TELKOMNIKA (Telecommunication Computing Electronics and Control)
TELKOMNIKA (Telecommunication, Computing, Electronics and Control) ISSN: 1693-6930, e-ISSN: 2302-9293 is a peer-reviewed, scientific journal published by Universitas Ahmad Dahlan (UAD). The aim of this journal is to publish high-quality articles that highlight the latest significant developments in the fields of electrical and electronics engineering, as well as computer science. Its scope encompasses applications related to telecommunication, computing, electrical and electronics, instrumentation, and control. This journal holds accreditation from the National Journal Accreditation (ARJUNA) under Indonesia's Ministry of Education, Culture, Research, and Technology. It has been granted First Grade status, or "Peringkat 1", as per Decree No. 169/E/KPT/2024, dated 1 October 2024. This recognition emphasises the journal's commitment to upholding high academic standards and promoting knowledge advancement within its discipline. The quality and credibility of the articles published in its pages assure researchers and scholars of its integrity. First published in 2003, the journal initially operated as a bimonthly publication and has been releasing six issues per year since 2018. <a style="background-color: #fff; border-bottom: 0 solid transparent; border-left: 0 solid #495e57; border-radius: 0; border-right: 2px solid #495e57; border-top: 0 solid transparent; color: #45474b; display: inline-block; font-family: Roboto,Tahoma,Verdana,Segoe,sans-serif; font-size: 12px; font-weight: 400; mso-border-alt: none; padding-bottom: 0; padding-top: 0; text-align: center; text-decoration: none; width: auto; word-break: keep-all;" onmouseover="this.style.backgroundColor="#0070BB",this.style.color="#ffffff"" onmouseout="this.style.backgroundColor="#ffffff",this.style.color="#45474B"" href="/index.php/TELKOMNIKA/about/submissions#onlineSubmissions" target="_blank">Submit Now<a style="background-color: #fff; border-bottom: 0 solid transparent; border-left: 0 solid #495e57; border-radius: 0; border-right: 2px solid #495e57; border-top: 0 solid transparent; color: #45474b; display: inline-block; font-family: Roboto,Tahoma,Verdana,Segoe,sans-serif; font-size: 12px; font-weight: 400; mso-border-alt: none; text-align: center; text-decoration: none; width: auto; word-break: keep-all;" onmouseover="this.style.backgroundColor="#0070BB",this.style.color="#ffffff"" onmouseout="this.style.backgroundColor="#ffffff",this.style.color="#45474B"" href="https://www.youtube.com/watch?v=B9lB-4sZNI4" target="_blank">How to Register<a style="background-color: #fff; border-bottom: 0 solid transparent; border-left: 0 solid #495e57; border-radius: 0; border-right: 2px solid #495e57; border-top: 0 solid transparent; color: #45474b; display: inline-block; font-family: Roboto,Tahoma,Verdana,Segoe,sans-serif; font-size: 12px; font-weight: 400; mso-border-alt: none; text-align: center; text-decoration: none; width: auto; word-break: keep-all;" onmouseover="this.style.backgroundColor="#0070BB",this.style.color="#ffffff"" onmouseout="this.style.backgroundColor="#ffffff",this.style.color="#45474B"" href="https://www.youtube.com/watch?v=hQD9-HeNy1k" target="_blank">How to Submit
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Reconfigurable ultra-wideband hexagonal antenna with two notched-band features for wireless applications
Owing to the demand for frequency agility, a switchable ultra-wideband (UWB) hexagonal antenna was developed in this study. The proposed antenna features two notch filters introduced by two U-shaped slots on the patch to reduce interference from other wireless networks by rejecting the unique frequency bands. In addition, the proposed antenna comprises a hexagonal radiator attached to a feeding 50 Ω standard microstrip line. To fabricate the antenna prototype, a substrate (Rogers RT/Duroid 5880) with loss tangent and relative permittivity values of 0.0009, and 2.2, respectively, was used. Frequency and pattern reconfigurability were achieved by changing the electrical equivalent circuit of two positive-intrinsic-negative (PIN) diodes sandwiched within two U-shaped slots. The evaluation confirmed that the antenna operated within the D1&D2-ON configuration across the entire UWB range while, effectively filtering the wireless body area network (WBAN) (6.10–6.56 GHz) and radar application (9.16–10.79 GHz) bands when both diodes were OFF. The radiation efficiency and gain reached values of 92.9 % and 7.5 dB, respectively. The proposed design offers a robust performance with enhanced interference rejection. This makes it suitable for modern cognitive radio systems.
Advanced signal transformation techniques to improve spectral efficiency in visible light communication systems
Visible light communication (VLC) offers high-speed wireless communication using the visible light spectrum. Achieving high spectral efficiency while maintaining a low bit error rate (BER) remains a challenge. This paper explores the use of quadrature amplitude modulation (QAM) combined with orthogonal frequency division multiplexing (OFDM) to address these challenges. Matrix laboratory (MATLAB) simulations show that QAM-OFDM achieves a BER of 0.001 at comparable signal-to-noise ratios (SNR), outperforming traditional hermitian symmetry (HS), complex signal mapping (CSM), and quad-light emitting diode (LED) complex modulation (QCM) techniques. Unlike CSM, and QCM, which increase complexity, and BER, QAM-OFDM efficiently utilizes available bandwidth, reducing errors, and enhancing spectral efficiency. The study concludes, that QAM-OFDM happens to be the optimal solution for the future VLC systems, offering better performance within both efficiency, and reliability.
Metamaterial-enhanced four-port MIMO antenna for 5G communications at 28/38 GHz
This work presents a novel compact four-port multiple-in multiple-out (MIMO) antenna enhanced with metamaterial unit cells for 5G millimeter-wave (mmWave) applications at 28 and 38 GHz. Compact MIMO antennas at mmWave bands often suffer from high mutual coupling, which degrades isolation and diversity performance. To address this, the proposed design integrates metamaterial loading around each radiating element to effectively suppress coupling, enhance isolation, and improve overall efficiency. The antenna, measuring 27×27×0.8 mm³, is implemented on a flexible FR4_epoxy substrate (εr=4.4), enabling compatibility with portable and embedded devices. Full-wave simulations performed in both ANSYS high-frequency structure simulator (HFSS) and computer simulation technology (CST) studio suite confirm the effectiveness of the approach, achieving an exceptionally low envelope correlation coefficient (ECC) (0.0001), a fivefold reduction in channel capacity loss (CCL), and a wide impedance bandwidth of 25.90–34.93 GHz with |S11| below −10 dB in both operating bands. The design also exhibits stable directional gain and low sidelobes. Compared with recent compact MIMO antennas reported in the literature, the proposed configuration offers significantly improved isolation, bandwidth, and mechanical flexibility. These features make it a strong candidate for integration into high-capacity 5G modules, portable terminals, and compact internet of things (IoT) communication systems.
Business intelligence through data visualization: a case study using marketing campaign dataset
In today’s competitive business environment, data-driven marketing strategies are essential for successful campaign outcomes. This study presents a comprehensive analysis of marketing campaign data, emphasizing its role in enhancing customer engagement, improving decision-making, and increasing conversion rates. It explores the complexity of campaign dynamics and consumer behavior, demonstrating how business intelligence and data visualization techniques support informed marketing decisions and actionable insights. Advanced data science methods such as data cleaning, feature engineering, and cross-validation enhance predictive accuracy and campaign optimization. Visualization plays a central role in transforming raw data into interpretable insights, enabling businesses to identify trends in customer preferences and purchasing behavior. Key findings reveal that customers aged 51–70, particularly those with higher education and income levels, show the greatest purchasing power, especially for wine and meat products. These insights help align marketing strategies with data-driven understanding to design personalized campaigns that resonate with target audiences. By combining analytical methods with effective visualization, businesses can develop impactful campaigns that drive engagement, boost conversions, and foster revenue growth. The study concludes with directions for future research, including real-time data processing and automated decision-making systems to ensure continuous improvement in digital marketing strategies.
Watermarking on spread multi-frame data video using discrete wavelet transform hybrid and frame ratio message variance
The exponential growth of video sharing demands secure and imperceptible watermarking methods. This study presents a video watermarking framework using discrete wavelet transform (DWT) with hybrid sub-band embedding and multi-frame allocation to balance imperceptibility, capacity, and robustness. Watermark bits are adaptively distributed across low-low (LL), low-high (LH), and high-low (HL) sub-bands of selected frames, with uncompressed audio video interleave (AVI) ensuring coefficient integrity. Experiments on 640×360 videos show LL-only embedding achieves high imperceptibility (peak signal-to-noise ratio (PSNR) > 38.6 dB, structural similarity index measure (SSIM) ≥ 0.9945, and bit error rate (BER) = 0), while LL-dominant hybrids increase capacity with slight robustness trade-offs. Embedding in LH and high-high (HH) sub-bands raises distortion vulnerability. Under cropping, BER rises from 0.005 to 0.205 (0–50%), and normalized correlation (NC) drops from 0.998 to 0.802, remaining acceptable for ≤30% cropping. The scheme resists joint photographic experts’ group (JPEG) compression quality factor 20–80 (Q20–Q80), resizing (≥70%), and mild Gaussian blur (3×3), maintaining efficient decoding under higher payloads. Future work may apply error-correction coding and redundancy-aware embedding for improved resilience. Overall, the proposed method offers a secure, adaptive, and efficient solution for video authentication and covert communication.
Retrieval-augmented generation for Arabic legal information: the family code case study
This document describes the implementation and evaluation of a retrieval-augmented generation (RAG) system to improve access to and understanding of Moroccan law, particularly the family code in Arabic. The research addresses the drawbacks of the widely used linguistic model applied to complex legal terminology in Arabic and aims to help citizens access crucial legal data. We built a new custom dataset with 2.5 k question-answer pairs while preprocessing and using the BGE-m3 embedding model in this experiment. Performance metrics, such as mean reciprocal rank (MRR), Recall@k, and F1-score, indicate that the RAG approach is effective compared to the use of standalone large language models (LLMs). Moreover, an evaluation on metrics such as the blue score, fidelity, response relevance, and contextual relevance indicated that the matching of meanings and context were well captured, which signifies a very good semantic understanding. The research highlights the need for language-specific model specialization in Arabic and presents its main challenges, such as dialectal variations and appropriate evaluation measures. The results indicate that well-developed RAG systems offer a promising approach to improving access to legal information in Arabic-speaking practice communities and to guiding future research and development in this field.
Optimizing vehicle inspection efficiency and integrity in Tanzania through blockchain technology
This study proposes a blockchain-based solution to improve the efficiency and integrity of vehicle inspections in Tanzania, with a focus on the National Institute of Transport. The system combines Hyperledger fabric, a permissioned blockchain that provides identity management and fine-grained access control, with the InterPlanetary file system (IPFS), a decentralized content-addressed store for large artifacts such as inspection images and portable document format (PDF) forms. Smart contracts encode inspection rules and approvals, which yield tamper-evident records, faster retrieval of histories, and uniform enforcement across centers. A mathematical model based on the M/M/1 queueing system, combined with a cost-benefit analysis, supports empirical findings: the total inspection cycle time decreases by approximately 30 percent, the average waiting time declines by about 20 to 30 percent, and annual operational savings reach approximately USD 800,000. These gains enhance auditability and transparency, which contribute to road safety outcomes by reducing opportunities for tampering and error. The design includes offline capture with later synchronization, which suits centers with intermittent connectivity. The approach is transferable to adjacent public services, for example, licensing, fine collection, and selected registries.
Enhancing handover management in 5G networks with encoder-decoder LSTM for multistep forecasting
The continuous evolution of wireless communication networks, fueled by advancements in 5G and the envisioned potential of 6G technologies, has introduced significant challenges in mobility management and handover (HO) optimization. The frequent HOs due to network densification, particularly at high frequencies like millimeter waves (mmWave) and terahertz (THz) bands, can lead to increased latency, and potential service disruptions. To address these issues, artificial intelligence (AI) driven approaches are emerging as promising alternatives. This paper explores the use of deep learning techniques for predictive HO management. An encoder-decoder long short-term memory (ED-LSTM) model is proposed to generate multistep predictions of future reference signal received power (RSRP) values. The model was trained and evaluated on two distinct real-world drive-test datasets. The results demonstrate that the proposed ED-LSTM model achieves lower prediction error, with a mean absolute error (MAE) of 2.07 for dataset 1 and 2.33 for dataset 2, and a mean absolute percentage error (MAPE) of 2.80% for dataset 1 and 2.96% for dataset 2. Overall, the ED-LSTM outperforms the bidirectional LSTM (BiLSTM) and standard LSTM (S-LSTM) model, achieving improvements of 33–38% on dataset 1 and 48-50% on dataset 2 in terms of MAE and MAPE, respectively.
An insight on using deep learning algorithm in diagnosing gastritis
Chronic autoimmune gastritis (CAG) is a condition in which the stomach membrane is significantly impacted by inflammation. Despite the availability of numerous modern medical techniques, the detection of this condition continues to be a difficult challenge. White light endoscopy (WLE) has been employed to diagnose gastritis, but it has been subject to certain constraints. This technique is most effective when executed by an endoscopist who possesses a high level of expertise. In the present day, WLE is frequently accompanied by artificial intelligence (AI) due to its superior ability to detect defects that lead to damage. Recently, there has been a substantial increase in the efficacy of AI in conjunction with the expertise of endoscopists in the detection of CAG. The 25,216 intriguing case studies were examined in the eight selected studies. The collection comprised 84,678 frames and 10,937 images. The AI was 94% sensitive (95% CI: 0.88-0.97, I2 = 96.2%) and 96% specific (95% CI: 0.88-0.98, I2 = 98.04%). The receiver operating characteristic curve had an area of 0.98 (95% confidence interval: 0.96–0.99). A camera is highly effective when combined with AI to assist in the identification of CAG and is advantageous for clinical review.
Application of artificial intelligence in emission prediction for hybrid electric vehicles: integrating ANN and GPR
In recent years, hybrid electric vehicles (HEVs) have emerged as a promising solution to mitigate vehicular emissions and improve fuel efficiency. This study focuses on the Toyota Prius HEV, employing advanced artificial neural networks (ANN) and Gaussian process regression (GPR) to develop a predictive model for vehicle emissions. The model considers multiple pollutants, including carbon monoxide (CO), carbon dioxide (CO₂), hydrocarbons (HC), and nitrogen oxides (NOx), measured under diverse driving conditions. The ANN model predicts emission trends, while GPR estimates prediction uncertainty, enhancing the model’s robustness. The GPR models achieved uncertainty levels of ±0.829 ppm for CO, ±9.978 ppm for HC, ±0.144 ppm for NOx, and ±411.256 ppm for CO₂, respectively, underscoring the robustness of the integrated approach for emission prediction. This research aims to support the development of more sustainable vehicle technologies and inform policy making for environmental sustainability (e.g., Euro 6/Euro 7 standards). Overall, the study addresses how artificial intelligence (AI) can be utilized to achieve accurate multi-pollutant emission predictions in HEVs. The findings reveal that an integrated ANN-GPR approach yields superior predictive performance (R² values approaching 1.0) with quantifiable uncertainty, outperforming a stand-alone ANN model and providing a robust solution to the emission prediction challenge.
A hybrid ARIMA and DNN approach with residual learning for electric vehicle charging demand forecasting
The rapid growth of electric vehicle (EV) adoption has created significant challenges for power grid management and charging infrastructure planning. Accurate forecasting of EV charging demand is therefore essential to ensure reliable electricity supply and effective station deployment. This study proposes a novel hybrid forecasting framework that combines autoregressive integrated moving average (ARIMA) with deep neural networks (DNN) through a residual learning strategy. In this approach, ARIMA models the linear temporal patterns, while DNN captures the nonlinear residuals, resulting in improved efficiency and predictive accuracy. The proposed hybrid model is one of the first applications of the residual learning approach for EV demand forecasting in Indonesia. Experimental evaluation using real-world daily consumption data shows that the hybrid method achieved the highest prediction accuracy of 98.22%, consistently outperforming single-model baselines. Beyond technical performance, the model can support stakeholders in planning charging infrastructure and help maintain grid stability in rapidly growing EV ecosystems.
Machine learning-based reconstruction of missing rainfall extremes: a comparative analysis with classical models
The limited availability of daily rainfall data remains a key challenge in rainfall data analysis. This study assesses the effectiveness of spatial interpolation and bias correction techniques using satellite-derived rainfall data to fill missing observations in the Banten and Jakarta regions. Three interpolation methods inverse distance weighting (IDW), kriging, and spline were compared. Nine statistical and machine learning-based bias correction methods were applied to climate hazards group infrared precipitation with station data (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP), and global precipitation measurement-integrated multi-satellite retrievals for GPM (GPM IMERG). Performance was evaluated using root mean square error (RMSE), mean absolute error (MAE), bias, Pearson correlation (R), and Kling-Gupta efficiency (KGE) in the expert team on climate change detection and indices (ETCCDI) extreme index. The research findings indicate that CHIRPS with quantile mapping (QM) bias correction delivers the best performance, followed by random forest regression (RFR) as the most accurate machine learning method. In spatial interpolation, IDW stands out as the leading method. Testing the extreme index ETCCDI confirms that CHIRPS-QM consistently outperforms machine learning and interpolation methods. In general, CHIRPS-QM and IDW represent the most effective combination of techniques for reconstructing daily rainfall, particularly extreme events. This study uniquely integrates spatial interpolation and bias correction in a unified evaluation.
Optimization of principal component analysis and k-nearest neighbors in cultivation area classification red onion
This research aims to increase the effectiveness in classifying shallot cultivation areas through the combined application of principal component analysis (PCA) and k-nearest neighbors (KNN) methods. Shallot is an important agricultural commodity, and identification of optimal areas for its cultivation is essential to support food self-sufficiency. Onion cultivation is generally done in the highlands. One of the areas with shallot cultivation in North Sumatra Province is Berastagi, Karo Regency. This research was conducted by determining the spatial extent of upland land. In the use of data there are 2 types of data that will be used: land suitability dataset and land condition dataset for each region. The PCA method is utilized to simplify the data structure by reducing the number of dimensions and removing insignificant attributes, while KNN was used to classify regions based on their suitability for shallot cultivation. This research produces a classification map that can be used to identify the most optimal areas for shallot cultivation. The test results with the regional spatial dataset using precision, recall and fi-score testing accuracy value 0.92%, and macro avg value 0.94%, weighted avg value 0.93%.
Comparative performance analysis of convolutional neural network-architectures on coffee-bean roast classification
The classification of coffee bean roast levels using Agtron standards has evolved from traditional subjective methods to technology-driven approaches employing advanced artificial intelligence. Recent advancements in computer vision have demonstrated the capability of convolutional neural networks (CNNs) in providing objective and consistent roast level classification compared to human visual assessment, which is prone to variability and subjectivity. This research presents a performance analysis of five CNN architectures (AlexNet, ResNet, MobileNet, VGGNet, and DenseNet) for classifying coffee beans into eight distinct Agtron roast levels. The comprehensive methodology encompasses four phases: i) data acquisition, ii) image preprocessing, iii) model training and validation, and iv) evaluation metric. During training-validation, DenseNet outperformed other models, achieving 99.702% training accuracy and 77.68% validation accuracy. In the testing evaluation, DenseNet also led with an average testing accuracy of 93.8%, followed by ResNet at 92.6%, VGGNet and AlexNet both at 92.4%, and MobileNet at 89.7%. The results show that the DenseNet shows promise in classifying Agtron coffee-bean roast classification.
Automatic diagnosis of rice plant diseases using VGG-16 and computer vision
Pathogens are organisms that cause disease in plants. In the case of rice, these pathogens can include fungi, bacteria, nematodes, protozoa, and viruses. This study aims to investigate rice plant diseases using a hybrid system that employs the visual geometry group-16 (VGG-16) architecture and computer vision techniques, alongside various optimization algorithms and hyperparameters. We utilize the convolutional neural network (CNN) architecture of VGG-16 for feature extraction, implementing a process known as transfer learning. Additionally, this research compares different optimization algorithms with the VGG-16 model to identify the most effective optimization for the CNN architecture applied to the tested dataset. The main contribution of this study is the development of a model for identifying rice plant diseases based on data collected using VGG-16 for feature extraction and neural networks for classification with specific parameters. Our findings indicate that the best optimization algorithm is stochastic gradient descent (SGD) with momentum, achieving training and validation loss results of 0.173 and 0.168, respectively. Furthermore, the training and validation accuracies were 0.95 and 0.957. The model’s performance metrics include an accuracy of 95.75, precision of 95.75, recall of 95.75, and an F1-score of 95.73.
Mixed attention mechanism on ResNet-DeepLabV3+ for paddy field segmentation
Rice cultivation monitoring is crucial for Indonesia, where paddy field areas de clined by 2.45% according to the Central Bureau of Statistics due to land func tion changes and shifting crop preferences. Regular monitoring of paddy field distribution is essential for understanding agricultural land utilization by farmers and landowners. Satellite imagery has become increasingly common for agricul tural land observation, but traditional neural networks alone provide insufficient segmentation accuracy. This study proposes an enhanced deep learning architec ture combining residual network (ResNet)-DeepLabV3+ with coordinate atten tion (CA) and spatial group-wise enhancement (SGE) modules. The attention mechanisms establish direct connections between context vectors and inputs, enabling the model to prioritize relevant spatial and spectral features for precise paddy field identification. The CA module enhances spectral feature discrim ination, whereas the SGE improves spatial characteristic representation. The experimental results demonstrate superior performance over the baseline meth ods, achieving intersection over union (IoU) of 0.85, dice coefficient of 0.89, and accuracy of 0.95. The proposed mixed attention mechanism significantly improves the accuracy and efficiency of automatic crop area identification from satellite imagery.
Analyzing temporal properties of speech trajectory using graph structures towards speech recognition
Speech signal analysis aims to identify patterns within data to develop effec tive recognition algorithms. This process primarily utilizes feature extraction techniques such as linear predictive coding (LPC), linear predictive cepstral co efficients (LPCCs), and Mel-frequency cepstral coefficients (MFCCs). These features are crucial for constructing recognition algorithms that leverage both statistical and deep learning methods. While deep learning models require ex tensive datasets, they often prove unsuitable for low-resource languages. The Hidden Markov model (HMM)is the most widely adopted statistical framework in speech processing. However, HMMs are characterized by state-dependent models, where each state interacts only with its neighboring states. This limita tion restricts HMMs from capturing long-term signal properties, highlighting the need for addressing these constraints at the feature extraction stage. Most feature extraction methods rely on short-term signal processing, which further limits the comprehension of speech utterances. To overcome these limitations, alter native methods are necessary to capture more comprehensive patterns. This pa per presents a graph-based approach for analyzing speech trajectories and their temporal properties, which are subsequently validated using HMMs in speech recognition tasks. Graph-based representations on a low-resource Telugu dataset improve recognition accuracy by 13% while reducing processing time compared to traditional LPC.
Novel fractional order sinusoidal oscillators using operational trans resistance amplifier
The design of fractional order circuits in very large-scale integration (VLSI) domain is gaining the interest of many researchers. At the same time design of fractional circuits using the current mode devices is attracting the research community. In this paper, several possible fractional order sinusoidal oscillators using operational trans resistance amplifier (OTRA) as a basic building block is presented. The necessary condition for the frequency of oscillation and condi tion for oscillations is derived. Fractional order operator sα is the most crucial one to be approximated. In this paper, the fractional order element is approxi mated by the continued fraction expansion (CFE). The approximation is carried out up to fifth order. The circuits are tested with the simulation software named LTspice. The results agree with the theoretical one. The proposed circuits of fers a frequency of 15 MHz, 20 MHz, and 25 MHz which is higher in value as compared to the existing circuits. The proposed circuits finds applications in bio medical, communication circuits.
Particle swarm optimization-optimized integrator backstepping for the control of electric wheelchairs velocity
Most people suffering from temporary or permanent disabilities rely on wheelchairs or electric powered wheelchairs (EPW) to maintain autonomy of movement. To address different EPW control challenges, several studies have investigated this kind of robot. This paper focuses on the optimization of the integrator backstepping control parameters of the EPW. The system operates using two permanent magnet synchronous motors (PMSM), noted for their great efficiency, substantial torque, minimal noise, and robustness. At first, the dynamic model for both EPW-motors is showned. After that, a nonlinear integrator backstepping command based on Lyapunov’s second technique, which combines the choice of the energy function with the control laws, was applied to the resulting global model. To ensure optimal performance, the control parameters were tuned by means of an optimization approach. Specifically, the particle swarm optimization technique (PSO) was employed to search for the optimal parameters (gains) of the integrator backstepping controller. In order to assess the performance of the optimized backstepping–based control approach, numerical simulations were conducted to illustrate the evolution of both electrical and mechanical velocity- related variables.
The effectiveness of bentonite in reducing soil resistance in acidic water swampland
This study aims to evaluate the effectiveness of bentonite mixtures in reducing grounding resistance in acidic swampy areas. The method used is an experiment comparing resistance before and after the addition of bentonite in various compositions (25%, 50%, 75%, and 100%), supplemented with linear regression analysis. The results showed that bentonite significantly reduced soil resistance in three types of electrodes: iron rebar, copper-coated iron, and galvanised iron. The highest reduction in resistance was achieved in iron rebar electrodes, from 35.93 Ω to 22.46 Ω (a 37% reduction) with the addition of 25% bentonite. Linear regression analysis showed a consistent negative relationship between the percentage of bentonite and grounding resistance, with a coefficient of determination (R²) varying between 26.40% and 73.39%. These findings indicate that bentonite is effective as a natural grounding material in acidic swampy areas. This research makes an important contribution to the development of more efficient and safer electrical systems in swampy areas and challenging environments, while also supporting the use of natural materials to reduce dependence on synthetic chemicals.
Performance of piezoelectric energy harvesters at various angles
Piezoelectric materials are capable of generating electricity in response to mechanical strain, making them suitable for energy harvesting applications. Piezoelectric energy harvesters (PEHs) are promising alternatives for renewable energy generation, particularly because mechanical strain can be induced in various ways, including utilizing wind flows. This study investigates the performance of a PEH integrated with a laboratory-scale wind-driven micro-windmill. The experiment is carried out by rotating blades of the windmill intermittently; thus, it contacts the PEH, inducing oscillatory motion and generating strain, which finally produces electricity. The configuration angle is varied with 30°, 45°, and 60° to produce variation of power output analyzed in this study. The results demonstrate that a lower configuration angle, specifically 30°, produces the highest voltage near 1.4 V. This is due to the alignment of the applied force with the natural bending direction of the cantilever, resulting in greater induced strain and increased voltage output. Conversely, increasing the configuration angle reduces the effectiveness of force induced to PEH, diminishing strain induction and electrical generation, which only about 1.2 V. The finding of this study can potentially contribute to advance the design and optimization of PEHs for renewable energy applications, particularly in powering microelectronic devices.
A design and reconfigurable phase shift inductor inductor capacitor converter for switch failures
The reliability of a converter operation strongly affects overall system performance and is vital for uninterrupted power-electronic operation. Harsh operating conditions and environmental stresses degrade device performance and reduce reliability. In particular, a switching device failure may prevent an inductor inductor capacitor (LLC) resonant converter from operating near its resonant frequency while still maintaining stable output voltage, potentially causing loss of operation as well as significant drops in both efficiency and power delivery. To address this challenge, this paper proposes a fault-tolerant topology and control strategy for the LLC converter under open circuit switch (OCF) faults. The proposed method integrates a bypass arm with a secondary-side series configuration; when a primary-side open-circuit fault occurs, the auxiliary switch is activated to bypass the faulty leg, reconfiguring the secondary side into a voltage doubler rectifier (VDR). This reconfiguration enables continuous operation with an output voltage doubled relative to the normal condition, while minimizing performance degradation. Simulation results confirm that, even under a single-switch OCF, the proposed approach maintains an efficiency of 98% with output voltage fluctuation limited to less than 1%. Compared to conventional methods, the proposed strategy greatly enhances reliability and fault tolerance, making it well-suited for high-efficiency power conversion applications.
Lightning studies on effects on distribution lines: a bibliometric analysis
The study of lightning effects on distribution lines is of vital importance for the reliability and safety of electrical systems, as lightning is one of the main causes of failures. The purpose of this study is to perform a bibliometric analysis to evaluate academic productivity trends and research trajectories in this field. The methodology was based on a comprehensive search of the Scopus database, from which a total of 545 articles published between 1932 and 2024 were analyzed. For the analysis, the VOSviewer tool and the Bibliometrix library in R were used. The results reveal a constant increase in productivity since the 1970s, with Japan and China emerging as the most prolific countries. The research has evolved from early theoretical and experimental studies toward the use of advanced computational models and, more recently, the application of machine learning techniques for fault detection. In conclusion, the findings of this study provide a consolidated view of the field, which is fundamental for engineers to be able to design more robust protection systems and to guide future research toward model validation and the integration of renewable energy technologies.
Design and analysis of a new scheme of the FOSTA for DFIG based wind turbine
An super-twisting algorithm (STA)-based controller was designed and implemented in this study to achieve precise control over the stator active and reactive power of a doubly fed induction generator (DFIG)-equipped wind turbine device. The fractional calculus theory (FCT) allowed the STA to maximize its effectiveness and performance. A distinct form is sent to the FCT-based STA controller. The stator flux orientation technique uses control that is independent of stator active and reactive powers. In order to achieve a quick system with sufficient precision and a robust control strategy, the hybrid method control is based on the fractional-order super twisting algorithm (FOSTA) and FCT. To demonstrate the performance, efficacy, and resilience of the stated nonlinear approach, a number of simulations are provided.
Performance enhancement of PV generator using a sensor based dual axis solar tracking system in Algeria
This article presents the implementation of a two-axis solar tracking system and its impacts to increase the performance of the photovoltaic system in northern Algeria. The system enhances the efficiency of solar systems by optimizing their exposure to sunlight making the sunbeam perpendicular to solar panel. The main objective of the study is to develop a technically proficient and economically viable solution to increase solar energy production. The design relies on integrating light sensors and motors controlled by an Arduino board, enabling automatic adjustment of solar panel positions. This approach offers dynamic and precise orientation, based on light dependent resistor (LDR) sensor design and threshold value, resulting in a significant increase in energy output. The results show that the dual-axis solar tracking system can capture 60.64% more solar energy, taking into account the power consumption of the two electric actuators. The findings of this study will positively influence the promotion of clean and sustainable energy sources while providing a practical solution for more efficient utilization of solar energy in Algeria.
Escalating QoS by firefly optimization of CGSTEB routing protocol with subordinate energy alert gateways
Wireless sensor networks (WSNs) comprise large numbers of sensor nodes that are highly constrained by limited battery power, making energy-efficient routing essential for sustaining network lifetime and service quality. Among existing solutions, the general self-organized tree-based energy balancing (GSTEB) pro tocol with clustering has been widely adopted for energy-aware communication. However, GSTEB and its clustered variant often suffer from energy imbalance, high packet loss, and reduced quality of service (QoS) due to excessive load on cluster heads (CHs). To address these challenges, this paper introduces an enhanced routing framework that integrates firefly optimization with clustered GSTEB(CGSTEB)andintroduces subordinate energy alert gateways (SEAGs). The firefly algorithm is applied to optimize CH selection through a fitness func tion that balances residual energy and node proximity, ensuring efficient cluster formation and adaptive load distribution. Meanwhile, SEAGs establish a two hop communication model between CHs and the base station (BS), reducing CH energy consumption and preventing premature node failures. Simulation exper iments conducted in NS2 demonstrate that the proposed firefly-CGSTEB with SEAG significantly improves QoS metrics, including network lifetime, energy utilization, throughput, and packet loss rate, compared with conventional CG STEB. These results confirm the effectiveness of combining metaheuristic opti mization with gateway-assisted routing for resilient and energy-efficient WSNs.
Object detection and tracking with decoupled DeepSORT based on αβ filter
With the rapid growth of the population, the demand for autonomous video surveillance systems has substantially increased. Recently, artificial intelligence has played a key role in the development of these systems. In this paper, we present an enhanced autonomous system for object detection and tracking in video streams, tailored for transportation and video surveillance applications. The system comprises two main stages: detection stage; this stage employs you only look once (YOLO)v8m, trained on the KITTI dataset, and is configured to detect only pedestrians and cars. The model achieves an average precision of 97.3% and 87.1% for cars and pedestrians classes respectively, resulting a final mean average precision (mAP) of 92.2%. Tracking stage; the tracking component utilizes the DeepSORT algorithm, which originally incorporates a Kalman filter for motion prediction and performs data association using cosine and Mahalanobis distances to maintain consistent object identifiers across frames. To improve tracking performance, we introduce two key modifications to the original DeepSORT: architecture modification and Kalman filter replacement. The tracking tests are carried out on KITTI and MOTChallenge Benchmarks. The final order tracking accuracy (HOTA) scores achieve 77.645 and 54.019 for Cars and Pedestrians classes respectively in the KITTI-Benchmark and 45.436 for the Pedestrians class in the MOTChallenge-Benchmark.
Adaptive DICOM images encryption using quadtree and lightweight ITUBee algorithm
The encryption of medical images protects the privacy of patient information transmitted over networks and communications. In this paper, a lightweight encryption method for medical images is proposed, combining a quadtree-based segmentation and a modified ITUBee algorithm for encryption. A digital imaging and communications in medicine (DICOM) image is divided into variable-size blocks using the Quadtree technique, and the key is generated through a two-dimensional Henon map; the first dimension is used in the confusion process (bit permutation) of the pixel values, and the second sequence is used to generate the key schedule through the application round function. Different numbers of rounds are applied to the ITUBee method based on the size of the segments in the Quadtree, making the algorithm adaptive by increasing the round number when the block size is reduced. The method is used as a lightweight encryption method for encrypting all blocks, utilizing different round numbers for each block size to balance the degree of complexity with the total time consumption of the DICOM image. The result reinforces the proposed method, which produced a high mean squared error (MSE) between the DICOM image and the Encrypted One, and a lower peak signal-to-noise ratio (PSNR). The proposed generated numbers were also tested using national institute of standards and technology (NIST) to evaluate the randomness.