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
Get PDFAccurate 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
Get PDFPremature 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
Get PDFMost 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
Optimizing vehicle inspection efficiency and integrity in Tanzania through blockchain technology
Get PDFThis study proposes a blockchain-based solution to improve the efficiency and integrity of vehicle inspections in Tanzania, with a focus on the National Institute of Transport. The system combines Hyperledger fabric, a permissioned blockchain that provides identity management and fine-grained access control, with the InterPlanetary file system (IPFS), a decentralized content-addressed store for large artifacts such as inspection images and portable document format (PDF) forms. Smart contracts encode inspection rules and approvals, which yield tamper-evident records, faster retrieval of histories, and uniform enforcement across centers. A mathematical model based on the M/M/1 queueing system, combined with a cost-benefit analysis, supports empirical findings: the total inspection cycle time decreases by approximately 30 percent, the average waiting time declines by about 20 to 30 percent, and annual operational savings reach approximately USD 800,000. These gains enhance auditability and transparency, which contribute to road safety outcomes by reducing opportunities for tampering and error. The design includes offline capture with later synchronization, which suits centers with intermittent connectivity. The approach is transferable to adjacent public services, for example, licensing, fine collection, and selected registries
Watermarking on spread multi-frame data video using discrete wavelet transform hybrid and frame ratio message variance
Get PDFThe exponential growth of video sharing demands secure and imperceptible watermarking methods. This study presents a video watermarking framework using discrete wavelet transform (DWT) with hybrid sub-band embedding and multi-frame allocation to balance imperceptibility, capacity, and robustness. Watermark bits are adaptively distributed across low-low (LL), low-high (LH), and high-low (HL) sub-bands of selected frames, with uncompressed audio video interleave (AVI) ensuring coefficient integrity. Experiments on 640×360 videos show LL-only embedding achieves high imperceptibility (peak signal-to-noise ratio (PSNR) > 38.6 dB, structural similarity index measure (SSIM) ≥ 0.9945, and bit error rate (BER) = 0), while LL-dominant hybrids increase capacity with slight robustness trade-offs. Embedding in LH and high-high (HH) sub-bands raises distortion vulnerability. Under cropping, BER rises from 0.005 to 0.205 (0–50%), and normalized correlation (NC) drops from 0.998 to 0.802, remaining acceptable for ≤30% cropping. The scheme resists joint photographic experts’ group (JPEG) compression quality factor 20–80 (Q20–Q80), resizing (≥70%), and mild Gaussian blur (3×3), maintaining efficient decoding under higher payloads. Future work may apply error-correction coding and redundancy-aware embedding for improved resilience. Overall, the proposed method offers a secure, adaptive, and efficient solution for video authentication and covert communication
The effectiveness of bentonite in reducing soil resistance in acidic water swampland
Get PDFThis study aims to evaluate the effectiveness of bentonite mixtures in reducing grounding resistance in acidic swampy areas. The method used is an experiment comparing resistance before and after the addition of bentonite in various compositions (25%, 50%, 75%, and 100%), supplemented with linear regression analysis. The results showed that bentonite significantly reduced soil resistance in three types of electrodes: iron rebar, copper-coated iron, and galvanised iron. The highest reduction in resistance was achieved in iron rebar electrodes, from 35.93 Ω to 22.46 Ω (a 37% reduction) with the addition of 25% bentonite. Linear regression analysis showed a consistent negative relationship between the percentage of bentonite and grounding resistance, with a coefficient of determination (R²) varying between 26.40% and 73.39%. These findings indicate that bentonite is effective as a natural grounding material in acidic swampy areas. This research makes an important contribution to the development of more efficient and safer electrical systems in swampy areas and challenging environments, while also supporting the use of natural materials to reduce dependence on synthetic chemicals
Object detection and tracking with decoupled DeepSORT based on αβ filter
Get PDFWith the rapid growth of the population, the demand for autonomous video surveillance systems has substantially increased. Recently, artificial intelligence has played a key role in the development of these systems. In this paper, we present an enhanced autonomous system for object detection and tracking in video streams, tailored for transportation and video surveillance applications. The system comprises two main stages: detection stage; this stage employs you only look once (YOLO)v8m, trained on the KITTI dataset, and is configured to detect only pedestrians and cars. The model achieves an average precision of 97.3% and 87.1% for cars and pedestrians classes respectively, resulting a final mean average precision (mAP) of 92.2%. Tracking stage; the tracking component utilizes the DeepSORT algorithm, which originally incorporates a Kalman filter for motion prediction and performs data association using cosine and Mahalanobis distances to maintain consistent object identifiers across frames. To improve tracking performance, we introduce two key modifications to the original DeepSORT: architecture modification and Kalman filter replacement. The tracking tests are carried out on KITTI and MOTChallenge Benchmarks. The final order tracking accuracy (HOTA) scores achieve 77.645 and 54.019 for Cars and Pedestrians classes respectively in the KITTI-Benchmark and 45.436 for the Pedestrians class in the MOTChallenge-Benchmark
Decision support system in machine learning models for a face recognition-based attendance system
Get PDFThis 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
Get PDFWith 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
Get PDFThe 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