic inference directly into distributed edge nodes to evaluate flooded road conditions in real time. The system employs multiple JSN-SR04T ultrasonic sensors and WeMos D1 microcontrollers interconnected via a wireless network to measure water levels across multiple road segments. Each node autonomously processes sensor inputs to classify road conditions into three categories—Safe, Caution, and Not Safe—and displays results locally via OLED SSD1306 and RYG (Red-Yellow-Green) LED indicators. Experimental results demonstrate that the proposed system achieves an average sensor error of 2.2 cm compared to calibrated measurements and maintains wireless communication stability up to 25 meters with an average system response time of 1.19 seconds. Furthermore, the fuzzy inference outputs from the embedded system closely matched MATLAB-based simulations, validating computational consistency and inference reliability. The integration of edge-level fuzzy decision-making within distributed IoT nodes represents a key innovation, enabling autonomous flood assessment with minimal latency. This study contributes a low-cost, scalable, and intelligent framework for enhancing road safety and smart city flood resilience, particularly in developing urban regions prone to recurrent inundation.
Neural networks for efficient touch detection on capacitive panels
The advancement of capacitive-based touch panel technologies has opened new opportunities for their incorporation into embedded devices. However, this progress also underscores the need for improved software algorithms to achieve high precision in calculating touch coordinates. Traditional position calculation methods often exhibit diminished accuracy when applied to smaller panels, and modifying and tuning these methods can be time-consuming and labor-intensive. To address these limitations, this study investigates the performance of two neural network architectures, specifically a two-layer fully connected neural network and a radial basis function network, in enhancing the accuracy of touch coordinate calculation. A key advantage of these models is their ability to learn efficiently from limited datasets while minimizing the risk of overfitting. The high touch position accuracy achieved by the proposed neural network solutions makes them suitable for deployment in devices with limited computing resources, such as microcontrollers. Furthermore, the simplicity of the proposed models enables their implementation in embedded systems with low power consumption, offering a practical and scalable solution for a wide range of applications. Overall, the integration of these neural network models in touch coordinate processing provides notable benefits in terms of accuracy, efficiency, and adaptability.
A Comparative Study of Modulation Schemes and Mitigation Techniques for Gamma-Gamma FSO Channels
FSO communication systems are proven to be an effective substitute to RF technologies as FSO communication technologies boast high data rates which are not subjected to licensing and are not affected/spoiled by electromagnetic interference. However, despite these positive aspects, FSO links suffer from air turbulence which has strong detrimental effects on links and cause irradiance fluctuations (scintillation) and extreme fading. This has caused this paper to offer an in-depth performance analysis of FSO systems operating on a Gamma-Gamma turbulence channel and further explores the performance of different turbulence mitigation schemes and modulation strategies. This analysis uses OOK as a basis of comparison to other modulation schemes like 4 PPM, DPSK, and DCO-OFDM and explores the possible improvements Adaptive Optics (AO) and Dynamic Beam Steering (DBS) systems may provide to link reliability. This paper investigates the performance of DBS under three different settings: best path selection, path steering with a realistic motion model, and path steering with an alignment error. The results of many Monte Carlo simulations indicate that when it comes to turbulence, DPSK will be the best option because it adapts to changes. This paper suggests that DCO-OFDM is not optimal because it is the most adversely affected by channel fading. The best way. This works better than Adaptive Optics, especially in turbulent conditions. These results indicate that combining strong modulation techniques like DPSK with spatial diversity methods like DBS is essential for constructing dependable, high-speed free-space optical networks, even in challenging atmospheric conditions.
A LoRa Net Connect: An Adaptive Framework for Optimized LoRa Communication in IoT Networks
This paper presents Lora Net Connect, an innovative framework that significantly enhances LoRa communication through adaptive parameter optimization and intelligent data compression. The proliferation of IoT devices necessitates effi cient long-range communication solutions, yet traditional LoRa implementations often suffer from suboptimal performance due to fixed transmission parameters and inefficient data handling. Our framework introduces a novel two-way communication protocol with real-time feedback, content-aware compression algorithms, and dynamic parameter adaptation based on en vironmental conditions. The system achieves an average 42% improvement in data throughput and 35% reduction in energy consumption compared to conventional LoRa implementations. A comprehensive web-based monitoring interface provides real time analytics and control capabilities, making the solution both powerful and accessible. Experimental results demonstrate the framework’s effectiveness across various deployment scenarios, establishing it as a robust solution for next-generation IoT networks.
Deep Reinforcement Learning-Enabled Energy-Efficient Routing Protocol for Underwater Wireless Sensor Networks
Underwater wireless sensor networks are widely used in sea and ocean exploration, monitoring of the environment, defense surveillance. These applications are restricted by limited energy availability, propagation delay of acoustic signal, and topology changes. To address these issues, a reinforcement learning (RL)-based routing protocol that combines energy-aware clustering with Q-learning to improve packet forwarding efficiency is proposed in this paper. In this approach, the role of each autonomous agent is performed by sensor node and forwarding actions based on residual energy, hop count, and distance to the sink are adaptively selected. MATLAB simulation results demonstrate that the proposed scheme achieves a packet delivery ratio (PDR) of 95.2%. Compared with vector-based forwarding (VBF) and reinforcement learning-based opportunistic routing (RLOR), the achieved PDR is 7.6% and 3.7% higher, respectively. The performance improvement is mainly attributed to adaptive Q-learning-based next-hop selection and energy-aware clustering, which reduce redundant and long-distance transmissions and avoid routing voids. Moreover, the proposed protocol extends network lifetime to 5000 iterations, achieving improvements of 19% and 6.4%, while reducing average energy consumption by 25.7% and 13.3% compared with VBF and RLOR.
Hybrid Optical–Acoustic Underwater Communication with Joint Modulation and Bio-Inspired Power Optimization
This paper presents the design and performance evaluation of a hybrid optical–acoustic underwater wireless communication framework that integrates short-range optical links and long-range acoustic links through cooperative transmission for high bandwidth underwater applications. The system utilizes optical intra-cluster and acoustic inter-cluster links, with cooperative communication to improve data transmission and reliability. Analytical models incorporating acoustic and optical signal attenuation and noise are developed, considering practical underwater conditions. Optical links utilize Quadrature Amplitude Modulation with Turbo coding, while acoustic links adopt Frequency Shift Keying with Reed–Solomon coding to ensure reliable data transmission under heterogeneous channel conditions. Transmission power allocation is optimized using the Electric Eel Foraging Optimization algorithm to improve energy efficiency. Simulations demonstrate that the proposed framework supports reliable and high-throughput transmission under different underwater propagation conditions. Furthermore, the Electric Eel Foraging Optimization algorithm reduces transmission power consumption while maintaining stable throughput and received traffic load.
Some experience with upgrading Wi-Fi modules in PC laptops
This article investigates the feasibility of upgrading Wi-Fi modules in older PC laptops to comply with modern IEEE 802.11 standards, aiming to enhance network performance. Experimental results show that modern Wi-Fi adapters significantly improve throughput and efficiency, particularly in the 5 GHz band. However, challenges such as driver availability, BIOS whitelists, and hardware compatibility must be taken into account. The findings suggest that Wi-Fi upgrades can extend the usability of older laptops cost-effectively, with future research planned to explore newer standards and configurations.
A PPO-Based Deep Reinforcement Learning Framework for Dynamic Resource Allocation and Network Slicing in 5G Mobile Networks
Abstract-This study proposes a new intelligent framework to cope with the challenges involved with dynamic resource allocation in the 5G network environment based on Proximal Policy Optimization (PPO), which is one of the most successful Deep Reinforcement Learning (DRL) techniques. We have reformulated resource allocation as a Markov Decision Process (MDP). Here, the "state" represents the current status of the network in terms of demand, interference, and channel quality. At the same time, the "Action" represents the allocation decision made for each service slice in terms of spectrum, capacity, and time. The proposed model focuses on balanced dynamic resource allocation across three main segments: eMBB, URLLC, and mMTC, through ensuring that QoS requirements for each segment are met without impact to the overall system performance. Our simulation results have demonstrated excellent performance by the proposed algorithm when compared to traditional algorithms (i.e., GA, PSO, Q-Learning and Round Robin). In our results, we showed a throughput increase of approximately 180 Mbps, energy efficiency of 0.91 bps/joule, a Fairness Index of 0.88 overall performance improvement between 12% to 15%. As a result of the simulation results, we believe that the PPO-MDP Framework is a good, realistic option for optimizing the use of resources within a dynamically segmented environment, thus improving the ability of a 5G system to efficiently and sustainably respond to a variety of service demands.
Q-MOHO: Q-Machine Learning Guided Multi-Objective Power Optimization for RIS-Assisted MIMO-NOMA Systems
In this study, we introduce Q-MOHO (Q-Machine Learning Guided Multi-Objective Hybrid Optimization) for power allocation in MIMO-NOMA communication systems aided by RIS. To optimize throughput, improve equalization, and reduce power variation, the algorithm adjusts power distribution among three users facilitated by 64 RIS components. The Q-learning approach operates over an SNR spectrum of 0 dB to 30 dB and trains over 500 instances employing a separate power allocation strategy with five tiers. Simulation studies indicate that the proposed Q-MOHO results in an average reward increase of 32% over baseline methods after approximately 350 episodes. Dynamically adjusting with SNR, the optimal power distribution spans from 0.2 at elevated SNR levels to 0.8 at reduced SNRs. The Q-MOHO performance of 5.40 bps/Hz at an SNR of 25 dB is substantial when contrasted with equal power allocation (EPA) and difference of convex optimization (DCO). These results confirm that, in real-world wireless conditions, the Q-MOHO algorithm can effectively acquire optimal strategies and significantly enhance system performance.
Robust Text-Independent Speaker Identification and Verification Using Multi-Feature Fusion and Student’s t Modelling
Abstract—This paper presents a text-independent speaker identification system that utilizes MFCC, LPC, prosody, and optimized multi-level DWT features for robust speaker modeling. The system is designed for multiple standard speech databases, including TIMIT, NTIMIT, SITW, and NIST2008. During train- ing, features from each speaker are normalized to zero-mean and unit-variance, and Student-t distributions are fitted to model the statistical characteristics of each speaker. For testing, features are normalized using the corresponding speaker’s training statistics, and speaker identity is predicted based on maximum log-likelihood estimation over the trained models. Experimental results confirm the superiority of the proposed system, which achieves high accuracy across multiple datasets (e.g., 98.33% on TIMIT, 89.38% on NTIMIT, 96.88% on SITW, and 100.00% on NIST2008) and consistently outperforms existing state-of-the- art methods under AWGN conditions, demonstrating significant improvements in identification accuracy and the effectiveness of multi-feature fusion and Student-t modeling.
Design and Analysis of PTL based Reversible Logic Encoder Circuits for Low-Power Applications
Gray codes are extensively used in modern digital systems to reduce errors occurring due to the sensitivity of binary transitions in communication and arithmetic circuits. This project discusses the design and implementation of a binary-to-Gray code converter using different XOR gate realizations and reversible logic gates to study performance and efficiency. Implementation of the converter using conventional XOR gates, XOR made from transmission gates (TG), Pass Transistor Logic (PTL) XOR, Feynman gate, Toffoli gate, and their PTL-based counterparts is presented. Synthesis and simulation for all designs are done using Cadence tools on 90 nm CMOS technology. Through comparative analysis, the implementations show that PTL-based Feynman gate implementation results in the least power dissipation, which is up to 90.15% lesser power consumption compared with the conventional XOR-based binary-to-gray code converter. The result implies that PTL and reversible logic represent an efficient alternative for low power and area-optimized design of digital circuits, suitable for modern VLSI applications.
Emergent Harmonic Field Routing for Dynamic Networks
Routing in dynamic networks, such as MANETs, remains a challenging problem due to the need for continuous adaptation, robustness to topology changes, and limited coordination among nodes. Traditional routing approaches, including Distance Vector and Link-State protocols often underperform, leading to instability or high overhead in dynamic environments. In this paper, we propose Harmonic Field Routing (HFR), in which forwarding decisions base on a self-organizing potential field maintained through purely local interactions. Each node iteratively updates a scalar state based on neighboring values, leading to the implicit solution of a discrete Laplace equation with Dirichlet boundary conditions. The resulting harmonic field continuously adapts to network changes and induces a loop-free routing structure, where packets follow the steepest descent of the potential. We analyze the theoretical foundations of the method by relating it Jacobi iterative solver. Additionally, we propose two asynchronous HFRs: Red-Black Gauss-Seidel HFR and Chaotic HFR. Experimental evaluation shows that HFR methods provide superior reliability in dynamic scenarios. Furthermore, due to emergence of the early gradient, HFR can find the route comparably fast to the Distance Vector.
Mobile Cellular Network-Based Positioning Using Machine Learning
Positioning systems are essential for various applications, ranging from navigation to location-based services. In the case of mobile cellular networks. This study explored the use of mobile cellular network signals to develop a positioning system for beehives. Our research introduces a new approach for positioning using mobile cellular (LTE, UMTS, and GSM) radio signal data. We trained a machine learning model to predict geographic coordinates (latitude and longitude) based on various parameters extracted from the intercepted radio signals. These parameters include the Cell Identity, Mobile Country Code (MCC), Mobile Network Code (MNC), Location Area Code (LAC), and additional relevant identifiers.Our study offers a novel approach for precise position determination by utilizing information provided by mobile cellular signals.
Detecting and responding to attacks and weather effects in hybrid FSO/RF systems using the Dempster-Schaffer theory with AI algorithms
In this paper, a new intelligent switch for hybrid Free-Space Optical (FSO) RF communication is proposed for improved reliability and security in the presence of dynamic environmental changes and cyber-attack interferences. Using Dempster-Shafer Theory (DST) for reliable threat classification and ANN, KNN, and SVM for machine learning, an extraordinary real-time communication link selection is achieved. A broad training dataset (10,000 simulated samples), covering eavesdropping and jamming threats, fog and dust effects, was used to train and validate the network. Our work combines DST to combine evidence from multiple sources and make an accurate belief assignment for communication modes. In addition, the system exhibits a high claimed confidence, RF and FSO link beliefs around 0.88-0.89 and 0.82-0.83, respectively. The machine learning models have excellent performance on threat detection and mode classification. ANN, KNN, and SVM obtained accuracies of 0.99986, 0.99984, and 0.99930, respectively. All models achieved near-perfect AUC values, where most classes reach 1, meaning a better discriminative performance. Importantly, the performance of ANN was significantly outperformed by KNN and SVM in all metrics, demonstrating its robustness. This work provides an efficient and dynamic approach to keep the communication in difficult FSO/RF links secure and reliable, and brightens the path for future communication systems.
RIS-Aided Cooperative UAV Swarm for mmWave Communications in Urban Environments
This paper investigates the integration of reconfigurable intelligent surfaces (RIS) mounted on cooperative unmanned aerial vehicle (UAV) swarms to enhance mmWave communication in dense urban environments. While mmWave bands offer large bandwidths for high data rates, their severe path loss and blockage remain major obstacles in practical deployments. UAVs provide flexible line-of-sight (LoS) connectivity, whereas RIS enables programmable reflection to strengthen non-line-of-sight (NLoS) links. By combining these technologies, we develop a proposed framework that determines UAV positions and RIS phase configurations under realistic constraints, including quantized RIS phases, UAV, and flight restrictions. Simulation results demonstrate that RIS-aided UAV swarms significantly improve system reliability compared to single-UAV or non-RIS baselines. The findings reveal that both UAV swarm size and number of RIS elements strongly influence performance: average SNR increases with more UAVs, BER decreases exponentially with RIS enlargement, and outage probability exhibits a steep once the number of UAVs and RIS elements is reached a certain limits. In particular, swarms of four to five UAVs with 16–20 RIS elements achieve outage probabilities below 0.05, ensuring near–ultra-reliable communication while balancing energy and hardware costs. These results confirm that cooperative RIS-enabled UAV swarms are a promising solution for robust mmWave coverage in urban 6G networks.
Copyright (c) 2025 International Journal of Electronics and Telecommunication https://creativecommons.org/licenses/by-nc/4.0
A novel approach for Volt-VAR optimization in Radial Distribution System Using Modified Tuna Swarm Optimization
Distributed Generation integrated with Volt-VAr Optimization(VVO) is crucial for dependable and cost-effective distribution grid operations. Energy management strategies are essential to ensure demand, enhance system performance, and guarantee the proper operation of renewable energy-based hybrid systems. In recent years, DG has rapidly gained recognition for its potential to optimize power demands. This work proposes the Modified Tuna Swarm Optimization (MTSO) based swarm intelligence technique to redesign the Radial Distribution network (RDN) for voltage profile improvement and power losses reduction. With 0.9742 p.u. voltage, the proposed MTSO has successfully reduced active and reactive power losses to 105.47kW and 88.25kVAr, respectively in 33-bus system. With 0.9913p.u. voltage, the suggested MTSO reduced the 69-bus's active and reactive power losses to 68.47kW and 47.25kVAr, respectively.
A Non-Invasive Online Method for Real-Time Determination of the Most Probable Energy (MPE) of Industrial Electron Beams
A non-invasive method for real-time determination of the most probable energy (MPE) of industrial electron beams is presented. The method is based on the use of beam energy dispersion in a scanning magnetic field combined with local detection of beam intensity using a thin collecting electrode. Due to the lack of synchronization between the accelerator pulse generator and the scanning magnet, the beam is sampled in an asynchronous manner, allowing reconstruction of the spatial and energy distribution through signal accumulation. Two methods for MPE determination were investigated: local parabolic approximation and a centroid-based approach. The latter was found to be more robust under noisy industrial conditions and provided superior repeatability. Application of the method to an industrial linear accelerator demonstrated that the beam energy exhibits significant temporal fluctuations. The proposed method provides a direct, real-time measure of effective beam energy and can be used as an operational parameter for accelerator tuning and process control. It complements standard dosimetric techniques by enabling continuous monitoring of beam energy under normal operating conditions.
The Association of Polish Electrical Engineers – contribution to strategy 2026 - 2034
SEP is a higher-benefit organization that has been working diligently for over a century to benefit its professional community. It shares the ups and downs and undergoes changes along with its professional community, but also with the entire society it diligently serves. A growing number of scientific, technical, economic, and even political and social organizations are actively using AI analyses and functional support. AI's capabilities encompass a wide range of activities, including planning, contextual situation assessment, problem-solving, strategic game management, and more. In this article, we argue that, in the current situation, without significant AI involvement or support, a well-formulated new statute and the associated modern Strategy of the Association of Polish Electrical Engineers may be inadequate for today's rapidly changing professional and broader social contexts. The AI environment allows for flexible, context-sensitive support in the construction of complex legal and formal documents, such as the organization's operational strategy, with multiple accepted arguments and functional variables related to the organization's activities. It is worth taking advantage of this opportunity and testing it, for example, during discussions on the SEP's Operational and Development Strategy for the next two terms. Here, we test what AI suggests in this regard and what discussion can be opened.
Robustness Analysis of CNN and Convolutional KAN Architectures against Weather and Geometric Distortions in Traffic Sign Recognition
This paper investigates the robustness of traffic sign classification models against real-world visual disturbances. We conduct a comparative evaluation of three distinct architectures: a standard CNN, a hybrid CNN enhanced with Kolmogorov-Arnold dense layers (CNN-KAN), and a fully convolutional Kolmogorov-Arnold Network (CKAN). Unlike traditional CNNs, the KA-based models utilize learnable activation functions, potentially offering improved resilience. The experiments were conducted using the German Traffic Sign Recognition Benchmark (GTSRB) dataset, containing 43 classes of traffic signs. Models were trained and tested on both original images and versions degraded by controlled disturbances, including rotation, blur, brightness variation, and simulated rain. The results demonstrate that the proposed CNN-KAN model provides consistently superior performance under small-to-moderate rotations (up to 20 degrees) and moderate brightness increases, achieving the highest accuracy in all rain-mask scenarios. It remains competitive under blur, where it ranks second only to the standard CNN. Performance decreases were observed only at extreme brightness levels, where both the standard CNN and CKAN maintained higher stability. Overall, the findings highlight the potential of Kolmogorov-Arnold-based architectures for improving robustness in traffic sign recognition systems operating under realistic and dynamically changing environmental conditions.
AI-Generated Image Detection Using Machine Learning Techniques
Rapid developments in the field of generative artificial intelligence have enabled the creation of photorealistic images that are becoming increasingly difficult to distinguish from real photographs. This work aims to implement or adapt three contemporary methods for generated image detection: a Photo-Response Non-uniformity (PRNU) extractor paired with a custom CNN, an Error-Level Analysis (ELA) extractor coupled with the same custom CNN, and an adaptation of the Latent Reconstruction Error framework LaRE2. Each of the methods has been thoroughly tested on a custom dataset, which was constructed by combining part of the Tiny GenImage dataset with images generated by a state-of-the-art transformer-diffusion model named FLUX. The data, balanced evenly between real and fake images, has been separated into five subsets, each respective to one of the included models: ADM, BigGAN, FLUX, Midjourney, and Stable Diffusion v1.5. Experiments were conducted in three different categories, to ensure proper validation of performance of the tested methods.
Zero Short Learning for Wildlife Imagery
This paper introduces an innovative approach for object detection from wildlife images using Zero-Shot Learning (ZSL) with the YOLO-World model. Unlike previous object detection algorithms, which relied on domain-specific training data, YOLO-World is optimized for zero-shot object recognition, thus recognizing a wide range of categories without explicit training on specific labels. The data for this research have been taken from a dataset pre-processed and pre-trained, already split into sets of training and testing, such that the accuracy in the resulting outcome is more precise. Performance evaluation has been taken with the help of key parameters such as precision, recall, F1-score, Intersection over Union (IoU), mean Average Precision (mAP), and proved the adequacy of the model and its efficiency in detecting highly accurate wildlife objects. The experimental results highlight the better performance of ZSL in the detection of wildlife imagery, with a precision of 0.95 and recall of 0.92, thus achieving a mAP of 0.93 and F1-score of 0.87. A comparative analysis with existing YOLOv3 and YOLOv5 models also highlights the merits of the proposed approach in wildlife recognition tasks.
Federated Learning and Quantum Computing for Cybersecurity in the Banking Sector: A Systematic Review
The modern banking sector faces growing cybersecurity challenges, demanding collaborative yet privacy-preserving defense mechanisms. Federated Learning (FL) has emerged as a key paradigm for addressing these needs, but the advent of quantum computing threatens its long-term viability. This development introduces a critical duality: the existential threat of quantum capabilities to break classical cryptography and the transformative opportunities offered by Quantum Machine Learning (QML). While research has addressed these aspects separately, a unified analysis that unites them is lacking. This paper presents the first comprehensive systematic review mapping this dualistic landscape. Following PRISMA guidelines, the review synthesizes the current state of research and evaluates the challenges and opportunities at the intersection of FL, quantum computing, and financial cybersecurity. The findings reveal a strong consensus on the urgent need to integrate Post-Quantum Cryptography (PQC) to secure FL frameworks against future threats. Simultaneously, a long-term vision is emerging to leverage QML to enhance threat detection capabilities beyond classical boundaries. A significant trend toward integrated, layered architectures that combine FL, blockchain, and quantum technologies is also identified. This review concludes that the evolution of FL in banking is inextricably linked to quantum advances, demanding a two-pronged proactive strategy: mitigating immediate risks with PQC while strategically investing in R&D for QML.
Hybrid Audio Encryption Method Based on DCT and Walsh-Hadamard Transform Using Henon Chaotic Maps and Logistic Maps
A new hybrid audio signal encryption algorithm is proposed, combining transform-domain processing using the discrete cosine transform (DCT) and the Walsh–Hadamard transform (WHT) with the application of Henon chaotic maps and the logistic map. The novelty of the method lies in the integration of DCT–WHT transforms to enhance decorrelation and energy compaction, as well as in an adaptive key self-generation mechanism based on the statistical characteristics of each audio frame, which eliminates the need for transmitting and storing external secret data. The algorithm reduces the correlation between the original and encrypted signals to approximately 0.0006 (compared to approximately 0.00045 for the Hénon+LWT method), increases entropy to 5.18 bits (an improvement of approximately 8.3%), provides NPCR approximately 97.63% (versus 98.47%) and UACI approximately 6.67% (versus 15.57%), with RMSE of reconstruction 0.0205 (versus 0.0354). Experiments were conducted on the VCTK corpus (16 kHz, 16-bit, 1000 audio segments). In addition to describing the proposed method, the paper also contains a detailed comparative analysis of its performance relative to other approaches, including DCT+Hénon, WHT+Hénon, and Hénon+LWT algorithms. Such a comparison allows an objective assessment of the advantages and limitations of each method in the context of cryptographic security, reconstruction quality, and computational load.
Towards Improved Phishing Website Detection: Heuristic-Based Approaches vs. Machine Learning
Phishing is widely acknowledged as one of the most insidious types of social engineering attacks. Despite substantial efforts to combat this issue, it continues to evolve in sophistication, resulting in increasing financial losses. Historically, countering phishing involved a blend of human vigilance and software-based detection mechanisms, primarily relying on list-based strategies. However, with the advent of advanced data science, innovative phishing detection techniques utilizing Machine Learning models have emerged and garnered significant research attention. This study aims to comprehensively compare the effectiveness of traditional heuristic-based and modern Machine Learning classification models, while addressing challenges associated with their efficiency. Experimental results involving the Random Forest classifier, although requiring slightly more computational power, demonstrated a substantial increase in detection accuracy (57.2% higher) and a remarkable reduction in testing time (11.28 seconds faster vs 0.01s) when compared to heuristics using the same input data.
E-HUNF: Explainable Hybrid Unsupervised Network Forensics for Robust Cybercrime Anomaly Detection
Anomaly-based network forensics is very important for finding new types of cybercrime that don't have reliable signatures or labelled training data. But most unsupervised detectors only look at one view of normality and don't give any forensic interpretability. This study talks about E-HUNF, an Explainable Hybrid Unsupervised Framework that can find crimes in network traffic. E-HUNF uses a manifold-aware, Centre-regularized auto encoder to get compact latent representations of flows. It then uses these to get three different anomaly scores based on reconstruction error, latent density, and distance from a learnt normalcy Centre. These scores are combined into a hybrid anomaly score with adaptive, percentile-based thresholding to help people make judgements that are mindful of risk. An explainability layer blends local linear surrogates with prototype retrieval to show how each alert's features and historical examples are related. When tested on a standard network-forensics dataset with benign, DoS, Probe/Scan, R2L/U2R, and Botnet traffic, E-HUNF got an accuracy of 0.987, an F1-Score of 0.978, a ROC-AUC of 0.995, and a PR-AUC of 0.993. It did better than Deep SVDD, DAGMM, VAE-AD, and Isolation Forest. Even for small R2L/U2R attacks, the class-wise F1-Scores stay above 0.937. Ablation results show that adding density and boundary cues to reconstruction improves the F1 score by 3.3% over reconstruction-only versions. These results show that E-HUNF has the best detection performance and the most useful forensic transparency for modern cyber-defense operations.
Resource-Efficient and High-Speed PRINCE Cipher for Enhanced RFID Security
Radio Frequency Identification (RFID) is a core technology inside the rapidly expanding Internet of Things (IoT), with several applications in various industries. However, RFID systems have significant privacy and security risks. To address these difficulties, this paper proposes an efficient RFID-based mutual authentication protocol (MAP) that uses the PRINCE lightweight cipher. The proposed PRINCE cipher utilizes a pipelined design supported by an enhanced control mechanism for round operations, resulting in significantly improved throughput and operating efficiency. This design's integrated hardware architecture allows it to carry out both encryption and decryption duties smoothly. The protocol uses five encryption and two decryption operations during the mutual authentication process between RFID tags and readers to provide a safe connection. Performance evaluations show that the suggested PRINCE cipher outperforms existing solutions in terms of area utilization and efficiency. Operating at 224 MHz, the architecture has a remarkable latency of 3.5 clock cycles and a throughput of 4.11 Gbps. When integrated into the RFID-based MAP, it achieves a throughput of 374.6 Mbps, an efficiency of 140.7 Kbps/Slice, and a processing latency of 0.355 μs. Such performance shows its effectiveness and suitability as a robust security solution for IoT environments.
A Multifactor Model of Countering Targeted Attacks Within the Framework of an İnfinite Antagonistic Game Scheme
A multifactor model for countering targeted attacks is developed. This model considers the parties' financial resources (FRs) in an infinite antagonistic game framework. The model helps to make rational decisions on allocating FRs for cybersecurity under the threat of APT attacks. The proposed model allows for determining the game's value and assessing the degree of risk when using optimal mixed strategies for FRs. Optimal mixed strategies refer to a combination of strategies that maximize the player's expected payoff, taking into account the opponent's strategies. This concept is essential for analyzing the financial aspects of countering APT attacks. The relevance of the research is due to the growing number of targeted attacks on critical infrastructure objects. Computational experiments visualize the game's results, which is helpful for cybersecurity analysts. The solution to such a problem contributes to the analytical search for the game's meaning. It makes it possible to find the characteristics of the degree of risk of the players reaching the target when they apply optimal mixed strategies. The found solution contributes to the analysis of the situation with counteraction to the party attacking various objects, including critical ones, with the help of APT attacks.
Enhanced High-Voltage Power Line Insulator and Contamination Classification using Score-Level Fusion
High-voltage (HV) power line insulators are critical for grid reliability, but their performance degrades due to contamination (e.g., salt, soot, excrement). Traditional visual inspection methods are subjective, risky, and time-consuming. To overcome these challenges, this paper proposes an efficient and accurate framework for classifying insulator materials (glass, porcelain, composite) and contamination types. The framework employs features extracted independently from three lightweight CNNs: MobileNetV2, ShuffleNet, and EfficientNet-B0. These features are then fed into base classifiers, and their outputs (scores) are combined using various score-level fusion rules (Majority Vote, Maximum, Average, Sum, Minimum, Product) to enhance classification accuracy and robustness. The framework's effectiveness is validated on three datasets, including synthetic contamination scenarios and real-world images from the Merged Public Insulator Dataset (MPID). Results demonstrate that score fusion significantly outperforms individual lightweight models, achieving accuracies up to 98.49% for contamination classification, 98.59\% for combined material/contamination classification, and 99.26% for real-world material identification. Comparative analysis demonstrates significant improvements over existing methods, including VGG16 (97.00%) and custom CNNs (98.00%), highlighting the efficacy of feature and score fusion. The results validate the framework’s adaptability to diverse environments, computational efficiency, and potential for deployment in resource-constrained settings.
Compact Broadband Circularly Polarized Truncated Parasitic Patch Antenna
In this paper design of a microstrip patch antenna, incorporating parasitic patches and Defective Ground Structure (DGS) to achieve wide bandwidth, high gain and circular polarization is presented. The initial single-patch design operating at 5.55 GHz showed poor impedance matching (-4.52 dB), limited bandwidth, and no circular polarization. To overcome these issues, parasitic patches were added, resulting in dual-band operation at 5.25 GHz and 7.45 GHz, with enhanced return losses of -14.64 dB and -12.04 dB, with impedance bandwidths of 190 MHz and 160 MHz, respectively. The 100 MHz axial-ratio bandwidth was the first step towards achieving circular polarization. Further optimization resulted in a wideband resonance at 6.1 GHz, with a bandwidth of 1.12 GHz (5.12–6.24 GHz) and an improved axial-ratio bandwidth of 1.10 GHz (5.09–6.20 GHz). The gain increased to 10.7 dBi. In the final phase incorporation of H-shaped DGS resulted in a broadband of 2.12 GHz (4.07–6.19 GHz) and axial-ratio bandwidths of 360 MHz and 870 MHz. The recommended antenna is appropriate for satellite communications and radar applications.
Water effects on Body-Centric Communication Radio Channel
Nowadays, the trend in communication technology is gathering clothing with electronic technology. There are two factors despite each others, the convenient of On-Body antennas power supply on the cloth and the power combined with the biological lossy tissues for the human; therefore, efficiency is important when considering Body-Centric communication systems. In this work, the experiments conducted in the study of Off-Body radio with water in the channel are described. To manage the searching properties of water two techniques were studies. The first was a simulation of multilayeres human model with and without a water layer in aspect of transmission coefficient and antenna reflection, the second was to use a gel to absorb water, Polyacrylamide, within special box underneath the On -Body antenna measuring the s11 in frequency 2.1 GHz
Development and Evaluation of Gradient-Based Numerical Algorithms for Object Localization in Multistatic Passive Radar System
Passive radar does not transmit its own signals but relies on external sources of illumination. Estimating a target position requires distance measurements from at least three spatially separated sensors. This paper presents an adaptation of a maximum likelihood estimator (MLE) for target localization using multiple (three or more) bistatic range or DToA (Difference Time of Arrival) measurements. The Gauss–Newton, Levenberg, and Levenberg–Marquardt methods are compared with respect to numerical stability and localization accuracy under different noise levels and initialization conditions.
Cocotb in Modern Functional Verification – a Critical Review and Comparative Analysis with SystemVerilog/UVM
This paper presents a review of recent scientific literature concerning the use of cocotb as a Python-based frame-work for functional verification of digital systems. The study categorizes existing works into three groups: design verification case studies, tool enhancement methodologies, and comparative analyses between cocotb and SystemVerilog/UVM environments. The strengths and limitations of cocotb are evaluated with respect to accessibility, ecosystem maturity, constrained random verification capabilities, and industrial applicability. The analysis reveals that cocotb provides a flexible, cost-effective solution, particularly suited to early-stage RTL verification and open-source workflows, while SystemVerilog/UVM remains advantageous for large-scale industrial projects due to its mature ecosystem and commercial tool integration. The paper identifies current gaps in methodology evaluation, coverage analysis consistency, and reproducibility in existing research, and outlines directions for future development of hybrid verification flows.
Student review of innovations in quantum biophotonics, part 2
This article presents a student review on several selected disciplines that can collectively be classified as quantum biophotonics technologies. Discussed topics represent students' areas of interest, showing their perspective on the development of emerging technologies and their potential implications. The covered subjects range from novel fields, such as optically pumped magnetometers, to new applications of widely studied processes, such as photosynthesis. This paper briefly describes these disciplines and highlights emerging research gaps created by capabilities of these findings.
A novel dual independent control for a quasi Z source inverter driven electromagnetic actuator based energy harvesting system
This paper proposes a Z source inverter assisted electromagnetic energy harvesting system. Electrical and mechanical components in an energy harvesting system produces electrical energy when tuned optimally. Adjustment of damping and resonant frequency is a crucial parameter in operating an energy harvesting system. This paper proposes a Z source inverter assisted system, where the independent control freedom of Z source inverter has been utilized to regulate the output power. The two key components, damping and resonant frequency, have been regulated and therefore, an improved performance of the energy harvesting system has been achieved. The simulation based validation establishes the effectiveness of proposed architecture and control under operating conditions of varying frequency and varying amplitude.
Small-Signal Model of RF InAlGaN/GaN HEMT and On-Chip Calibration Structures
The advanced GaN-based devices utilized for high-performance radio frequency (RF) applications are intensively studied to be used as RF sensors or amplifiers. The paper is focused on microwave characterization of two port passive devices, especially on-chip calibration structures and InAlGaN/GaN electron mobility transistor (HEMT) operating in the cold bias region (zero applied voltage). The acquired S-parameters are inputs to build a passive device small-signal model consisting of three star-connected impedances. The calculated Z-parameters are possible to be utilized for on-chip signal paths design. The parameters to be calculated are assumed frequency independent, however, more proper HEMT modelling requires non-zero voltage application, therefore, the model possibilities are depicted and discussed.
Enhanced One-Day-Ahead AUD/USD Price Exchange Rate Prediction using CatBoost Model
The foreign exchange (Forex) market is highly liquid and volatile, making accurate short-term forecasting both critical and challenging. This study investigates one-day-ahead AUD/USD exchange rate prediction using CatBoost, Random Forest (RF), and Support Vector Machine (SVM) machine learning (ML) models with continuous and discretized technical indicators. Ten technical indicators were derived from 5,027 historical data points. This is the first study to apply discrete technical indicators with CatBoost for recent AUD/USD price forecasting. Results showed that CatBoost achieved the highest accuracy (89.68%) and AUC (0.9609) on the discretised dataset. Statistical test confirmed the significance of CatBoost’s superior performance, highlighting its potential to enhance predictive performance and support real-time decision-making in Forex trading. Keywords— AUD/USD exchange rate; price prediction; CatBoost; one-day-ahead forecasting; machine learning
Analysis of the CPG-DRL Framework for Quadruped Robot Locomotion
Autonomous robots are expected to be crucial in mitigating the lack of precise border surveillance in complex terrains, especially within forests, mountains, and industrial settings, focused on predicting the entry of terrorists and illegal refugees. These robots must demonstrate navigational proficiency in both controlled areas, such as border control gates and borders with plane surfaces, and unplanned country borders in daily surveillance tasks, including mountains, forests, and mud. In this regard, quadruped robots are attracting considerable interest due to their ability to carry substantial surveillance equipment while navigating inclines and obstacles. Given the environment-dependent characteristics of optimal gait patterns, it becomes a necessity for mechanisms capable of independently and effectively optimizing gait patterns for adaptation to different scenarios. To address these challenges, we explored a model for quadruped locomotion that combines central pattern generators (CPGs) with deep reinforcement learning (DRL). Using both external and internal sensing, the agent learns to coordinate rhythmic movement among multiple oscillators to follow speed instructions, while also adjusting these instructions to avoid bumping into things around it. This research helps to use DRL to study important questions in neurology, such as how certain pathways, connections between oscillators, and sensory information affect walking patterns. Moreover, this paper will present fundamental knowledge, research trends, and challenges regarding the implementation of the CPG-DRL framework in robotic perception.
A Comparative Performance Analysis of Phase Configuration Algorithms in Near-Field for Multi-User LIS-Aided UAV Systems
Large intelligent surfaces (LIS) are a key 6G technology, but extremely large antenna arrays (ELAA) necessitate near-field (NF) operation, challenging conventional far-field (FF) beamforming. This paper addresses NF multi-user (MU) beamforming in a LIS-aided Unmanned Aerial Vehicle (UAV) relay system. We develop and compare two distinct phase configuration algorithms for multi-beam NF focusing: a low-complexity Grouped Beamforming heuristic and a Grey Wolf Optimizer (GWO) Beamforming metaheuristic. Performance, including Average Array Gain, Average Minimum Array Gain, their Cumulative Distribution Functions, and computational complexity, is evaluated via Monte Carlo simulations against baseline methods. Both proposed algorithms significantly outperform baselines. GWO Beamforming especially ensuring fairness under high user loads, but incurs a considerable computational cost. Conversely, Grouped Beamforming offers a practical real-time solution. Our findings illuminate the critical performance-complexity trade-offs guiding algorithm selection for NF MU LIS-UAV deployments.
This paper addresses the design of safe finite state machines (SFSMs) using the Verilog hardware description language (HDL) alongside the Quartus design tool to implement FSMs on field-programmable gate arrays (FPGAs). Three styles of finite state machine (FSM) description are proposed (safe, safe_error, and safe_idle), which provide different options for SFSM implementation. Experimental studies on FSM benchmarks have shown that the presented approach reduces SFSM area by an average factor of 2.436 and improves performance by an average factor of 1.588 compared to synthesizing SFSMs in the Quartus design tool. Recommendations are also provided on the practical application of this approach for designing SFSMs
Aerial Mapping Based on Visual for Search and Rescue Robot Application
This research paper aims to develop aerial mapping based on visual using RTAB-MAP for search and rescue robot applications. Mapping these areas is crucial for locating disaster sites that are challenging for humans to reach. Area mapping utilizes a visual RGB-D camera as input and RTAB-Map as a data processing system to represent a 3D map. RVIZ ROS is used for visualizing the map in 3D format. RGB-D images are processed using RTAB-Map to obtain several data points, including odometry, loop closure, timestamp, x, y, z coordinates, and roll, pitch, yaw data from the camera. The test results yielded an optimal area mapping, where the loop closure results in finding corresponding image data quite effectively. As the number of loop closures increases, the area mapping quality improves. In this study, there were a total of 450 loop closure data, which allowed the system to create an optimal 3D map representation. The creation of this map is expected to assist in visualizing hazardous areas to aid in search and rescue efforts during natural disasters.
Copyright (c) 2026 International Journal of Electronics and Telecommunications https://creativecommons.org/licenses/by-nc/4.0
Congestion avoidance in DEC protocol using Leaky Bucket Algorithm
A wireless sensor network is vital in various fields and entails of a large number of sensor nodes. These nodes perform multiple functions, such as sensing, processing, communication, and power management. A substantial part is transferred data, which moves from source to destination. Congestion will occur at the cluster head and during data transfer between nodes. Either resource allocation or traffic division will cause congestion. The main reason for packet loss and the need for retransmission are due to traffic imbalance and congestion. To avoid such circumstances an efficient flow control and congestion controlling schemes need to be adapted. To manage congestion control, a limited number of wireless sensor networks with different protocols are employed. The Deterministic Energy Efficient Clustering (DEC) protocol, which relies entirely on residual energy, is considered to minimize energy consumption. This protocol discards the leaky bucket algorithm. Our idea offers a solution to address traffic congestion and makes improvements to the situation in the event that it arises. Experiments using simulation show that the suggested method can considerably advance increases in lifetime, energy and throughput.
I–V Characteristics of ISFET Devices in Iron(II) Fumarate Solutions
This work presents the fabrication process and I–V characteristics of ISFET devices with an open gate. Measurements were performed in deionized water and in deionized water solutions containing iron(II) fumarate at various concentrations. Changes in the electrical parameters of the transistors were analyzed as a function of electrolyte composition, with particular emphasis on the influence of Fe2+ ions and fumarate anions. The study aimed to assess the sensitivity and stability of ISFET devices in a complex ionic environment.