TELKOMNIKA (Telecommunication Computing Electronics and Control)
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Improved channel quality indicator estimation using extended Kalman filter in LTE networks under diverse mobility models
Full text linkAccurate 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
Temperature response analysis between PD and PI controls applied to infant incubators
Full text linkPremature infants, born with low birth weight, require specialized care and isolation due to their vulnerability to infections in public settings. Baby incubators, classified as life support equipment, play a crucial role in safeguarding these infants by maintaining a consistent temperature and humidity similar to the mother’s womb. This study compares the temperature control systems in baby incubators, specifically proportional and derivative (PD) control versus proportional and integral (PI) control. LM35 and DS18B20 sensors were employed in the study. Results from PD control using the LM35 sensor show a rise time of 5 min and 40 sec, a settling time of 25 min, and an overshoot of 2.2 °C. The DS18B20 digital sensor, under PD control, achieves a rise time in 6 min and 30 sec, a settling time of 23 min, with an overshoot of 1.2 °C. For PI control with the LM35 sensor, there’s a 3 °C overshoot, a 5-minute rise time, and a 30-minute settling time. The DS18B20 sensor under PI control exhibits a 2.7 °C overshoot, a 5-minute rise time, and a 29-minute settling time. PD control demonstrates lower overshoot and faster response but longer rise times than PI control. Future research explores fuzzy control systems and proportional integral derivative (PID)-fuzzy hybrid control
Particle swarm optimization-optimized integrator backstepping for the control of electric wheelchairs velocity
Full text linkMost people suffering from temporary or permanent disabilities rely on wheelchairs or electric powered wheelchairs (EPW) to maintain autonomy of movement. To address different EPW control challenges, several studies have investigated this kind of robot. This paper focuses on the optimization of the integrator backstepping control parameters of the EPW. The system operates using two permanent magnet synchronous motors (PMSM), noted for their great efficiency, substantial torque, minimal noise, and robustness. At first, the dynamic model for both EPW-motors is showned. After that, a nonlinear integrator backstepping command based on Lyapunov’s second technique, which combines the choice of the energy function with the control laws, was applied to the resulting global model. To ensure optimal performance, the control parameters were tuned by means of an optimization approach. Specifically, the particle swarm optimization technique (PSO) was employed to search for the optimal parameters (gains) of the integrator backstepping controller. In order to assess the performance of the optimized backstepping–based control approach, numerical simulations were conducted to illustrate the evolution of both electrical and mechanical velocity- related variables
Decision support system in machine learning models for a face recognition-based attendance system
Full text linkThis research aims to develop a predictive model using face recognition-based attendance data and integrating decision support system (DSS) theory with machine learning (ML) techniques to identify high-performing teachers at vocational high schools (SMKs). The novelty of this research lies in integrating theory with the use of face recognition data and ML algorithms to predict and identify high-performing teachers, thereby enhancing decision-making processes and teacher performance management in SMK schools. The dataset consists of SMK teachers' attendance data obtained through a face recognition attendance system, totaling 998 entries. This research employs sensitivity analysis concepts from DSS theory and classification approaches from ML models utilizing support vector machine (SVM), decision trees (DT), and random forest (RF). The models are trained and tested on Google Colab using Python, with data distribution guided by the Pareto principle. The research findings indicate that integrating DSS theory with ML contributes to innovation and benefits in improving decision-making and teacher performance management by successfully predicting high-performing teachers. Evaluation results show the highest accuracy rate of 98% with the RF model, making it the best predictive model compared to the other two models
Power system frequency control: instantaneous discrete testing for numerical relay using wavelet transform
Full text linkWith today’s advanced technology and rapidly growing energy demands, the reliability of electrical power systems has reached an important level. With extensive monitoring and protection, system issues like voltage drops, power irregularities, and frequency variations can have destructive consequences on the power network. Therefore, as frequency relays play a critical role in protecting power generators and load equipment from power frequency shifts, relays have evolved from electromechanical to solid-state devices with ongoing optimization to handle integrated modern networks. Traditional numerical relays use Fourier transform to identify frequency changes, which necessitates numerous data samples and has limitations with transient waveform data. To address these challenges, this work proposes a new relay algorithm based on instantaneous discrete testing and wavelet transform for frequency analysis, aimed at enhancing relay performance. This new approach demonstrates promising advantages, including significant reductions in data sample requirements, compilation complexity, decision-making time, and improved handling of transient waveforms
Reversible data hiding with selective bits difference expansion and modulus function
Full text linkThe integration of the internet of things (IoT) has significantly enhanced human life but also raises concerns about information security and privacy. Information security can be achieved through cryptography, which encrypts data to make it unreadable, or steganography, which hides data within other media. For sensitive media, such as military, medical, and forensic imaging, specialized techniques like reversible data hiding (RDH) are necessary to ensure the media can be fully restored after data extraction. Many researchers have proposed improvements to the RDH method in recent years. In this study, we propose an improved RDH method utilizing difference expansion and a modulus function. The method embeds data into the 4-bit, 3-bit, and 2-bit least significant bits (LSB) of the difference value of pixels, with a range of -2 and 2. The experimental findings demonstrate that our approach achieves a embedding capacity of 0.2507 bpp with 55.445 dB of peak signal-to-noise ratio (PSNR) for common images and 0.3849 bpp with 54.6810 dB of PSNR for medical images, using 2-bit difference values. The results demonstrate that our approach surpasses previous methods and holds promise for practical applications in IoT systems and the medical field, where secure and reversible data embedding is essential
Performance optimization of MIMO-NOMA systems in Nakagami-m fading environments
Full text linkIn this context, the utilization of multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) technologies is applied to improve wireless communication. This paper is dedicated to the evaluation of the performance in the MIMO-NOMA system under Nakagami-m fading environments, which is a more general scenario for different kinds of fading conditions that occur normally. Our proposed framework is applied to evaluate key performance metrics, including bit error rate (BER), outage probability, spectral efficiency, and ergodic capacity. The results reveal the deep impact of Nakagami-m fading on these key performance metrics, emphasizing an intricate balance between reliability and spectral efficiency that is achieved through power domain multiplexing in conjunction with successive interference cancellation (SIC). Our results are further evidence of the strength and flexibility of MIMO-NOMA, and point to insights and practical guidelines that are new towards the optimization of next-generation wireless networks. This overall analysis not only closes the gap in current literature on the subject but also sets a new benchmark for future research on advanced communication technologies
Integration of image processing with 6-degrees-of-freedom robotic arm for advanced automation
Full text linkThis paper presents the design, construction, and development of a 6-degrees-of-freedom robotic arm, specifically tailored to the conditions at our university. The arm is powered by stepper motors and controlled via a programmable logic controller, while utilizing image processing data from a Raspberry Pi board. The objective of this research is to study automated pick-and-place operations, specifically targeting the handling of fruits such as oranges and apples. The system integrates advanced motion control techniques with vision-based object recognition to enable precise and reliable manipulation of the fruits. The robotic arm is equipped with an end-effector capable of handling objects with varying shapes and sizes, ensuring safe and efficient grasping and placement. Image processing algorithms are employed to identify and localize the fruits in real time, allowing the robotic arm to perform tasks in dynamic environments with minimal human intervention. Calibration, motion planning, and feedback control strategies are optimized to ensure high accuracy and prevent collisions or damage to the fruits. The system’s performance is evaluated through a series of experiments that demonstrate its capability to effectively pick and place oranges and apples, making it a promising solution for applications in agricultural automation and food processing
The use of dolomite to overcome grounding resistance in acidic swamp land
Full text linkThis research addresses the effectiveness of grounding systems in acidic swampland, which poses a challenge in protecting people and electrical equipment from the risk of electric shock. The increasing use of swampland for electrical installations necessitates a solution to reduce the high grounding resistance resulting from poor soil resistivity values. This study proposes using dolomite as an admixture to improve soil conductivity and lower grounding resistance. Experimental methods were conducted by embedding rod electrodes of various materials in dolomite-mixed media with varying compositions. The results showed that adding dolomite significantly decreased the grounding resistance, although there were inconsistencies in the test results; on average, the decrease in resistance reached 25%. Galvanized electrodes proved to be the most effective in this system. These findings provide new insights in the field of grounding systems and offer practical solutions that are environmentally friendly and sustainable. This research is expected to be an important reference for developing more innovative and effective grounding system techniques in the future
Realization of Bernstein-Vazirani quantum algorithm in an interactive educational game
Full text linkQuantum 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