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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Rate-splitting multiple access in satellite-terrestrial communication systems: performance analysis
This paper investigates the throughput and outage probability (OP) of rate splitting multiple access (RSMA) in satellite–terrestrial communication networks. By dividing user messages into common and private parts, RSMA enhances spectral efficiency and user fairness while addressing hardware impairments and co-channel interference. The proposed hybrid system model is analyzed and compared with non-orthogonal multiple access (NOMA) under various power allocation coefficients and channel conditions. Results show that RSMA achieves lower OP and higher throughput than NOMA, particularly in dense multi-cell deployments. Numerical evaluations further demonstrate RSMA’s robustness against interference and hardware limitations, underscoring its potential as a reliable solution for next-generation satellite–terrestrial relay networks.
Gain enhanced 5.8 GHz patch antenna with defected ground structure: design and measurement
A rectangular microstrip patch antenna including a rectangular defective ground structure (DGS) is introduced to simultaneously enhance gain, bandwidth, and return loss while reducing antenna dimensions. This small antenna is engineered for 5.8 GHz applications, functioning throughout the frequency spectrum of 5.62 to 5.94 GHz. The design was executed on a 1.6 mm thick FR-4 substrate with a relative permittivity of 4.3, utilizing a microstrip line feed. The dimensions of the antenna are 31.75×28 ×1.6 mm³. The design approach utilized computer simulation technology (CST) Microwave Studio simulation software. The antenna attains resonance at 5.8 GHz, providing an initial bandwidth of 270 MHz and a return loss of -26 dB. A rectangular DGS was implemented to boost performance, yielding a 21.89% increase in bandwidth to 323 MHz and substantially enhancing the return loss from -23 dB to -47 dB. The gain increased from 3.95 dBi to 5.10 dBi, indicating a 30% enhancement, while sustaining an efficiency of around 83% at the resonant frequency. The antenna was constructed, and experimental measurements of parameters including gain and return loss closely matched the computer results.
A proposed scheduling algorithm for real time application in 5G networks
The third-generation partnership developed the fifth-generation specifications to satisfy the expansion of mobile applications and the grown demand for extra data flow. As the real time services in 5G networks are widespread, professional scheduling algorithms are necessary to deal with the assignment of the scarce frequency resources among different categories of applications, ensuring the quality of service and improving the user experience. This paper proposes a real time flow scheduling algorithm by enhancing the scheduling metric to prioritize real time flows such as voice and video, particularly as the packet delay approaches its threshold time. The performance metrics of the proposed algorithm were evaluated and compared to three well-known algorithms, which are the modified largest weighted delay first, the exponential proportional fair, and the logarithmic rule. The simulation results, which was conducted by a dedicated software, showed that the proposed algorithm achieved up to 1.5 times the throughput of the other algorithms and resulted in less than half the video packets loss ratio compared to others, moreover, it offered a higher fairness index between users than other algorithms for video packets.
Improved channel quality indicator estimation using extended Kalman filter in LTE networks under diverse mobility models
Accurate channel quality indicator (CQI) estimation is crucial for optimizing resource allocation, improving link adaptation, and sustaining high performance in long term evolution (LTE) networks. In real-world scenarios, where channel conditions fluctuate rapidly due to user mobility, inaccurate CQI estimation can lead to suboptimal scheduling, degraded throughput, and reduced quality of service (QoS) for both users and network operators. Traditional Kalman filter (KF) approaches often struggle with the non-linear and time-varying nature of wireless channels, especially under unpredictable mobility patterns. This paper proposes an improved CQI estimation method based on the extended Kalman filter (EKF), which models non-linear system dynamics more effectively. The method is implemented in LTE-Sim, analyzed using MATLAB, and evaluated under random and Manhattan mobility models. Results show that across mobility regimes, KF outperforms EKF in the structured Manhattan model, while in the non-linear random-direction model, EKF yields markedly higher signal-to-interference-plus-noise ratio (SINR) stability and robustness to channel variation with SINR values above 10 dB between 300-450 s and a peak of approximately 60 dB. These results underscore the importance of mobility-aware estimation strategies in enhancing LTE network adaptability and throughput.
A dual-band modified-rectangular patch with parasitic antenna for 2.4/5 GHz wireless local area network applications
This research presents the design and implementation of a dual-band patch antenna (DBPA) optimized for 2.4 GHz and 5 GHz wireless local area network (WLAN) applications. The antenna features a modified rectangular patch with a cut corner and two parasitic rectangular patches, enabling dual-band operation with enhanced gain. The DBPA is fed by a 50-Ohm coplanar waveguide and fabricated on a single-layer copper circuit board using a flame-retardant 4 substrate with a relative permittivity of 4.3 and a thickness of 1.6 mm. A prototype with compact dimensions of 0.040×0.040×0.0009 λ³ was constructed and experimentally evaluated. Measurements reveal a nearly omnidirectional radiation pattern, achieving peak gains of 2.92 dBi at 2.4 GHz and 4.25 dBi at 5 GHz. The antenna demonstrates a wide 10 dB return loss bandwidth of 67.7% (1.7–3.44 GHz) for the lower band and 56% (4.59–8.16 GHz) for the upper band. The strong agreement between simulated and measured results validates the design’s potential for practical and scalable implementation. This DBPA design offers a simpler, more compact, and wider-bandwidth alternative to conventional antennas, making it ideal for modern WLAN systems.
Substrate thickness variation on the frequency response of microstrip antenna for mm-wave application
Substrate height (Hs) is an important parameter that influences antenna propagation. This research designed a low-profile 28 GHz microstrip antenna on a polyimide substrate with varying Hs using CST Studio software. The simulated results and MINITAB software were used to develop regression model equations, which analyzed the impact of Hs variation on the antenna performance. The proposed models’ equations have indicated an increase in average responses of resonant frequency (Fr), percentage bandwidth (% BW), gain (G), return loss (RL), and efficiency (ƞ) as the Hs decreased. The antenna achieved a BW of 3.87 GHz at Hs 0.525 mm and 5.54 GHz at 0.025 mm, a G of 3.89 dBi at Hs 0.525 mm and 3.91 dBi at Hs 0.025 mm, and an ƞ of 94.19% at Hs 0.525 mm and 98.24% at Hs 0.025 mm. The antenna was fabricated and tested, and the experimental results were validated with the models’ equations. The thinner substrate resulted in an improvement in the antenna performance.
The comparison of underwater source localization between Riemannian MFP and blind channel equalizer
Blind channel equalization (BCE) has been widely used in underwater communications due to its strong robustness against multipath propagation and its suitability for rapidly varying environments. However, there has been little research on the application of BCE for underwater source localization. On the other hand, conventional matched field processing (MFP), and particularly Riemannian MFP (RMFP), have been regarded as highly effective for this problem. In this paper, based on the statistical characterization of the signal-to-noise ratio (SNR) in underwater acoustic channels, we propose a method for estimating the channel transfer function, which is then used to construct a blind channel equalizer. A source localization approach using the proposed BCE is also presented. The localization performance using BCE is comparable to that of RMFP, achieving a depth error of 10 meters and a range error of 100 meters, while requiring significantly lower computational complexity.
DDoS attack detection using optimal scrutiny boosted graph convolutional and bidirectional long short-term memory
The distributed denial of service (DDoS) attack occurs when massive traffic from numerous computers is directed to a server or network, causing crashes and disrupting functionality. Such attacks often shut down websites or applications temporarily and remain among the most critical cybersecurity challenges. Detecting DDoS is difficult and must occur before mitigation. Recently, machine learning and deep learning (ML/DL) have been employed for detection; however, architectural limitations restrict their effectiveness against evolving attack methods. This paper presents a novel framework, scrutiny boosted graph convolutional–bidirectional long short-term memory and vision transformer (SBGC-BiLSTM-ViT), which integrates graph convolutional, BiLSTM, and ViT models with machine learning classifiers such as support vector machine (SVM), Naïve Bayes (NB), random forest (RF), and K-nearest neighbors (KNN). The integration enables autonomous extraction of critical features, enhancing precision in detecting and classifying DDoS attacks. To further boost performance, a Bayesian optimization algorithm (BOA) is applied for hyperparameter tuning of SBGC and ML methods. Evaluation on benchmark datasets UNSW-NB15 and CICDDoS2019 demonstrates that the proposed approach achieves higher accuracy and effectively identifies new DDoS variants, outperforming conventional methods.
An architecture to build high performance infrastructures on cloud computing for telecommunications organizations
Nowadays, many small and medium organizations of the telecommunication sector must solve intrinsic heterogeneous problems in their own environments that have been associated with high computational complexities of their algorithms. These class of problems require to use high performance computing (HPC) infrastructures for their executions. Therefore, these must be accelerated to reduce significantly the execution times, included many problems that should be solved in real time: like the processing of multiples video streams, the pattern recognition in big volumes of data, the traffic analysis in cybersecurity solutions and among others. The building of HPC infrastructure permits to organize the technological platform to increase the productive and business indicators of the organizations. This paper describes an architecture as reference model and ecosystem for the building and systematic improvement of HPC infrastructures based on practical experiences from successive process of HPC infrastructure building on cloud deployment. That’s processes have been useful for the organizations permitting the integration of emergent hardware and software components launched to the international market. This landscape vision is pertinent for academics, scientifics and business organizations compelled to implement scientific and engineering applications to diverse fields that have a high impact in the society digital transformation.
Realization of Bernstein-Vazirani quantum algorithm in an interactive educational game
Quantum algorithms are celebrated for their computational superiority over classical counterparts, yet they pose significant learning challenges for non-physics audiences. Among these, the Bernstein-Vazirani (BV) algorithm stands out for its quantum speedup by efficiently identifying a secret binary string. However, the accessibility of such algorithms remains constrained by their inherent technical complexity. To address this educational gap, this paper introduces a gamified, web-based tool that innovatively reinterprets the BV algorithm’s complex mathematical settings through an into engaging scenario of identifying broken lamps. Players assume the role of an investigator, utilizing both classical and quantum solvers to identify faulty lamps with minimal queries. By transforming the BV algorithm into an intuitive gameplay experience, the tool helps reducing technical barriers, making quantum concepts much more comprehensible for educators and students than traditional methods that demand rigorous mathematical understanding. Developed using Qiskit, IBM’s Python package for quantum computation, and deployed via Flask, a popular Python microframework for building web applications, the game effectively simplifies complex quantum algorithms while demonstrating the practical applications of quantum speedup. This contribution advances quantum education by merging technical depth with interactive design, fostering a broader understanding of quantum principles and inspiring new innovations in gamified learning.
Advanced image processing techniques for intelligent building environments using pattern recognition
The use of smart building environments, along with high-technology image processing and pattern recognition, is discussed within this paper. The study shows that the Canny edge detection algorithm is better than the Sobel operator in the edge clarity, continuity and accuracy in segmenting those edges, posting 92.7% of edge detection accuracy. Incorporating fuzzy logic, the hybrid Hough transform, and sophisticated segmentation techniques, like adaptive simple linear iterative clustering (SLIC) superpixel division, the study advances line detection and feature identification in the images of buildings. The variational autoencoder (VAE) and principal component analysis (PCA) help optimise the feature extraction substantially by retaining more than 93% variance at a lower dimension. In addition, adaptive Otsu thresholding and region-growing segmentation allow improving the segmentation accuracy, resulting in a significant increase in building detection F1 score from 77.3% to 89.6%. Irrespective of the Hough transform issues like noise sensitivity and over-joining, the results suggest computing process ideas that are computationally effective, scalable, and applicable in smart building systems. This study suggests extending the current advancement of hybrid models and incorporating them with the urban planning procedures, energy control, and building security systems.
Elevating cultural understanding: interactive museum exploration using 3D AR and MDLC framework
Limited access to information and interaction with artifacts in museums often hinders visitors from gaining a deeper understanding of the culture and historical context presented. This study addresses this challenge by developing a three-dimensional (3D) augmented reality (AR)-based interactive museum that enhances the museum visitor experience through an intuitive user interface (UI) and enriched content related to the exhibited artifacts. This study explores the potential of 3D AR technology in enhancing visitor engagement and interaction with museum exhibits, providing a more immersive and informative experience. This study uses the multimedia development life cycle (MDLC) as a framework to develop a 3D AR-based interactive museum. By applying the MDLC approach, this study integrates advanced AR technology with comprehensive and detailed content, resulting in a structured and user-centered interactive platform. Key benefits of this approach include enhanced interactivity, enriched artifact information, and an intuitive interface that facilitates easier access to museum content. The findings indicate that the developed interactive museum successfully overcomes the barriers of limited accessibility of information and interaction with artifacts. Through the application of advanced AR technology, the museum visitor experience is significantly enhanced, making the museum more inclusive, interactive, and educative for visitors.
Prediction of land suitability for food crop types using classification algorithms
Decision-making in the selection of crop types is often conducted using conventional approaches. It is relying on limited experience and knowledge without considering the latest data or information. This approach has the loss of opportunities to use crop types. The crop types are more suited to environmental conditions and market demand, and it inhibits the application of innovation in agriculture. Therefore, the use of information technology becomes crucial to enhance accuracy in determining land suitability and crop selection. This study recommends the Random Forest algorithms and AdaBoost due to their excellent performance across all metrics (AUC, CA, F1, Precision, Recall) on various dataset sizes with scores above 0.9, so it is the solution to predict land suitability for specific crop types. Furthermore, it enables farmers to maximize land potential and achieve optimal yields.
Optimized human detection in NLOS scenarios using hybrid dimensionality reduction and SVM with UWB signals
Trapped victim localization in search and rescue (SAR) operations is especially difficult in non-line-of-sight (NLOS) conditions, where traditional techniques fail due to debris and signal distortion. Ultra-wideband (UWB) NLOS signal datasets offer a promising alternative but are often high-dimensional and noisy. This study proposes an optimized dimensionality reduction framework combining an adaptive human presence detector (AHPD) with genetic algorithms (GA) and independent component analysis (ICA), followed by support vector machine (SVM) classification. The approach is tested on a public NLOS dataset comprising 23,522 dynamic instances, each with 256 signal samples per attribute, simulating complex SAR scenarios including rubble and dynamic obstacles. The results indicate that the AHPD+GA+SVM model reached an accuracy of 85.78%, sensitivity of 80.00%, and specificity of 96.46%, which is better than the AHPD+ICA +SVM model that had an accuracy of 79.20%, sensitivity of 73.07%, and specificity of 81.05%. These findings demonstrate the framework’s robustness and scalability, making it a strong candidate for real-time human detection in disaster recovery missions.
Advanced pneumonia classification using transfer learning on chest X-ray data with EfficientNet and ResNet
Pneumonia is a serious lung infection that demands accurate and timely diagnosis to reduce mortality. This study explores the use of deep learning and transfer learning for classifying chest X-ray images into two categories: normal and pneumonia. A total of 5,632 labeled images were used to train and evaluate six pre-trained convolutional neural network (CNN) architectures: EfficientNetB1, B3, B5, B7, ResNet50, and ResNet101. The models were tested across three training scenarios by varying learning rates (LR), batch sizes, and epochs. Among all models, EfficientNetB3 achieved the highest performance, with accuracy of 99.04%, precision of 99.76%, recall of 99.23%, and F1-score of 99.34%. These results indicate that EfficientNetB3 offers a robust and efficient solution for pneumonia detection. This research contributes to the development of intelligent diagnostic tools in the medical field and provides practical guidance for selecting effective deep learning models in clinical imaging applications.
Lexicon-based comparison for suicide sentiment analysis on Twitter (X)
Suicidal individuals frequently share their desires on social media. As a result, it was determined that a learning machine for early detection of suicide issues on social media was required. This study aims to examine Twitter (X) users’ suicide-related sentiment expressions. The results of searching X for the keywords ‘suicide’, ‘wish to die’, and ‘want to commit suicide’ for 4 months yielded 5,535 tweets. Following the cleaning process, 2,425 tweets were collected. The findings of labeling with the lexicon-based valence aware dictionary and sentiment reasoner (VADER) and Indonesia sentiment (INSET) lexicon, which psychologists confirmed, revealed that VADER was more accurate (92.1%) than INSET (81.6%). Sentiment research reveals negative (86.4%), positive (11.1%), and neutral (2.5%) sentiment. Support vector machine (SVM), K-nearest neighbor (KNN), and Naïve Bayes modeling results show accuracy above 86%, with SVM having the best accuracy (87.65%). Because of its great accuracy, this model can be used to identify and analyze suspicious behavior relating to suicide on X. Further research is still required, despite the excellent identification of early indicators of suicide ideation from social media posts.
Improved classification for imbalanced data using ensemble clustering
Imbalanced datasets frequently occur in fields like fraud detection and medical diagnosis, where the number of instances in the majority class vastly exceeds those in the minority class. Traditional classification algorithms often become biased towards the majority class in these scenarios. To address this challenge, we introduce a novel method called improved classification using ensemble clustering (ICEC) for imbalanced datasets in this paper. ICEC merges classification with the strengths of consensus clustering to improve the classifier’s generalization ability. This approach utilizes a cluster ensemble to capture the structural characteristics of both the majority and minority classes, and the stable clustering scheme thus delivered is used to generate new auxiliary features. These features enhance the existing feature set, helping classifiers develop a more ro bust predictive model. Extensive testing on fifteen imbalanced datasets from the knowledge extraction based on evolutionary learning (KEEL) repository demonstrates the effectiveness of our proposed method. The approach was evaluated for random forest (RF) and linear support vector machine (SVM) classifiers on these data sets. Results indicate that ICEC proved to be effective for both classifiers, with an observed F1-score improvement of more than 10% for SVM and 3%for RF.
Water quality monitoring using soft computing techniques in Udupi Region, Karnataka, India
A monitoring of water quality index parameters using soft computing technology is the current research focus as the main challenge of which is to design a soft computing algorithm with the highest accuracy and less computation time. For the secondary dataset obtained by the government database, this research proposes a water quality prediction and classification method based on decision tree algorithm. The comparative analysis is made for the different highest accuracy algorithms like decision tree algorithm with support vector machine (SVM), k-nearest neighbour (KNN) classifier, linear discriminant analysis, Naïve Bayes classifier and logistic regression. Decision tree algorithm had the highest accuracy compared to other algorithms. The KNN algorithm used as clustering algorithm to plot the two classes good and bad. The trend analysis of the water quality is performed with various water quality parameters like pH, fluoride and total dissolved solids (TDS) test results are plotted and observed for the variations of the values with respect to increase in time. The performance is measured with statistical indices and the prediction accuracy of 0.99 and mean squared error of 0.05. The results prove that the KNN algorithm found to be better for clustering purposes.
Hybrid Kolmogorov-Arnold and convolutional neural network model for single-lead electrocardiogram classification
This study proposes a hybrid Kolmogorov-Arnold networks (KANs) and convolutional neural networks (CNN) to classify electrocardiogram (ECG) signal abnormalities in one lead ECG data of wearable telemedicine. The hybrid model combines CNN to extract hierarchical features from sequential data and KANs to model non-linear relationships with fewer parameters as an efficient classification. The study explores the model’s capacity to balance accuracy, computational efficiency, and memory usage as critical factors for real-time health monitoring in resource-constrained environments on the single-lead MIT-Beth Israel hospital (MIT-BIH) Supraventricular Arrhythmia database with five different class labels. For comparison, standalone CNN and KAN models were also trained on the same balanced dataset. The CNN model achieved an accuracy of 96.62%, precision of 96.81%, and recall of 96.53%. The KAN model, while computationally efficient, performed less effectively, with an accuracy of 94.15%, precision of 95.01%, and recall of 92.57%. In contrast, our hybrid KAN-CNN model outperformed both, attaining an accuracy of 97.53%, precision of 97.66%, recall of 97.40%, and a low loss of 0.0840. The study also explores the impact of quantization and compression on model performance, revealing that both CNN and Hybrid KAN-CNN models retained high accuracy post-quantization, whereas the KAN model exhibited a more significant drop in performance.
Deep transfer learning based disease detection and classification of tomato leaves - a comparative analysis
A wide variety of diseases have a significant impact on tomato plants. To avoid crop quality issues, a prompt and precise diagnosis is crucial. Classifying plant diseases is one of the numerous applications where deep transfer learning models have recently produced remarkable results. This study dealt with fine-tuning by contrasting the most advanced architectures, including Inception V3, ResNet-18, ResNet-50, VGG-16, VGG-19, GoogLeNet, and AlexNet. In the end, a comparison evaluation is conducted. Nine distinct tomato disease classes and one healthy class from PlantVillage make up the dataset used in this study. Precision, recall, F1-score, and accuracy were the basis for a multiclass statistical analysis that assessed the models. The ResNet-50 approach yielded significant results with precision: 82%, recall: 81%, F1-score: 81%, and accuracy: 85%. With this high success rate, it is reasonable to say that mobile applications or IoT-compatible gadgets implemented with the ResNet-50 model can assist farmers in identifying and safeguarding tomatoes against the aforementioned diseases.
Experimental validation of positioning and tracking system using ultra-wideband and low-cost microcontroller units
Indoor positioning systems (IPS) have become increasingly critical in various applications, from asset tracking to smart environments. While global positioning system (GPS) offers precise outdoor localization, its signal is unavailable indoors. Ultra-wideband (UWB) technology emerges as a promising alternative due to its high accuracy, robustness against multipath interference, and ability to operate in dense environments. Aiming to develop an affordable and efficient system, we present a UWB-based IPS using the DW1000 UWB chip, evaluated with two different low-cost microcontroller units (MCUs): the ESP8266 system-on-chip (SoC) and the Arduino Uno R3. The findings suggest that the ESP8266 SoC is a superior choice for building an affordable and efficient UWB IPS, making it a compelling option for widespread adoption in budget-sensitive applications.
Design and implementation of a power supply unit for a smart airport lighting control system
In this paper, a buck-boost converter is used to design and implement a power supply for intelligent airport lighting system applications. Innovative approaches to power supply design are required to meet the increasing demand for fault detection solutions for lighting systems in vital infrastructure such as airports. The buck-boost converter’s ability to step up or down input voltage levels makes it particularly well suited to this application, ensuring stable operation over a range of load conditions. With a fast-settling time of 26 ms at 6.1 V input and dropping to 6 ms at 22.4 V input, the power supply offers exceptional output stability. The output stabilizes steadily at 5 V with low ripple over a wide input voltage range (5 V to 23 V). The physical prototype, simulations, component selection and circuit design are all carefully tested and supported by experimental results. According to these results, the proposed converter-based power unit operates with stability and reliability, making it ideal for demanding lighting applications. By improving power stability in dynamic environments, this work improves the reliability of aviation infrastructure power systems and lays the groundwork for future advances in intelligent airport technologies.
Regulation of glucose insulin metabolism using feedback linearization
Diabetes is a chronic disease that occurs when the pancreas does not produce enough insulin, or when the body is not able to effectively use the insulin it produces. Insulin is a hormone that regulates blood sugar levels, this regulation is done by the pancreas. When this organ is damaged, the patient will have to regulate its blood sugar level themselves. This task is really painful and we will have to resort to an artificial pancreas or we will have to design a regulator which stabilizes blood sugar at its basal value. Several controls have been developed and the objective of this paper is to use input output linearization technique to regulate blood glucose levels by injecting an adequate quantity of insulin. The glucose insulin metabolism is a non-linear system whose input is the quantity of insulin to be injected and the output is the blood glucose measured in the blood. Simulations examples are given to demonstrate the usefulness of the command developed.
Enhanced torque control for horizontal-axis wind turbines via disturbance observer assistance
This paper presents an enhanced control strategy for optimizing energy capture in horizontal axis wind turbines operating in the partial-load region (region 2). The proposed approach builds upon conventional standard torque control (STC) by incorporating a generalized extended state observer (GESO) that follows the active-disturbance-rejection paradigm. Although traditional torque control methods have proven effective under steady wind conditions, they often lack robustness against disturbances, system faults, and model uncertainties inherent in wind energy systems. The proposed observer-assisted control scheme addresses these limitations by estimating and compensating for total disturbance signals, including non-modeled dynamics, parameter uncertainties, and actuator faults. The effectiveness of the proposed control strategy is validated through comprehensive simulations using a 5 MW wind turbine model subjected to realistic operational conditions. Simulation scenarios include turbulent wind speed profiles and actuator degradation to assess controller performance. The results demonstrate improved robustness and energy capture efficiency compared to the conventional control approach, while maintaining the simplicity of the implementation. This work contributes to the development of more reliable wind energy conversion systems (WECSs) by offering a practical solution that improves both performance and fault tolerance in partial load operation.
Development of hydraulic servo controller for mechanical testing with optimization of PID tuning methods
This study explores the use of hydraulic servo control (HSC) systems in static and dynamic structural testing, focusing on optimizing proportional, integral, derivative (PID) controller tuning. The HSC system comprises three main components: hydraulic, control, and measurement systems. To achieve optimal performance, the research begins with preparing setpoint displacement/force data and developing mathematical models for the cylinder actuator and servo valve, incorporating sensors like load cells and linear variable differential transducers (LVDTs). A closed-loop transfer function is used to predict outputs that align closely with setpoint values. Three PID tuning methods—Ziegler-Nichols, Cohen-Coon, and adaptive control—are evaluated. Simulation results show all methods yield satisfactory performance with evaluation errors below 1.5%. Implementation tests further confirm effectiveness, with root mean square deviation (RMSD) values under 1%, indicating high precision. Despite promising results, the study acknowledges limitations due to restricted datasets and test conditions. Future research should address broader dynamic load variations, nonlinearities such as fluid leakage and hysteresis, and integrate intelligent optimization techniques like machine learning to enhance robustness and adaptability. This work contributes to improving the reliability and accuracy of HSC systems in structural testing, paving the way for smarter, more responsive control strategies in engineering applications.
Cost-effective long-range secure speech communication system for internet of things-enabled applications
A new communication framework has been developed that allows voice transmission over long distances for internet of things (IoT) applications such as healthcare, smart cities, and remote monitoring in the least costly way and most secure manner. The system is based on long range (LoRa) technology and takes advantage of its spread spectrum technique, to provide long range transmission without the high-power requirements. The main limitation is LoRa’s bandwidth with a maximum throughput of 22 kbps for data. This presents a challenge for voice transmission communications. To address this shortened bandwidth issue, researchers developed an innovative compression solution that compresses voice data to less than 8 kbps to fit into LoRa’s capabilities. The compression allows for real practical voice communications and possibly can provide even greater distance than an uncompressed voice transmission update. The voice communications transmissions have cryptographic protection in place to protect the transmitted voice messages from unauthorized access.
Optimal active disturbance rejection control with applications in electric vehicles
This work proposes an optimal control strategy based on a modified active disturbance rejection control (ADRC) that considers disturbance weighting for a three-phase induction motor under rotor field-oriented control (FOC) to enhance energy efficiency. Induction motors (IMs) are widely used in electric vehicles (EVs) due to their cost-effectiveness and technological maturity. However, improving energy efficiency remains a key challenge, as it directly impacts vehicle range. The proposed approach employs ADRC, where part of the disturbance rejection task is handled offline by a hybrid optimization algorithm combining particle swarm optimization (PSO), tabu search (TS), and simulated annealing (SA) to tune a state-feedback controller. The controller parameters are optimized using a composite cost function that balances energy consumption and performance. Simulation and experimental results indicate that disturbance weighting has a significant impact on both problem complexity and performance. Optimal weighting improves the overall system response compared to conventional disturbance rejection methods. Energy and performance analyses show that disturbance weighting enhances energy usage compared to the traditional ADRC method, suggesting a novel efficiency control strategy for electric machines.