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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Deep learning-based power amplifier linearization in OFDM systems with unknown channel state information
This paper presents an end-to-end deep learning-based approach for orthogonal frequency-division multiplexing (OFDM) communication systems impaired by nonlinear power amplifiers (PAs) and channel fading. The PA nonlinearity is modeled using the modified Rapp model, and simulations are performed on a 64-subcarrier OFDM system with a cyclic prefix (CP) of 8 and 16-quadrature amplitude modulation (16-QAM). The proposed autoencoder-based OFDM–PA (AE-OFDM-PA) system jointly optimizes the transmitter and receiver through end-to-end learning, enabling simultaneous compensation of both PA nonlinearities and channel distortions without requiring explicit channel state information (CSI) estimation. Instead, the model leverages embedded pilot sequences to learn the implicit CSI representation directly from data, allowing the receiver to correct amplitude and phase distortions adaptively. Simulation results demonstrate that AE-OFDM-PA significantly outperforms conventional OFDM and OFDM-PA systems, achieving over 70× block error rate (BLER) improvement compared with the uncompensated OFDM-PA system at an input back-off (IBO) of 3 dB. Furthermore, the proposed method achieves approximately 11.5 dB adjacent channel leakage ratio (ACLR) improvement over the classical memory polynomial digital predistortion (DPD) technique, while slightly reducing the peak-to-average power ratio (PAPR). Overall, AE-OFDM-PA provides a robust, spectrally efficient, and low-complexity solution for nonlinear and fading environments with unknown or varying CSI.
Secure hybrid power-frequency multiple access in satellite terrestrial communication systems: a performance study
This paper investigates a secure hybrid power–frequency multiple access (PFMA) framework for satellite–terrestrial communications. By integrating power- and frequency-domain multiplexing, PFMA achieves approximately 4 dB lower transmit signal-to-noise ratio (SNR) than non-orthogonal multiple access (NOMA) for the same connection outage probability (COP) at SNR > 0 dB, and it reduces the COP by up to 30% at low-to-medium SNRs. It further decreases the intercept probability (IP) by 20–25% at PS = 10 dBm. Closed-form COP and IP expressions are derived under shadowed-Rician fad ing with both internal and external eavesdroppers and validated via Monte Carlo simulations. Parameter analysis indicates that PFMA’s SNR gain can either ex tend coverage by 60% or save 37% energy, providing design guidelines for 6G, satellite IoT, and emergency communication systems. The single-cell assump tion points to future work on multi-cell and mobility scenarios.
Enhancing reflective elements of intelligent reflective surfaces in 6G communications using artificial intelligence
The dynamic landscape of 6G communication networks necessitates innovative strategies to address energy inefficiency and signal degradation in densely populated regions with limited line-of-sight (LOS) coverage. A novel technology known as an intelligent reflecting surface (IRS) has emerged; it can dynamically modify the characteristics of electromagnetic waves to enhance signal propagation. Unfortunately, current IRS models frequently neglect the balance between energy efficiency (EE) and the quantity of reflective elements (N) in Rayleigh fading scenarios. This study introduces an algorithm called dynamic-static particle swarm optimization (DS-PSO) aimed at improving EE and decreasing the quantity of reflective components in the performance optimization of IRS. The research assesses the proposed model in comparison to single-input single-output (SISO) systems, conventional IRS models, and IRS models from prior studies within a realistic urban framework. The optimized IRS, which only uses seven reflective elements, has a peak EE of 366 Mbit/Joule. This is a big improvement over IRS models from earlier research, as shown by the numbers. The findings indicate that artificial intelligence (AI)-driven optimization can enhance IRS technology for sustainable and efficient 6G networks.
Performance evaluation of serverless cloud-native API deployment: a case study on a mobile health application
As software applications become increasingly complex, there is a growing need for scalable, flexible, and high-performance backend solutions. Cloud computing-based application programming interfaces (APIs) address these demands by enabling developers to offload resource-intensive tasks to the cloud while eliminating the burden of infrastructure management. This study presents a case study using Obesifix, a mobile health application for real time dietary monitoring and personalized nutrition recommendations. Two deployment models were evaluated: a traditional server-based architecture using Google Compute Engine (GCE) and a serverless approach using Google Cloud Run (GCR). Performance testing was conducted with Apache JMeter under simulated loads of 60, 120, and 180 users across four critical API endpoints (register, login, recommendation, prediction). Results show that GCR consistently achieved 20–30% lower response times and 15–20% higher throughput compared to GCE, while maintaining 0% error rate, lower memory consumption, and more balanced virtual central processing unit (vCPU) utilization. Time to first byte (TTFB) remained below 800 ms across all scenarios, confirming good server responsiveness. These findings highlight the scalability and efficiency benefits of serverless architectures for mobile health applications. Future research should explore asynchronous programming paradigms, autoscaling thresholds, and cost-performance trade-offs, as well as multi-cloud deployments to enhance system resilience and generalizability.
Developing tuberculosis drug information system using a throwaway prototype: Udayana Hospital case study
Tuberculosis (TB) remains a major health problem in Indonesia, and efficient drug management is essential to ensure continuous treatment and prevent resistance. At Udayana University Hospital, manual recording and reporting often caused delays and errors, while integration with the National Tuberculosis Information System (SITB) was limited. This study developed a TB drug information system using the throwaway prototype model to address these challenges and enhance hospital workflow efficiency. The system implementation demonstrated measurable improvements in operational performance, with data entry errors reduced by 83% and the average recording time per patient shortened by 35% compared to the previous manual process. User feedback confirmed improved usability, accuracy, and reliability in supporting hospital workflows and timely reporting. In conclusion, the proposed system effectively improved the accuracy and efficiency of TB drug management while addressing hospital level operational challenges. This study demonstrates the applicability of the throwaway prototype model in healthcare information-system development and provides insights for scaling and integration with national TB programs.
Implementation of markerless augmented reality and cyber physical-social systems for smart tourism application
Lampung province holds substantial tourism potential that remains underutilized due to fragmented information and limited promotional strategies. This study introduces a smart tourism application integrating markerless augmented reality (AR) with cyber-physical-social systems (CPSS), representing the first implementation of its kind for location-based tourism in the region. The novelty lies in the hierarchical coordinate transformation architecture (HCTA), a multi-layer computational framework employing the Haversine formula to achieve high-precision mapping of geographic coordinates into AR-optimized perceptual views. The system was evaluated for geolocation accuracy, resource utilization, backend scalability, AR rendering robustness, and user experience. Results show strong performance: geolocation tests across seven destinations yielded a mean error rate of 1.5%; AR operations remained efficient with 8–10% central processing unit (CPU) and 140–160 MB random access memory (RAM) usage; and rendering was stable across 360° device orientation. Backend tests confirmed scalability, sustaining 56 requests per second with zero failures under 100 concurrent users. A user study with 20 participants using the user experience questionnaire-short (UEQ-S) revealed highly positive outcomes, with overall scores 2.275, all within the Excellent benchmark. These findings confirm that the application is not only technically robust and efficient but also engaging and enjoyable, offering a scalable framework for immersive smart tourism ecosystems.
Comparison methods in a decision support system for determining JavaScript frameworks
The selection of an appropriate JavaScript framework in web-based software development often leads to errors when the chosen framework is incompatible with the design. The ability to make decisions quickly, accurately, and precisely is therefore a key factor in successful software design. Addressing this need, the present study analyzes the accuracy of the analytical hierarchy process-weight product (AHP-WP), analytical hierarchy process-technique for order preference by similarity to ideal solution (AHP TOPSIS), and analytical hierarchy process-simple multi-attribute rating technique (AHP-SMART) methods in determining the most suitable JavaScript framework according to the International Organization for Standardization (ISO) 9126 classification. To evaluate accuracy, the mean absolute percentage error (MAPE) was applied as a cost function to measure the error percentage of each method. The analysis was conducted on ten popular JavaScript frameworks selected based on their popularity and usage trends. The evaluation considered six quality criteria: functionality, reliability, usability, efficiency, maintainability, and portability. The results show the ranking of each alternative for all methods. Accuracy measurement using MAPE revealed that the AHP-WP method produced the smallest error percentage (37.77645%), compared to AHP-TOPSIS (47.12566%) and AHP-SMART (46.4041%). Accordingly, the AHP-WP method is recommended for decision support system (DSS) development.
Information technology value engineering through partial adjustment valuation theory
The paper proposes a systems management approach that utilizes information technology (IT) treatment as a framework to help firms enhance future performance by optimising key parameters. The method certifies a valuation approach that enables businesses to better manage their IT infrastructure and improve performance. A case study of A case study of PT Telekomunikasi Indonesia (Telkom) and PT XL Axiata (XL) (2004–2018) shows the method’s effectiveness. Once the IT value is identified, specific parameters can be engineered to improve performance without changing other variables. The approach uses a partial adjustment valuation model, enabling performance gains at lower costs. The results show significant improvements in both firms’ performance values and ratios compared to their originals. This supports adopting a cost leadership strategy, making IT based businesses more efficient, cost-effective, and better performing across financial, business, and strategic dimensions.
Performance enhancement of embedded object detection via neural hardware acceleration
This paper presents the first benchmarking of you only look once version 11 (YOLO11) on the Rockchip RK3566 neural processing unit (NPU) within the Orange Pi 3B platform. Performance was compared between the quad-core ARM Cortex-A55 CPU and the integrated NPU using the COCO2017 dataset, evaluating latency, energy, and accuracy. NPU acceleration achieved >80% latency reduction and ≈ 94% lower per-inference energy consumption, with speedup of up to 16.7× while maintaining accuracy within 0.03 mean average precision (mAP) of the baseline. Average power remained nearly constant (3.60 W central processing unit (CPU) vs. 3.59 W NPU), indicating that the efficiency gains stem from reduced inference time rather than lower wattage. Limitations included unstable INT8 quantization due to unsupported operators and calibration-range mismatch, as well as minor CPU-side overhead in preprocessing and non-maximum suppression. The findings confirm that the RK3566 NPU delivers substantial efficiency gains without accuracy loss, enabling compact and low-cost platforms to sustain modern object-detection workloads. This demonstrates that affordable NPUs can provide reliable, real time artificial intelligence (AI) inference for embedded vision, internet of things (IoT), and robotics applications.
RWT-Net: A hybrid ResNet-wavelet-transformer for early detection of left ventricular hypertrophy
Early detection of left ventricular hypertrophy (LVH), a key predictor of heart failure and stroke, is critical. However, standard 12-lead electrocardiogram (ECG) criteria suffer from low sensitivity. While deep learning shows promise, a research gap exists for models that robustly integrate diverse signal fea tures to improve detection, especially sensitivity. We propose ResNet-wavelet transformer net (RWT-Net), a hybrid architecture that fuses deep morpholog ical features from a ResNet1D with statistical time-frequency features from a wavelet packet transform (WPT) using a transformer encoder. The model was evaluated on the PTB-XL dataset (11,201 recordings) using a stringent, patient level 5-fold cross-validation. RWT-Net achieved a mean area under the curve (AUC)of0.9868andF1-scoreof0.8725. Critically, its wavelet-enhanced stream yielded significantly higher sensitivity compared to a ResNet-transformer base line (0.8964 vs. 0.8716, p=0.0039), better addressing the clinical need to mini mize false negatives. A key limitation is the reliance on ECG-based labels, not an echocardiography gold standard. RWT-Net demonstrates potential as a re liable, automated screening tool to prioritize at-risk patients for further clinical assessment.
Dental caries detection and treatment cost prediction using YOLOv11
Dental caries is one of the most prevalent oral diseases, progressively damaging tooth structure and often leading to significant treatment costs. Variations in dental service fees across clinics can become a financial barrier, discouraging timely and appropriate care. This study introduces an artificial intelligence (AI)-based framework that utilises smartphone camera images to detect dental caries and predict treatment costs. A total of 1,200 images of carious and normal teeth were collected from dental clinics in Denpasar, Bali, Indonesia, and classified by three dental experts. Data augmentation expanded the dataset twentyfold to 23,060 images to address variation and class imbalance. The you only look once version 11 (YOLOv11) deep learning algorithm was employed for caries detection, and its performance was evaluated using mean average precision (mAP), precision, and recall metrics. The model demonstrated high accuracy, achieving an mAP of 96.1%, a precision of 95.5%, and a recall of 93.0%. This study provides the first integration of YOLOv11 with RGB-intensity-based cost prediction in digital dentistry. The proposed system offers a fast, accessible, and cost-efficient approach for early caries detection and treatment cost estimation. These findings highlight its potential to support real-time, AI-assisted preventive dentistry and contribute to more equitable access to oral healthcare.
Challenges in radar-based non-supercell tornado detection using machine learning approaches
Tornado detection in Indonesia remains challenging as most areas are monitored by single-polarization weather radar, while dual-polarization systems offer superior detection capabilities. This study presents a novel approach by applying random forest (RF) and XGBoost machine learning algorithms to detect tornadoes using single-polarization radar data, addressing a critical gap in tropical tornado monitoring where dual-pol infrastructure is limited. Four tornado cases in Surabaya during 2024 were analyzed. Radar features including reflectivity, radial velocity, vorticity, and angular momentum were extracted through a multi-elevation sliding window technique. Spatial labels were assigned based on reports from the Indonesian National Meteorological Services (BMKG) with a 7.5 km radius from the event center. The dataset was balanced using synthetic minority over sampling technique (SMOTE). Evaluation was performed using the leave one-case-out (LOCO) scheme. Within-case evaluation showed strong performance with area under the curve (AUC) >0.94 for both models. XGBoost achieved higher probability of detection (POD 0.67-0.72) but with elevated false alarm rates (FAR up to 70%). RF demonstrated more balanced performance (POD 0.61-0.65, FAR 0.34-0.35). LOCO evaluation revealed significant POD reduction and FAR increase when tested on new cases. This indicates generalization challenges due to variability in tornado characteristics. This study demonstrates the potential of machine learning for tropical tornado early detection using readily available single-polarization radar.
Technology levels in artificial intelligence robotics and industrial automation: impacts and implications
Robotics technology has progressed rapidly since its debut in 1922, evolving from simple programmable automation to highly sophisticated systems. This study employs a hybrid methodology, combining qualitative analysis of key robotic components manipulators, controllers, end effectors, and geometric configurations with quantitative comparison of performance metrics to classify robots according to their technological level (low-tech versus high tech). The findings show clear distinctions across these levels. Low-tech robots typically achieve positioning accuracy of about 0.025 mm and rely mainly on single electric motor actuation, making them suitable for simple, repetitive tasks. In contrast, high-tech robots can perform complex operations with positioning accuracy of up to 3 mm, integrating multiple actuation systems such as electric, pneumatic, and hydraulic mechanisms for enhanced flexibility and control. Moreover, high-tech robots exhibit greater manipulative capabilities and advanced control systems that enable multi axis and adaptive operations not feasible for low-tech counterparts. These results demonstrate how the technological level directly shapes a robot’s precision, actuation complexity, and functional range, providing a clear framework for selecting appropriate robotic solutions in both industrial and research settings.
SELLA: An IoT-based smart shopping trolley with real-time RFID tracking and automated checkout
The contemporary retail sector faces a persistent challenge in enhancing in store customer experience, primarily due to inefficiencies at checkout. This paper presents smart e-cart for lean logistics application (SELLA), a smart shopping trolley system engineered to eliminate this bottleneck. The system’s architecture is centered on a Raspberry Pi 4 microcontroller, orchestrating an ultra-high frequency (UHF) radio frequency identification (RFID) subsystem for instantaneous, non-line-of-sight product identification, and a responsive 7-inch touchscreen graphical user interface (GUI) developed in PyQt. The core contribution lies in developing a self contained shopping solution with integrated payment processing, supported by comprehensive performance validation. We present a detailed methodology, including the system’s multi-threaded software architecture and core operational algorithm. Experimental evaluation demonstrates a mean tag detection accuracy of 98.2% under optimal conditions, a robust user interface (UI) latency of under 500 ms, and an average central processing unit (CPU) utilization of 28%, proving system efficiency. Comparative analysis confirms that SELLA’s integration of on-trolley automated payment and detailed performance metrics represents a significant advancement over existing prototypes. The system provides a validated, high-performance solution for next-generation smart retail environments.
Advancements in physical layer key generation: a review on channel reciprocity and IoT security techniques
With the burgeoning internet of things (IoT), securing communication becomes paramount. Traditional cryptography does not meet computational needs and brute-force attacks. This review explores the state-of-the-art physical layer secret key generation (PLKG) that takes advantage of the inherent reciprocity and randomness of wireless channels. We investigate cutting-edge techniques such as feature extraction networks, domain adversarial training, and deep learning-based approaches, evaluating their effects on the security and efficiency of key generation. In addition to these methods, the review addresses real-world challenges such as multi-user scenarios, reconciliation overhead, and inconsistent channel measurement. We believe that improved key generation rates and security can be achieved through the use of millimeter wave technology and full-duplex communication. To strengthen the robustness of key generation, the paper concludes by suggesting future directions, such as incorporating more random sources, such as physiological signals and sensor data. This comprehensive overview offers deep insights into the state-of-the-art and paves the way for reliable communication in ever more complicated IoT settings.
Distorted born iterative method reconstruction in high-noise environments using KNN-based machine learning denoising
Ultrasound tomography reconstruction using the distorted born iterative method (DBIM) is sensitive to measurement noise, which degrades image fidelity and slows convergence. We propose integrating a k-nearest neighbors (KNN) denoising step within each DBIM iteration to suppress noise adaptively while preserving structural edges. Simulations with a circular cylindrical target and transmit/receive geometry (12×12) were conducted at signal-to-noise ratio (SNR) levels of 6 dB, 3 dB, and 1 dB. Compared with conventional DBIM employing Tikhonov regularization, the KNN-filtered DBIM reduces normalized reconstruction error by up to 57.2% at 1 dB and shows faster error decay over successive iterations. The method is training-free, computationally lightweight, and preserves fine structural details. These properties make KNN-filtered DBIM attractive for noisy or resource-constrained imaging environments. Future work will validate the approach on experimental data and explore adaptive K selection.
Optimized edge-aware frequency-guided filtering for robust image denoising
The problem of denoising intrusion is still of great concern in computational imaging because of the trade-off between noise reduction and image structure and details recovery. This paper proposes an optimized edge-aware fast adaptive guided filter (E-FAGF) combining wavelet-domain decomposition, edge-awareness, and lightweight deep learning for efficient and effective denoising. The biorthogonal wavelet transform is employed to decompose noisy images into low- and high-frequency sub bands and an improved edge-attention map for selective high-frequency denoising. Regularization parameters are estimated pixel-wise by a compact convolutional neural network (CNN), allowing spatial-varying filtering to be done with multi-scale processing. The resultant E-FAGF consistently outperforms the state of the art on this dataset: on BSD500 for speckle and Gaussian noise (peak signal-to-noise ratio (PSNR) of 39.63 dB and 33.97 dB, respectively), and competitive performance for Poisson noise (30.84 dB) a large margin compared to the reference bilateral and non-local means. Our method maintains high structural similarity (up to 0.97 in structural similarity index measure (SSIM)), runs at 0.015 seconds per 512×512 image on graphics processing unit (GPU), and can be applied without dataset specific training. These results suggest the possibility of E-FAGF to achieve a balance between classical efficiency and learning-based adaptability, thereby forming a new scenario to combine fast and reliable image restoration for actual scenarios.
Newchaos function from the composition of DTM and Gauss iterated map for digital image encryption
This manuscript introduces a novel chaotic discrete function, formulated through the composition of the dyadic transformation map (DTM) and the Gauss iterated map (GIM), and designated as DTGIM. The National Institute of Science and Technology (NIST) randomness test suite, bifurcation diagrams, and Lyapunov exponents are used to examine the chaotic characteristics of DTGIM. With ini tial condition x0 = 0.12345 and parameters α = −15 and β = 0.3, the func tion shows chaotic behavior in the bifurcation diagram and produces a positive Lyapunov exponent. Strong randomness is further confirmed by NIST tests, which achieve 100% for 32-bit binary sequences and 63.75% for 8-bit binary sequences. Additionally, we compare a number other chaotic discrete functions that also employ the composition method. These findings show that DTGIM is a viable option for applications involving chaos-based cryptography.
Advanced microwave imaging and artificial neural networks for early detection and localization of breast tumors
This study investigates the detection and localization of breast tumors based on dielectric property differences between cancerous and normal tissues. A microwave imaging technique integrated with artificial neural networks (ANNs) is proposed as a noninvasive alternative to conventional screening methods such as mammography and magnetic resonance imaging (MRI). A breast model with a 2.5 mm spherical tumor was designed using CST Microwave Studio. Simulation results show that the ANN achieves a detection rate close to 100%, providing negative outputs for tumor-free cases and positive outputs for cases with tumors. Additionally, ANN outputs strongly correlate with the actual tumor positions in the simulated environment. These findings suggest that microwave imaging combined with ANNs offers a cost effective, radiation-free, and patient-friendly solution for the early detection and localization of breast cancer, with promising potential for clinical translation.
A practical approach to Candi Siwa 3D reconstruction with COLMAP and Nerfstudio
We demonstrate a practical approach for large-scale object three-dimensional (3D) reconstruction with freely available frameworks, COLMAP and Nerfstudio. We performed the reconstruction of a temple named Candi Siwa, located at Prambanan Site, which is situated between Central Java and Yogyakarta Province, Indonesia. We utilized COLMAP and Nerfstudio as platforms for 3D reconstruction from images captured by an everyday smartphone. In the 3D model construction process, COLMAP generates a dense point cloud, whereas Nerfstudio generates a scene from source images. We selected 96 images of Candi Siwa to perform reconstruction using COLMAP. As a result, a 3D model for the temple with a clear structure and color was observed. A scene rendered in MP4 format was also generated using Nerfstudio. Additionally, we performed the 3D reconstruction from 150 images taken by the public and found them insufficient for constructing the object. This occurred despite the number of images being larger than those used in the previous reconstruction. The results indicate that the success of a crowdsourcing project for reconstructing a large-scale object should consider not only the number of images but also the variation in point of view and the completeness of the whole structure.
Optimized IMC with GWO algorithm and variable switching function for voltage regulation of SEPIC converter
With the growing application of single-ended primary-inductor converter (SEPIC) converters in power electronic systems, precise output voltage regulation under uncertainties and nonlinear conditions remains a significant challenge. Although internal model control (IMC) effectively addresses issues arising from unstable zeros and fixed time delays in non-minimum phase systems, its performance can degrade under large transient errors or sudden disturbances, leading to control signal saturation and instability. In this study, a modified IMC scheme is proposed, which integrates a variable switching function into the control structure. This addition enhances the robustness of the system by dynamically adapting the control effort to mitigate abrupt changes in the control signal and stabilize the output voltage. Furthermore, it prevents controller saturation during large-signal deviations, thereby improving transient response and maintaining system stability. The design parameters of the controller are optimized using the gray wolf algorithm to achieve an optimal balance between voltage overshoot, settling time, and closed-loop stability. Simulation results under various operating conditions confirm the superior performance of the proposed control method compared to conventional IMC.
Anchovy-inspired filter algorithm: A bio-inspired optimization approach for high-dimensional benchmark functions
This paper presents the anchovy-inspired filter algorithm (AFA), a novel bio-inspired metaheuristic optimization method motivated by the filter feeding behavior of anchovies. Unlike conventional swarm intelligence algorithms, AFA employs a filtering mechanism in which each agent generates multiple candidate solutions within a local sampling radius and selects the best, mimicking how anchovies filter microscopic prey from seawater. To evaluate its performance, AFA was benchmarked against particle swarm optimization (PSO) and genetic algorithm (GA) using six standard test functions: Sphere, Rosenbrock, Schwefel 1.2, Rastrigin, Griewank, and Ackley in 30-dimensional search spaces. Simulation results demonstrate that AFA consistently outperforms PSO and GA across unimodal and multimodal functions. For unimodal problems such as Sphere, Rosenbrock, and Schwefel 1.2, AFA achieved significantly lower best and mean fitness values, reflecting strong exploitation capability. For multimodal functions including Rastrigin, Griewank, and Ackley, AFA effectively avoided local minima, maintained robustness, and achieved stable convergence with lower variance. Convergence analysis further indicates that AFA steadily approaches near-global optima without premature stagnation. Overall, the results highlight the effectiveness of the filter-based exploitation mechanism in balancing exploration and exploitation. Future research will focus on adaptive filtering strategies, hybrid integration with other metaheuristics, and applications to real-world optimization problems.
System identification of batch milk cooling using output error models
Accurate modelling of milk cooling dynamics is essential to maintain product quality and improve energy efficiency in small-scale dairy operations. This study aims to develop a dynamic model for a batch milk-cooling system used at Koperasi Unit Desa Sinau Andandani Ekonomi (KUD SAE) Pujon. Synthetic temperature data were generated under controlled perturbations reflecting actual process conditions, and the data were analysed using the output error (OE) identification method implemented in the MATLAB System Identification Toolbox. Several OE model structures were compared using statistical indicators, including the coefficient of determination (R²) and root mean square error (RMSE). The OE (2,2,1) model achieved the best performance with R² = 0.9923 and RMSE = 0.0600, accurately representing the first-order dynamics of the cooling process. The identified model provides a reliable foundation for process optimisation, controller design, and operator training in dairy systems. Although the validation is limited to simulated data, the proposed approach offers substantial potential for real-time implementation and can be extended to other temperature-sensitive food processes.
A decoupling-based multivariable H∞ controller for PMSM speed and current regulation
High precision speed regulation of the permanent magnet synchronous motor (PMSM) is a critical challenge in modern industrial applications, including electric vehicles and traction systems. This task is significantly affected by external disturbances, such as variable load torque, as well as physical phenomena often neglected in analytical models, such as magnetic circuit saturation or thermal variations in electrical parameters. In this context, conventional control methods often fail to ensure both dynamic performance and robustness. This paper proposes a multivariable H∞ control strategy based on field-oriented control (FOC) and d/q decoupling to design a robust and high-performance controller. The diagonal multiple-input multiple-output (MIMO) model, linking the direct-axis voltage𝑣𝑑to the current 𝑖𝑑and the quadrature-axis voltage 𝑣𝑞to the rotational speed 𝜔𝑟, is derived directly from the decoupling principles of FOC, without relying on linearization around an operating point or modeling of parametric uncertainties. The H∞ controller is synthesized using the standard configuration, with carefully selected weighting functions to ensure dynamic performance, closed-loop stability, and effective disturbance rejection. Numerical simulations demonstrate that the proposed controller achieves accurate speed reference tracking, fine current regulation, and fast load disturbance rejection, confirming its effectiveness and robustness. This approach provides an advanced alternative to conventional control methods by fully exploiting the multivariable structure of the system.
Performance analysis of a multi-level inverter fed permanent magnet synchronous motor for electric vehicles
Electric vehicle (EV) drive systems utilizing permanent magnet synchronous motors (PMSMs) often encounter performance limitations due to switching losses, voltage stress, and harmonic distortion. To address these challenges, this paper presents a compact 31-level multilevel inverter (MLI) topology designed to enhance drive efficiency and power quality. The proposed inverter minimizes switching devices and driver circuits, resulting in reduced total harmonic distortion (THD), lower voltage stress, and improved waveform fidelity. Advanced control strategies are employed to further optimize performance. field-oriented control (FOC) ensures precise torque and flux regulation, while direct torque control (DTC) delivers rapid transient response. To mitigate torque ripple and variable switching frequency inherent in conventional DTC, adaptive predictive control (APC) is integrated to refine switching behavior and enhance dynamic stability. Simulation studies conducted in MATLAB/Simulink demonstrate the effectiveness of the proposed system, revealing significant improvements in torque smoothness, reduced THD (0.85%) and elevated efficiency under variable load conditions. This integrated solution offers a practical and scalable approach for next-generation EVs, contributing to greater reliability, energy utilization, and overall system performance.
Optimizing planar micro-transformer performance
Faced with new requirements for isolated switching power supplies with high efficiency and power density, planar transformer technology has emerged as a serious alternative to wound components. The work presented in this paper addresses the issue of developing planar transformers in the context of low-power electronics, where volume and weight constraints are paramount. The flat shape of the coils and the interlacing of the windings do not allow for control of magneto-thermal phenomena. Although scientific literature offers numerous simulation tools to aid in the design of such transformers, it must be noted that they do not allow for a rigorous account of these phenomena. In this paper, methods and a geometric and electrical sizing tool in planar technology are used for the design of flyback direct current to direct current (DC/DC) converters. Methods for dimensioning and estimating temperature rise are presented and compared in order to develop calculation tools for design purposes. This study enabled us to observe the distribution of the magnetic field, the role of ferrite, the distribution of currents and voltages in the coils, and the distribution of temperature in our device. It should be noted that conductive and convective heat transfer processes were considered in steady state.
Improved disturbance rejection of induction motor drives using PI–VGSTASM control and torque disturbance estimation
Induction motor (IM) drives often suffer performance degradation under load variations and parameter uncertainties when using conventional proportional–integral (PI)- based field-oriented control (FOC). To address these issues, this study proposes a composite control framework combining a PI regulator in the speed loop with a Lyapunov-based variable-gain super twisting algorithm (VGSTA) for the inner current loops to enhance robustness against disturbances and parameter variations. In addition, a load torque observer is developed to estimate unknown disturbances in real time and generate an equivalent compensation current, thereby improving disturbance rejection. Unlike existing approaches, the proposed strategy achieves a balance between simplicity, robustness, and smooth control by integrating classical PI control with higher-order sliding mode techniques and adaptive observer dynamics. Furthermore, the controller and observer gains are optimized using particle swarm optimization (PSO) to improve convergence and reduce overshoot under uncertain conditions. Simulation results demonstrate accurate speed regulation, effective chattering reduction, and reliable operation under load and parameter variations. Due to its low computational complexity and high robustness, the proposed method is well suited for industrial drive systems and electric mobility applications.
Optimization and techno-economic analysis of hybrid renewable systems in Nigeria
Rising electricity demand, fossil fuel depletion, and environmental concerns highlight the need for sustainable rural electrification. The Elenjere community in Kwara State, Nigeria, depends on costly diesel generation and limited grid access, creating an urgent demand for reliable and affordable alternatives. This study designs and optimizes a hybrid renewable energy system (HRES) for the community using hybrid optimization model for electric renewables (HOMER) Pro simulation. The proposed system combines photovoltaic (PV), wind turbines (WT), battery storage (BAT), inverter (INV), and a diesel generator (DG) as backup. Field data on load demand, solar radiation, and wind speed were used for realistic modeling. System performance was evaluated using levelized cost of energy (LCOE), net present cost (NPC), and system capital cost (SCC). Results show the PV/WT/BAT/INV/GEN configuration achieved the lowest LCOE of USD 0.455/kWh, an NPC of USD 2.98 million, and 86.2% renewable penetration, significantly reducing diesel use. Sensitivity analysis revealed that reducing battery costs and increasing PV capacity could lower the LCOE to USD 0.227–0.325/kWh. The study demonstrates how modest wind resources (4.19 m/s at 10 m) complement PV in low-wind regions while addressing inflation realism (25.5% discount rate, foreign exchange (FX) volatility). Future work will include dynamic control simulation and lifecycle analysis to enhance scalability and sustainability.
Optimizing blood cell classification: evaluating feature dimensionality and validation strategies
Manual blood cell classification is time consuming and may lead to inconsistent results. This study aims to assist pathologists in diagnosing hematological disorders using machine learning (ML) techniques for automated classification of blood cells in multi-color test images, distinguishing red blood cells (RBCs) and white blood cells (WBCs). Features were extracted using the InceptionV3 network, and several ML models were evaluated for classifying blood cells into eight categories. Two validation strategies: a 66%–34% train–test split and 20-fold cross-validation were applied. The effect of dimensionality reduction through principal component analysis (PCA) was also examined, reducing the feature space from 2,048 to 100 components. Among all models, support vector machine (SVM) achieved highest performance, with 93.4% accuracy and an area under the curve (AUC) of 0.996 without PCA, and 90.1% accuracy with an AUC of 0.991 after PCA. Although PCA slightly reduced accuracy, it improved computational efficiency. Overall, SVM provided the most accurate, stable, and generalizable classification results for automated blood cell analysis.