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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Network traffic analysis and bandwidth forecasting for using Meta’s Prophet: a case study
This study created a forward-looking bandwidth prediction system for students’ halls of residence at Landmark University. The system uses Meta’s Prophet, a method for analyzing patterns in data over time, and was trained on past internet traffic data from October to December 2024. The system was able to predict future bandwidth usage with over 90% accuracy. To assess how well the system worked, several common metrics were used, including mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The MAE was calculated as 10,099,863.10 bits per second (bps), and the RMSE was 13,570,959.58 bps. While the mean squared error (MSE) appears large numerically, this is anticipated due to the size of the bandwidth data involved in its calculation. Importantly, the prediction errors are considered reasonable when considered in relation to the actual peak bandwidth usage, which fluctuated between 47 and 50 megabits per second (Mbps). These findings suggest that machine learning can be a valuable tool for refining network infrastructure and improving the user experience quality of service (QoS) in environments with many users, such as university residences.
Hybrid GA–SA optimization for eMBB-oriented spectrum allocation in 5G device-to-device communication
The explosive growth of enhanced mobile broadband (eMBB) services in fifth generation (5G) networks presents new challenges in maintaining quality of service (QoS), particularly under dense deployments with device-to-device (D2D) communication. Interference caused by spectrum reuse among D2D pairs and cellular users can significantly degrade signal-to-interference-plus-noise ratio or signal-to-interference-plus-noise ratio (SINR), throughput, and fairness. This paper addresses the underexplored problem of optimizing spectrum allocation in eMBB-specific D2D scenarios by proposing a hybrid metaheuristic framework combining genetic algorithm (GA) and simulated annealing (SA). The proposed hybrid GA–SA algorithm leverages GA’s global exploration and SA’s local exploitation to improve allocation quality while ensuring robustness. Simulation results reveal that the hybrid approach achieves up to 25% improvement in SINR, an 18% increase in aggregate throughput, and a 22% reduction in interference compared to standalone GA and SA algorithms. Furthermore, the framework achieves improved fairness performance while maintaining competitive SINR and throughput under dense eMBB-oriented deployment scenarios. The algorithm demonstrates efficient convergence behavior and scalability to larger user populations, making it suitable for real-time or large-scale deployments. These results affirm the significance of tailored hybrid optimization in interference-aware spectrum management for future 5G networks.
Lightweight SDN/NFV-based framework for dynamic data-flow and network slice adaptation
The increasing demand for responsive and reliable network services in next-generation communication systems has intensified the need for dynamic resource management and quality of service (QoS) assurance. Software-defined networking (SDN) and network function virtualization (NFV) provide programmability and flexibility for modern networks. However, practical platforms that demonstrate real-time adaptive behavior remain limited. This study differs from prior simulation-focused work by demonstrating real-time adaptive slice control in a reproducible container-based SDN/NFV emulation environment. A bottleneck-aware slice controller is developed to classify degradations as network-limited, server-limited, or service failure using joint indicators, and to select rerouting or service migration using stability constraints and a lightweight action-cost model. Experimental results show that throughput is restored to above 90% of nominal capacity. Recovery typically occurs within two to three control iterations. Service continuity is maintained with low control-plane overhead. The work provides a reproducible experimental baseline and a decision mechanism that reduces incorrect reroutes/migrations under ambiguous key performance indicator (KPI) drops.
Stochastic Resonance-Aided Energy Detection for RF-Powered Cognitive Radio Networks
Conventional stochastic resonance (SR) techniques often face challenges with higher-frequency signals and parameter optimization for real-time applications, as observed in practical orthogonal frequency-division multiplexing (OFDM) systems that are vulnerable to noise uncertainty (NU). In this study, we present a novel SR-aided energy detection (ED) method that incorporates multi-taper spectrum estimation technique to improve spectrum estimation precision and Gauss-Seidel-like iteration method to accurately adjust the SR parameters for real-time adaptation. This combined strategy enhances weak signal detection, prevents signal distortion, and increases robustness against fluctuating noise conditions. Results from 5,000 Monte Carlo simulations showed that, at 0 dB NU, SR-aided ED attained 90% detection probability at -11 dB, outperforming conventional ED with an SNR gain of 12.5 dB. At 3 dB NU, the conventional ED accuracy degraded by 5.5 dB, resulting in a false alarm probability of 77%, while SR-aided ED demonstrated robustness to NU. At 10 dB NU, ED failed to distinguish the differences between noise and signal power, giving rise to 99% false alarm probability. In contrast, despite a 6 dB degradation, the developed SR-aided ED approach still guarantees a 1% false alarm probability. In clipping-prone systems, conventional ED is vulnerable to signal clipping. Conversely, SR-aided ED remains unaffected.
High-efficiency Doherty amplifier with metamaterial harmonic control for sub-6 GHz 5G base station
This paper presents a broadband Doherty power amplifier (DPA) for Sub-6 GHz 5G wireless applications, integrating a metamaterial-based harmonic control circuit to suppress the 2nd and 3rd harmonics. The design employs a CGH40010F transistor on a Rogers RO3004C substrate. All results are obtained from simulations, while the resonator has been experimentally validated. The proposed approach enhances drain efficiency under output back-off (OBO) conditions without compromising linearity. Simulation results show a gain of 12.2–16.18 dB, with drain efficiency from 50.42 % to 79.83 %. Efficiency remains high at 3 dB and 6 dB back-off, ranging from 50.8–75.6 % and 36–62.8 %, respectively. These findings demonstrate the DPA’s potential for next-generation base stations requiring compact, efficient, and wideband amplifiers, offering a practical solution for modern wireless communication systems. The novelty lies in combining broadband DPA design with harmonic suppression via metamaterials, providing improved efficiency and spectral performance.
A system modeling approach for business intelligence system design in the Indonesian kite string industry
Kite strings in Indonesia are crafted by local artisans who have learned from their predecessors, but the industry struggles to deliver quality products that meet customer standards. A Systematic approach to this problem could involve designing a business intelligence (BI) system to improve decision-making. Based on the main premises of the kite string industry, this paper focuses on developing a causal loop diagram (CLD) model as a first step toward designing an effective BI system for the kite string industry. The CLD model captures the relationships between core variables and brain areas, offering crucial insights into customer perceptions, production quality, and market dynamics. Based on data up to October 2023, the study uses a systematic approach to pinpoint the feedback loops enabling product quality and business performance. This framework can be used as a strategic approach to assist in decision-making, refine operational processes, and enhance overall product quality. This research provides a new perspective to the literature by combining systems thinking into BI system design under the context of traditional industries as an elaboration of findings aimed to gain a competitive advantage in Indonesian kite string business practices.
Neural network approaches for quality-of-service optimization in software-defined networking environments
Software-defined networking (SDN) enables centralized and programmable control of network behavior; however, conventional routing strategies remain largely reactive and struggle to adapt to rapidly changing traffic dynamics. To address this limitation, this study proposes a learning-based SDN routing framework that integrates a long short-term memory (LSTM) model to predict traffic patterns and proactively optimize routing decisions. The proposed approach is implemented and evaluated in an SDN testbed using realistic traffic scenarios. Experimental results are averaged over multiple independent runs to ensure robustness and reproducibility. Compared with static shortest-path routing and classical machine learning (ML) baselines, the proposed model demonstrates consistent improvements in latency, packet loss, and throughput under the evaluated conditions. In particular, the ablation study reports a 95% confidence interval for end-to-end latency ranging from 51.8 to 55.6 ms, confirming the statistical stability of the observed gains. Additional analyses show that the framework maintains low inference latency and modest control overhead, making it suitable for real-time SDN environments. Overall, the findings indicate that temporal learning models can effectively enhance SDN routing performance when evaluated within controlled experimental settings, offering a practical pathway toward more adaptive and intelligent network control.
Design and development of WIKIN: an interactive nuclear community website for Indonesia using Laravel framework
Despite its significant contributions to health, agriculture, and energy, the public perception of nuclear technology in Indonesia remains cautious and fragmented. Existing communication channels are largely one-way and regulatory, offering limited opportunities for public interaction and collaborative learning. This study investigates how an interactive web-based platform can enhance public engagement and knowledge sharing in nuclear science and technology. To address this challenge, a Nuclear Community Interactive Website (WIKIN) for Indonesia was designed and developed using the Laravel framework, following a structured waterfall methodology. The system integrates role-based access control, modular architecture, and responsive design to support community participation through the sharing of news, discussions, and documentation of service activities. The evaluation was conducted through black-box functional testing of 27 features (all passed) and a system usability scale (SUS) survey involving 51 users, which produced an average score of 74.8 (“Good”), indicating satisfactory usability and acceptance. These results demonstrate that WIKIN provides an effective model for fostering two-way communication, improving transparency, and strengthening public literacy regarding nuclear issues. This study contributes to digital public engagement research by demonstrating how user-centered design principles can be effectively applied to enhance trust, transparency, and community participation in nuclear science communication.
Real-time flood forecasting with attention-enhanced hybrid deep learning using internet of things data
Floods are a frequent disaster in Semarang city, Indonesia, requiring an accurate and real-time forecasting system to support effective risk management. This study introduces a hybrid long short-term memory-gated recurrent unit (LSTM-GRU) model with an attention mechanism (attention-enhanced LSTM-GRU) designed to improve the accuracy of flood predictions based on multiparameter internet of things (IoT) data. The novelty of this study lies in the integration of the attention mechanism within the hybrid LSTM-GRU architecture, which allows the model to provide adaptive focus on features and time periods that most influence flood occurrences. The dataset used consists of 1,736 time series samples covering rainfall and water level data collected every 15 minutes from IoT sensors in the upstream and downstream areas of Semarang, Indonesia. Experimental results show that the hybrid model with the attention mechanism provides the best performance with a mean absolute percentage error (MAPE) value of 1.4%, root mean squared error (RMSE) of 1.05, and coefficient of determination (R²) reaching 0.96. This model also achieves 100% recall for the “Danger” class, demonstrating its reliability in detecting critical conditions. The practical implication of this research is the availability of a flood prediction model that is accurate, adaptive, and can be directly applied to IoT-based early warning systems in flood-prone urban areas.
TikTok store affiliate performance sentiment analysis using support vector machine and gradient boosting machine methods
The development of social media-based e-commerce, particularly, opens new opportunities for digital affiliate systems. This study examines public perception of affiliate performance through comment sentiment analysis (positive, negative, neutral) using support vector machine (SVM) and gradient boosting machine (GBM). Data was collected from TikTok Shop comments, processed through text preprocessing, manual labeling, and then analyzed using Python. Evaluation using accuracy, precision, recall, and F1-score metrics showed that the combination of the synthetic minority oversampling technique (SMOTE) with SVM and GBM improved classification performance, although negative sentiment remained challenging. SVM achieved the highest accuracy (84%) with a ratio of 90:10, while GBM excelled in detecting neutral sentiment (F1 0.91). These findings are useful for sentiment-based marketing strategies and natural language processing (NLP) development for Indonesian-language texts on TikTok Shop.
Classification of premature cardiac contractions based on RFECV and ensemble learning
Premature cardiac contractions, including premature atrial contractions (PACs) and premature ventricular contractions (PVCs), are common arrhythmias that may increase the risk of cardiovascular complications when they occur frequently. Accurate classification of these events from electrocardiogram (ECG) signals remains challenging due to noise and signal variability. This study proposes a machine learning–based classification framework that combines recursive feature elimination with cross-validation for feature selection and an ensemble learning strategy to improve classification robustness. The approach was evaluated using the Massachusetts Institute of Technology – Beth Israel Hospital (MIT-BIH) Arrhythmia database and achieved high classification performance, with an accuracy of 95.34%, F1-score of 92.11%, and balanced precision and recall for PVC and PAC. In addition, SHapley Additive exPlanations (SHAP) were employed to identify the most influential features, enhancing model interpretability. The results demonstrate that the proposed framework provides a reliable and interpretable solution for distinguishing premature cardiac contractions, highlighting its potential application in clinical decision support systems.
Development of a recommendation system for selecting a formula in cataract surgery
Accurate intraocular lens (IOL) power calculation remains a critical factor for achieving optimal refractive outcomes in cataract surgery. This study analyzes existing methods and software solutions for selecting formulas used to calculate IOL power. To solve this problem, a support medical decision-making recommendation system (SMDRS) was developed to analyze patient biometric data and predict the most suitable calculation formula. Among the evaluated machine learning approaches, the random forest (RF) algorithm demonstrated the highest stability and classification accuracy, leading to its selection as the core predictive engine. The system was validated using retrospective clinical data and evaluated in a functioning ophthalmology clinic. Performance evaluation demonstrated that the system increased the success rate of surgical outcomes in complex cases from 73.5% to 90.5%, thereby confirming its impact on improving the efficiency of optical calculations in clinical practice. By minimizing human error and standardizing decision-making, the proposed solution offers a robust tool for ensuring consistently superior surgical results.
Transparent insights: explainable AI with machine learning classifiers for early stage of depression classification
Depression is a widespread mental health condition characterized by enduring feelings of persistent sadness, loss of interest, and impaired daily functioning. Untreated depression can result in significant implications, such as academic failure, social isolation, and even suicide. This study presents a machine learning (ML)–based framework for classifying depression severity among university students using the Zahir depression scale dataset, comprising 478 responses categorized into mild, moderate, severe, and profound depression. In order to address the issue of class imbalance, we utilized the synthetic minority over sampling technique (SMOTE) on the dataset. In addition, seven different ML algorithms are employed to classify the severity of depression, and each algorithm’s efficiency is determined by four performance evaluation metrics. Among the applied ML classifiers, extra tree classifier outperformed with an average accuracy of 97.85% and 95.75% precision, 95.76% recall, and 95.75% F1-score. To enhance interpretability, the shapley additive explanations (SHAP) method was integrated to identify influential features, providing transparency and insight into the model’s decision process. The proposed framework demonstrates that combining explainable artificial intelligence (XAI) with traditional ML can support healthcare professionals in early depression screening and data driven mental health interventions.
Leveraging artificial intelligence for detection of denial-of service attacks in 5G network environments
This research introduces an evaluation methodology that addresses the data leakage problem for detecting denial-of-service attacks in fifth-generation (5G) network slicing environments, and applies it to perform a benchmark comparison among twelve machine learning (ML), deep learning (DL), and probabilistic models using a publicly available 5G network slicing dataset for DoS/DDoS attacks. This methodology strictly enforces the execution of all preprocessing steps exclusively on the training data, where feature selection is performed using the mutual information (MI) metric, values are standardised via the z-score method, and synthetic samples are produced through the synthetic minority oversampling technique (SMOTE) technique on the training set only, with MI recalculated independently within each cross-validation (CV) cycle. Nine features out of eighty-four were retained at the elbow point where MI reached 0.51 or above. On the held-out test set containing approximately eighty percent benign data and twenty percent attack data, the convolutional neural network (CNN) model achieved the highest F1 value of 0.983 with a false discovery rate of 0.027, while the random forest model reached an F1 value of 0.968 at a considerably lower computational cost. All results remain tied to this particular dataset, and their generalisability to real-world 5G network traffic has not yet been validated.
Machine learning and deep learning for ransomware detection via feature decontamination
The continuous escalation of ransomware attacks poses a severe risk to network infrastructure and data integrity, highlighting the urgent requirement for dependable detection systems. This paper presents a comparative analysis of deep learning (DL) and machine learning (ML) techniques for identifying ransomware traffic using the UNSW-NB15 dataset. A significant obstacle in many intrusion detection investigations is feature contamination, where specific attributes inadvertently leak label data or reflect post-incident statistics, resulting in inflated and overly optimistic performance evaluations. To mitigate this concern, a feature decontamination protocol is implemented to isolate 29 reliable attributes, followed by the application of the synthetic minority over-sampling technique (SMOTE) to address the issue of class imbalance. Empirical results demonstrate that the random forest (RF) model achieves superior performance, reaching an accuracy of 0.9027 and a recall of 0.9507. Among the DL candidates, the multi-layer perceptron (MLP) delivers the most competitive outcomes with an accuracy of 0.8859 and an F1-score of 0.8996. These results suggest that ensemble-based ML frameworks offer more effective and computationally efficient ransomware detection when applied to decontaminated tabular datasets.
Asegmentation based optical character recognition system for Bangla printed text
Bangla ranks as the fifth most spoken language globally, catalyzing significant interest in the development of Bangla optical character recognition (OCR) sys tems. The intricate structure of the Bangla script, including compound char acters, modifiers, and headlines, complicates the formation of words. This research introduces a complete OCR system pipeline for printed Bangla text. It employs a thinning-based segmentation approach combined with a convolu tional neural network (CNN) to recognize Bangla fonts. Additionally, a part of speech (POS)-aware spell checker is proposed that automatically corrects mis spelled words while considering their context within the sentence. We intro duce semi-generalized filters that adapt to new fonts, addressing conjunct for mation challenges in Bangla OCR. This flexible design allows for adaptation to new fonts. The ResNet50 model is utilized to accurately recognize segmented characters and modifiers. We achieve a character segmentation error of 3.354% and an overall segmentation error of 2.332%. The ResNet50 recognition model achieves an accuracy of 98.345%.
Efficient IoT-based smart irrigation system using LoRaWAN for resource optimization in agriculture
The global agricultural sector faces critical challenges such as climate change, water scarcity, and inefficient irrigation practices. This paper presents an internet of things (IoT)-based smart irrigation system designed to optimize water usage and enhance agricultural productivity in Tunisia’s semi-arid regions. The proposed system integrates sensors (YL-69 soil moisture, DHT22 temperature-humidity, FC-37 rain), a STM32L072Z LRWAN1 board, and long-range wide area network (LoRaWAN) communication to transmit real-time data to a ThingPark server and MongoDB database. A mobile application developed in Flutter enables monitoring and control through manual, automated, and event-driven modes. Experimental validation demonstrates water savings and improved irrigation efficiency compared to conventional systems. Quantitative results, benchmarking, and cost-benefit analysis confirm the system’s affordability, energy sustainability, and scalability. This solution contributes to sustainable agriculture in resource-constrained environments.
Enhancing energy efficiency in wireless mesh networks through time-synchronized sleep scheduling and low-power hardware
Energy efficiency remains a critical challenge in wireless mesh networks (WMNs), particularly for internet of things (IoT) deployments with battery powered nodes and multihop communication. This paper proposes a time synchronized sleep scheduling framework that integrates a lightweight regression-based time synchronization model with low-power hardware to reduce energy consumption in long range (LoRa)-based WMNs. The proposed mechanism aligns local node clocks with a global reference using slope and offset correction, enabling synchronized active and sleep states across nodes. This coordination significantly reduces idle listening and unnecessary radio-on time. The proposed approach is validated through real world experiments on a multihop LoRa mesh testbed with up to three hops. Results show a substantial improvement in energy efficiency, reducing cumulative energy consumption from 125.31 mWh to 28.18 mWh over 10 hours (77.5% reduction). The sleep-mode current is reduced to 0.01 mA, demonstrating effective duty cycling. Furthermore, the approach maintains stable routing, bounded latency, and high packet delivery ratio (PDR). These findings confirm that accurate time synchronization is a key enabler for energy-efficient and reliable multihop communication, providing a practical solution to extend the operational lifetime of IoT-based WMNs.
Smart classroom 4.0 using embedded systems for attendance, energy monitoring, and environmental control
The increasing demand for digitalization and energy efficiency in vocational education has encouraged the development of intelligent classroom systems. This study proposes an integrated smart classroom 4.0 system based on the Internet of things (IoT) and embedded systems to improve attendance management, electrical safety, energy efficiency, and environmental monitoring. The proposed system integrates smart attendance, power monitoring, automatic lighting control, and environmental sensing into a unified architecture using ESP32 microcontrollers and a Raspberry Pi embedded server. Attendance is automated using radio frequency identification (RFID), fingerprint recognition, and non-contact body temperature measurement, achieving an average accuracy of 92.5% with a system latency of 1.1–1.4 s. Electrical monitoring using the PZEM-004T sensor shows zero error for voltage and current measurements and a maximum power measurement error of 0.31%, while overload and abnormal voltage conditions are successfully handled through automatic protection. Automatic lighting control based on YOLOv5s image processing achieves approximately 90% detection accuracy under high occupancy conditions. All subsystems communicate via the message queuing telemetry transport (MQTT) protocol and are visualized through a real-time web dashboard. The results demonstrate that the proposed system provides a low-cost, scalable, and reliable solution for energy-efficient and intelligent classroom management in vocational education environments.
Arobust blind signcryption scheme for secure internet of drones communication
The rapid deployment of the internet of drones (IoD) exposes aerial networks to authentication failures, eavesdropping, data theft, and impersonation attacks due to open wireless communication. This paper presents a lightweight identity based blind signcryption scheme for secure IoD communication. The scheme leverages hyper-elliptic curve cryptography to provide strong security with reduced computational overhead, making it suitable for resource-constrained drones. The blind signcryption mechanism enhances privacy by preventing the signer from accessing message content. Informal analysis shows that the scheme achieves confidentiality, authentication, anonymity, forward secrecy, and resis tance to common protocol-level attacks. Formal verification using the Scyther tool confirms secrecy, agreement, and authentication properties under a stan dard symbolic adversary model. Analytical and simulation-based evaluations demonstrate average reductions of 59.60% in computational cost, 48.86% in communication overhead, and 55.75% in energy consumption compared with existing schemes. While the results confirm protocol-level efficiency, real-world implementation and testbed validation remain future work.
Enhancing image security through nonlinear preprocessing and double random phase encoding using fractional fourier transform
Image encryption is crucial for secure data transmission in fields such as IoT, medical imaging, and biometrics. This paper proposes an enhanced encryption framework that combines nonlinear preprocessing with double random phase encoding (DRPE) using the fractional fourier transform (FrFT). The diffusion process replaces the conventional XOR operation with a nonlinear hyperbolic tangent (tanh) function, improving confusion diffusion complexity and resistance to cryptanalytic attacks. Experimental results show a reduction in peak signal-to-noise ratio (PSNR) from 9.03 dB to 7.25 dB and a mean squared error (MSE) increase to 10×10³, indicating stronger encryption and lower correlation with the original image. The proposed method also enhances robustness against histogram and key sensitivity attacks. Statistical analyses, including entropy and number of pixels change rate (NPCR) metrics, demonstrate that the approach outperforms conventional DRPE methods while maintaining computational efficiency. This hybrid nonlinear and FrFT-based framework provides a practical and scalable solution for secure image transmission in sensitive and real-time applications.
Low-cost environmental chamber for battery calendar aging under tropical conditions: design and validation
This study presents a low-cost environmental chamber designed to replicate tropical temperature and humidity conditions for calendar-aging studies of LiFePO₄ lithium iron phosphate (LFP) cells. The system integrates passive insulation with an Arduino-based active control system for real-time monitoring. The design’s novelty lies in its cost-effective ability to maintain tropical-specific profiles, validated against Meteorology, Climatology, and Geophysics Agency (BMKG) meteorological data to ensure correlation with diurnal cycles. Experimental results demonstrate stable operation for 13 consecutive days, with an average temperature of 29.47 °C and a stability metric of ±0.61 °C, keeping 100% of data within the 25–35 °C target. Although relative humidity (RH) showed an average of 76.52%, its stability was quantified by a 96.98% success rate in maintaining the 70–80% target range. The chamber’s suitability for long-term degradation studies was confirmed via a one-month calendar-aging test on a 15 Ah LFP cell, where tha battery capacity decreased from 100% to 99.58%. These results demonstrate that the proposed chamber reliably maintains tropical environmental conditions and is suitable for long-term, low-cost battery aging studies.
Interdigitized capacitive humidity sensor using FR4 printed circuit board material without sensitive layer
Humidity sensors are widely used across numerous applications, including agriculture, health, industry, environmental monitoring, and high-risk environments such as chip fabrication rooms. Interdigital capacitors (IDCs) are among several humidity-sensing designs that have been developed, and they are attractive due to simple fabrication and easy integration with electronic systems. We developed an IDC humidity sensor on a glass-epoxy substrate (FR4) using a standard commercial printed circuit board (PCB) production process. Without additional sensitive layer, the sensor production is simple. The sensor design was explored by varying electrode width. Calibration was performed in a self-constructed chamber over a relative humidity range from 15% to 90%. The IDC sensor was benchmarked against a standard humidity measurement instrument. The IDC capacitance showed a linear correlation with relative humidity, with a correlation coefficient of at least 0.98. With the finest track and spacing size of 0.254 mm achievable via commercial PCB manufacturing and a sensor footprint of 10×10 mm, the capacitance falls in the pF range, compatible with most available electronic devices. The sensitivity of the developed sensors is 2.361×10-4, 2.361×10-4, 2.361×10-4 picofarads for a track width of 0.254 mm, 0.3048 mm and 0.3556 mm respectively. These results indicate that mass production of IDC humidity sensors could be achieved at a low cost. The combination of small feature sizes and a compact sensor footprint support practical integration into existing electronic systems.
Layered virtual-leader DMPC with SQP for scalable V formation tracking of omni-robots in cluttered maps
This paper proposes a hierarchical virtual leader based distributed model predictive control (DMPC) framework for V-formation control of omnidirectional mobile robots in static obstacle environments. The physical leader follows a predesigned reference trajectory, while the followers maintain the desired formation through distributed optimization. A layered communication topology is established, where only a subset of robots receives the leader’s predicted states and acts as virtual leaders for downstream followers. Each robot independently optimizes its control sequence using local neighbor information, enabling fully distributed coordination without centralized synchronization. The unified cost function considers formation maintenance, leader or virtual leader tracking, obstacle avoidance, and control effort. Static obstacles are represented on a grid map to ensure collision-free motion. Simulation results demonstrate that the proposed framework achieves accurate formation keeping, smooth trajectory tracking, and effective obstacle avoidance. The hierarchical virtual-leader architecture enhances scalability, coordination efficiency, and robustness for multi-robot formation systems.
A study of constrained Bézier fitting curve with tangent continuity for quadruped walking robot gaits
Smooth and stable gait generation is critical for quadruped robots operating in unstructured environments. This paper introduces a constrained Bézier fitting framework. It enforces tangent continuity (𝐶1 continuity) at the gait cycle junction. This continuity addresses the tangential discontinuities that commonly arise in unconstrained Bézier trajectories. The method formulates foot-trajectory design as a constrained least-squares problem solved via Lagrange multipliers, enabling the control points to simultaneously satisfy interpolation targets and matched-tangent conditions. The resulting curves retain the geometric flexibility of classical Bézier parametrizations while producing well-behaved velocity profiles suitable for legged locomotion. These trajectories are integrated into an impedance-controlled leg model and evaluated in the MuJoCo simulator. Simulation results indicate noticeable reductions in torque spikes and improvements in tracking accuracy when compared to unconstrained Bézier and spline baselines, with representative trials showing reductions on the order of 40% and tracking improvements of approximately 25%. The proposed approach combines mathematical rigor with practical applicability, providing an efficient and reliable solution for high-performance quadruped gait planning.
Real-time classification of Pasaman oranges using Mamdani fuzzy inference system and ESP32 microcontroller
Manual classification of Pasaman oranges based on visual assessment of size and color often produces inconsistent results due to human subjectivity. This study develops an automatic classification system using a Mamdani fuzzy inference system (FIS) implemented on an ESP32 microcontroller. Fruit diameter is measured using a high-frequency sound wave ranging module (HC-SR04) ultrasonic sensor, while surface color is detected using a TAOS color sensor 3200 (TCS3200) color sensor. The obtained data are processed through fuzzification, inference, and defuzzification to classify oranges into three quality grades (A, B, and C). System performance evaluation shows strong agreement between the developed system and matrix laboratory (MATLAB) simulation, with a coefficient of determination (R²) value of 0.9855, indicating reliable and consistent classification performance for automated agricultural grading applications.
Experimental modeling of a square planar micro-coil
This study focuses on developing miniaturized electromagnetic components for applications in integrated microsystems, magnetic sensors, contactless energy transfer, and compact power converters. In a context of miniaturization and the pursuit of greater energy efficiency, the planar micro-coil plays a key role due to its compact size, ease of integration, and compatibility with microfabrication technologies. The work presented explores the design, modeling, fabrication, and experimental characterization of a square planar micro-coil. Its electromagnetic properties, such as inductance, resistance, and quality factor, are highly dependent on factors like the coil’s geometry (turns, track width, spacing, conductor thickness), substrate characteristics, and the manufacturing process. The main goal is to propose an accurate experimental model linking geometric parameters to electrical performance. The approach combines analytical modeling, finite element modeling (FEM), and experimental measurements on microfabricated prototypes. The results show strong agreement between theoretical predictions and experimental data, validating the model’s relevance. Lastly, the study highlights the impact of skin effects, proximity effects, and ohmic losses on the coil’s performance, offering insights for optimizing its design for high-performance, compact electromagnetic devices.