Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

31,207 Article Results

Analysis of software-defined network on Proxmox with quality of-service testing using Open vSwitch

10.12928/telkomnika.v24i5.27549
Shafira; STMIK AMIK Bandung Febriani , Riezkan; STMIK AMIK Bandung Aprianda Firmansyah
This study designs and implements a virtual network based on software defined networking (SDN) in the Proxmox Virtual Environment (VE) using Open vSwitch (OVS) and OpenDaylight (ODL) controller, with performance testing of quality of service (QoS). The parameters tested include rate limiter, throughput, packet loss, and forwarding rate. Proxmox VE is selected for its centralized virtualization and clustering capabilities, while OVS manages network traffic among virtual machines (VMs). ODL is chosen for its compatibility with OpenFlow and OVS database (OVSDB) protocols, essential for OVS management. Testing uses iPerf3 to generate network traffic and analyze performance, including database service access simulation to mimic typical cloud application traffic loads. Forwarding rate measures the system’s packet forwarding capability per unit time, providing a comprehensive network performance overview. Results contribute to optimized SDN-based virtual network implementations in Proxmox environments and provide scientific and practical references for developing QoS-based virtual networking systems.
Volume: 24
Issue: 5
Page: 1504-1512
Publish at: 2026-10-01

Real-time depth measurement and stability control of AUV using regression approximation and filtered pressure data

10.11591/ijece.v16i5.pp2417-2430
Senanjung Prayoga , Dhaniel Beny Wardhana , Ryan Satria Wijaya
This paper presents the development and experimental validation of a prototype-scale autonomous underwater vehicle (AUV) depth control system using a proportional-integral-derivative (PID) controller with depth feedback from a SEN0257 water-pressure sensor. Raw sensor readings are filtered and calibrated using linear regression, reducing the depth estimation error, as indicated by a decrease in root mean square error (RMSE) from 1.88 to 0.63 cm. The calibrated depth signal is implemented in real time as the feedback source for closed-loop control on the testbed. Controller performance is evaluated by comparing two tuning strategies: Ziegler–Nichols (ZN) closed-loop tuning and manual fine-tuning. Experiments were conducted at depth setpoints of 70 and 100 cm under consistent pool conditions, and additional trials were performed while the AUV executes forward motion to assess robustness under dynamic disturbances. System responses are quantified using rise time, overshoot, settling time, and steady-state error. Results show that calibration significantly improves sensor suitability for feedback, while the fine-tuned PID controller produces a more stable depth response with lower overshoot, smaller steady-state error, and shorter settling time than the ZN controller, despite the faster initial rise achieved by ZN tuning. Overall, combining calibrated pressure-based depth estimation with fine-tuned PID gains enables stable and accurate depth regulation for prototype AUV operation.
Volume: 16
Issue: 5
Page: 2417-2430
Publish at: 2026-10-01

A chip level design of a multi-mode compressive sensing image sensor

10.11591/ijece.v16i5.pp2393-2404
Zahra Sepehri , Sayed Masoud Sayedi , Ehsan Yazdian
This paper presents the full chip-level design of a multi-mode CMOS vision sensor, emphasizing the detailed implementation of its circuit architecture. The proposed chip incorporates our previously developed photodiode sensing array together with the on-chip design of control circuitry. By embedding these building blocks, the chip enables pixel-level compressive sensing and supports dual operation modes, allowing the transmission of image data in both compressed and non-compressed formats. In either mode, the sensor is capable of capturing both scene images and difference images between consecutive video frames, operating at a frame rate of 40 fps. A 64*64 vision chip is implemented using TSMC 0.18um standard CMOS technology. In the normal scene image mode, with compression (N-C) and  without compression (N-nC), the structure consumes 36.99uW and 37.09uW, respectively. Meanwhile, in the difference scene image mode, with compression (D-C) and  without compression (D-nC), it consumes 38.67uW and 38.75uW, respectively.
Volume: 16
Issue: 5
Page: 2393-2404
Publish at: 2026-10-01

A comparative analysis of hybrid FFNN-LSTM and FFNN-RNN architectures for short term electricity load forecasting

10.11591/ijece.v16i5.pp2347-2356
Temitope Akinyede , Josephine Adenike Akinyede , Paul Kehinde Olulope , Emmanuel Taiwo Fasina , Temitope Adewale Olominu
Short-term accurate forecasting of electricity demand is crucial for power-system operation and energy scheduling and the equilibrium between electricity generation and consumption. The predicting of electric power consumption is however difficult because electricity-demand profiles are non-linear and time varying. In this paper, we consider two hybrid deep learning architectures feedforward neural network long short-term memory (FFNN-LSTM) and feedforward neural network recurrent neural network (FFNN-RNN) for multi-horizon electricity-load forecasting. The architectures we proposed combine the ability of FFNN to represent data non-linearly with the sequential modelling capability of LSTM and RNN. The authors assess the models based on historical electricity-load observations from Ado-Ekiti, at three different forecasting horizons of 24 hours, 72 hours, and 168 hours. Root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) assessed predictive performance. The findings reveal that FFNN-LSTM achieves a consistently lower RMSE across the evaluated horizons, resulting in greater effectiveness to confine relatively large forecasting errors. On the other hand, the separate LSTM and RNN models achieve lower MAE and MAPE in various scenarios, suggesting stronger short-term reaction to electricity demand. The results, therefore, show a trade-off between forecast stability and sensitivity to rapid load changes. In general, the performance of hybrid architecture is more stable compared to the standalone recurrent models which are more responsive to short-term changes when we look at most of the forecasting horizons. According to the research paper, the forecasting architectures can now be selected for smart grid applications.
Volume: 16
Issue: 5
Page: 2347-2356
Publish at: 2026-10-01

Performance and quality analysis of brain MRI image transmission over free-space optical communication systems under severe atmospheric conditions

10.11591/ijece.v16i5.pp2526-2536
Entidhar Mhawes Zghair , Seham Hashem , Ali Hammadi
Reliable transfer of brain magnetic resonance imaging (MRI) data over atmospheric free-space optical (FSO) links is a key enabler of telemedicine, yet conventional FSO studies judge link quality by communication metrics such as the bit error rate (BER) alone, which cannot guarantee the structural and contrast fidelity that diagnosis demands. This study proposes a quality-aware FSO transmission framework for brain MRI in which link performance is assessed jointly through BER, peak signal-to-noise ratio (PSNR), and the structural similarity index (SSIM). A physical-layer FSO channel is modelled in OptiSystem 20 and co-simulated with MATLAB R2023b, which performs image serialization, reconstruction, and quality analysis. Thirty axial T2-weighted slices (256×256, 8-bit) from the public IXI dataset are transmitted at 1550 nm over clear-air, rain, and fog channels at 500, 1000, and 2000 m. Adopting conservative diagnostic thresholds of PSNR ≥ 30 dB and SSIM ≥ 0.85, the link is diagnostically usable in clear air at all tested distances (PSNR = 42.1 dB, SSIM = 0.98 at 500 m) and in rain up to 2000 m (PSNR ≥ 31.2 dB), whereas fog degrades quality below the thresholds at every distance, reaching PSNR = 22.7 dB and SSIM = 0.68 at 2000 m. A concatenated forward-error-correction (FEC) scheme is then shown to restore diagnostic quality under fog up to 1000 m, extending the usable fog range, while 2000 m remains infeasible and motivates hybrid FSO/RF operation. The framework provides quantitative deployment limits for FSO-based medical image transport.
Volume: 16
Issue: 5
Page: 2526-2536
Publish at: 2026-10-01

BDLock: A blockchain-enabled two tier privacy-aware federated idam service platform using RBAC

10.11591/ijece.v16i5.pp2537-2548
Muhammad Shakil Pervez , Md. Nasim Adnan , Sarker Tanveer Ahmed Rumee , Moinul Islam Zaber
Centralized identity and access management (IDAM) systems suffer from sin-gle points of failure, lack of authorization transparency, and susceptibility to in-sider threats and privilege abuse. While role-based access control (RBAC) of-fers structured permission management, its enforcement through centralized pol-icy engines introduces auditability gaps unacceptable in modern distributed service delivery environments. This paper presents BDLock, a blockchain-enabled two-tier privacy-aware federated IDAM platform integrating OAuth 2.0, OpenID Connect (OIDC), and Hyperledger Fabric 2.4. The first tier validates JSON Web Tokens (JWT) issued by Keycloak against a Spring Boot resource server, the second tier enforces immutable scope-based RBAC rights on the Hyperledger Fabric ledger, ensuring every access decision is tamper-proof and auditable. Unlike prior approaches, BDLock uniquely bridges OAuth-authenticated off-chain identities to cryptographic on-chain Fabric wallet identities, satisfying all six STRIDE-modelled threats categories across both Web2 and Web3 identity models. Validated with up to 1,800 concurrent users, BDLock achieves a peak throughput of approximately 200 transactions per second using round-robin load balancing. At high concurrency, it outperforms single-peer fallback by up to 25%. Furthermore, it maintains uninterrupted access control during peer failures, eliminating the single point of failure found in all nine compared state-of-the-art systems.
Volume: 16
Issue: 5
Page: 2537-2548
Publish at: 2026-10-01

Design science research in developing a religious chatbot based on Bulugh al-Maram

10.11591/ijece.v16i5.pp2782-2794
Aris Tjahyanto , Irmasari Hafidz , Faizal Johan Atletiko
Chatbots have recently gained significant popularity. For instance, ChatGPT has become a preferred tool for many individuals seeking instant answers without relying on human responses. This immediacy sets chatbots apart from books, which require users to search for information manually. This time-consuming process does not align with millennials' preference for convenience and efficiency. Studying hadith independently using the Bulugh al-Maram book demands considerable time and effort. The limited use of natural language processing technologies in religious chatbots restricts their ability to handle complex inquiries effectively. A chatbot capable of answering hadith-related questions could greatly assist the public in studying hadith texts by providing direct responses without extensive searching. This chatbot was designed for web browsers, utilizing deep learning as its core technology. This research led to the development of a chatbot prototype for learning hadith from Bulugh al-Maram. Built using the design science research (DSR) methodology, the prototype achieves an intent recognition rate (IRR) of 86.82%. However, its capabilities are below the BERT model, demonstrating a strong ability to accurately interpret user questions and statements.
Volume: 16
Issue: 5
Page: 2782-2794
Publish at: 2026-10-01

Calibration-guided score fusion for robust multimodal traffic anomaly detection

10.11591/ijece.v16i5.pp2516-2525
Quang Hiep Do , Thien Tan Nguyen
Multimodal traffic anomaly detection is affected by differences in visual and audio score ranges, temporal fluctuations, and unstable decision thresholds. This paper proposes a calibration-guided score fusion (CGSF) framework that processes video frames and audio spectrograms through separate reconstruction-based models. The resulting anomaly scores are temporally smoothed, normalized using validation data, and combined at the score level. A percentile estimated from normal validation samples is then used as the decision threshold. The framework was evaluated on the MAVD and DADA2000 datasets. On MAVD, CGSF achiev,,,,,,ed a ROC-AUC of 0.553, a PR-AUC of 0.082, and an F1-score of 0.129. It outperformed direct fusion in precision, recall, and F1-score, although the gain in ROC-AUC was small. Analysis on DADA2000 showed smoother temporal score behaviour after calibration and smoothing. The results indicate that CGSF mainly improves score comparability and threshold consistency rather than producing a large increase in detection accuracy. Its modular design also allows the visual and audio branches to be trained and updated independently.
Volume: 16
Issue: 5
Page: 2516-2525
Publish at: 2026-10-01

High-performance 28 GHz antenna array design for millimeter wave 5G systems

10.12928/telkomnika.v24i5.27857
Moussab; University Abdelmalek Essaâdi Chbeine , Abderrahman; University Abdelmalek Essaâdi Chbeine , Mohamed; University Abdelmalek Essaâdi Bayjja , Lahcen; Hassan II University of Casablanca Sellak
This paper presents the design and performance evaluation of compact 28 GHz microstrip antenna arrays for millimeter-wave (mmWave) fifth generation (5G) wireless communication applications. The proposed antennas are implemented on a Rogers RT/duroid 5880 substrate with a relative permittivity of 2.2 and a thickness of 0.508 mm and were evaluated using full-wave electromagnetic simulations in Computer Simulation Technology (CST) Microwave Studio. Starting from a single antenna element, several scalable array configurations, including 1×2, 1×4, 1×8, and 2×8 structures, were designed to improve antenna gain and radiation directivity while maintaining good impedance matching characteristics. The novelty of this work lies in the development of compact scalable antenna arrays using a simple feeding network configuration to achieve progressive gain enhancement with reduced structural complexity. The simulation results show that the antenna performance improves as the number of array elements increases, achieving a maximum gain of 16.2 dB at 28 GHz with satisfactory return loss, voltage standing wave ratio (VSWR), and radiation behavior. In addition, the proposed arrays provide an appropriate trade-off between gain, bandwidth, and compact size, making them suitable candidates for future high-data-rate mmWave 5G communication systems.
Volume: 24
Issue: 5
Page: 1492-1503
Publish at: 2026-10-01

Exploration of thesis topic trends of students majoring in informatics and computer engineering with LDA method

10.12928/telkomnika.v24i5.27674
Ruslan; University of Makassar Ruslan , Rezki Nurul; University of Makassar Jariah S. Intam , Sasmita; University of Makassar Sasmita , Dewi; University of Makassar Fatmarani Surianto , Andi Akram; University of Makassar Nur Risal , Nur Azizah; University of Makassar Eka Budiarti
The selection of a thesis topic is very important because it determines the focus of the research and its contribution to knowledge. However, many students find it difficult to choose a topic that suits their interests and expertise. This study models the thesis topics of students in the Department of Informatics and Computer Engineering (JTIK) using the latent Dirichlet allocation (LDA) method, with term frequency–inverse document frequency (TF–IDF) as the model input. The data set includes 969 thesis titles from 2009 to 2024. The optimized LDA model identifies 17 main topics by adjusting parameters such as the number of topics, alpha, and beta. The best coherence value (0.7431) is achieved with alpha = 0.81, beta = 0.01, and 17 topics. The dominant themes included information system development, computer networks, and multimedia, reflecting the main research areas of JTIK. In addition, a web-based system was developed and integrated with the best model to help students identify relevant topics and find thesis references. This study demonstrates the effectiveness of topic modeling in higher education and provides insights into academic research trends.
Volume: 24
Issue: 5
Page: 1550-1560
Publish at: 2026-10-01

Real-time multimodal fatigue detection using facial vision and alert integration via ESP32 for occupational health applications

10.11591/ijece.v16i5.pp2750-2768
Andrés Enrique Rojas Primo , Alfredo Lazaro Gutierrez , Felix Pucuhuayla-Revatta
Early detection of work fatigue is a major challenge in industrial settings due to the lack of non-invasive, accessible, and low-cost systems capable of operating in real time. In this context, this research proposes a multimodal real-time fatigue detection system using facial vision and artificial intelligence, aimed at risk prevention and promoting occupational health. The system integrates geometric and behavioral parameters, such as eye aspect ratio (EAR), head tilt, and mouth opening, processed on a Raspberry Pi 5 using MediaPipe and a hybrid convolutional neural network (CNN) MobileViT model. Visual and audible alerts are managed by an ESP32 microcontroller using the message queuing telemetry transport (MQTT) protocol, while a graphical interface developed in Tkinter allows real-time monitoring of operator status. Experimental results, evaluated in a simulated work environment using AI-generated synthetic videos, show an accuracy greater than 97% and a latency of less than 250 ms, confirming the system's effectiveness in the early detection of signs of drowsiness and attention deficit. In conclusion, the proposal represents a non-invasive, scalable, and efficient solution that combines computer vision, deep learning, and the Internet of Things (IoT) to strengthen workplace safety and well-being.
Volume: 16
Issue: 5
Page: 2750-2768
Publish at: 2026-10-01

Robust resource allocation in multi-cell UE-specific RIS-assisted D2D relay networks under imperfect CSI

10.11591/ijece.v16i5.pp2575-2594
Kayode Popoola , Ayodeji Ajani , Stuart Nicholson , Muheeb Ahmed , Srilatha Narayangari Pamuri , Ibrahim Bala Alhassan
Device-to-device (D2D) communication enhances spectral efficiency but remains constrained by limited transmission range, underlay interference, and the half-duplex overhead of conventional relays. User equipment-specific reconfigurable intelligent surfaces (UE-RIS) offer a promising alternative by enabling passive beamforming to strengthen D2D links without additional spectrum consumption. However, existing studies typically assume perfect channel state information (CSI) and single-cell operation, limiting their applicability to practical deployments. This paper proposes a robust multi-cell resource allocation (RMRA) framework for UE-RIS-assisted D2D relay networks under imperfect CSI. A hybrid uncertainty model is adopted, combining statistical Gauss-Markov CSI errors for intra-cell links with bounded norm-ball uncertainty for inter-cell links. The joint optimisation of resource reuse, transmit power allocation, and RIS phase configuration is formulated as a stochastic mixed-integer nonlinear program that maximises network spectral efficiency while satisfying outage and quality-of-service constraints. To efficiently solve the problem, a three-stage algorithm is proposed comprising distance-pruned Hungarian assignment, robust power control using Bernstein-type inequality and S-procedure based semidefinite programming, and soft actor-critic (SAC) based passive beamforming. Simulation results show that RMRA achieves a 94% D2D access rate at light load and over 75% at full load, improves sum spectral efficiency by 34.7% and 70.2% over AF relaying and direct D2D, respectively, attains 118.5 bits/s/Hz/W energy efficiency, and maintains 30.2 bits/s/Hz under severe CSI uncertainty.
Volume: 16
Issue: 5
Page: 2575-2594
Publish at: 2026-10-01

Impact of optimal power flow on power quality in a low voltage three-phase network: application to a real case in Lubumbashi (DR Congo)

10.11591/ijece.v16i5.pp2304-2320
David Milambo Kasumba , Guy Nkulu Wa Ngoie , Hyacinthe Tungadio Diambomba , Jean-Paul Katond Mbay , Bonaventure Banza WA Banza
Low-voltage distribution systems (LVDS) in rapidly growing Sub-Saharan African cities frequently experience severe phase imbalance, voltage deviations, and high technical losses due to overloaded and poorly balanced feeders. Despite the increasing availability of advanced optimization techniques, their application to real low-voltage networks in developing countries remains limited. This study investigates the impact of an unbalanced three-phase optimal power flow (OPF) framework on the power quality and operational performance of a real low-voltage distribution network located in Kamalondo, Lubumbashi (Democratic Republic of the Congo). The network model was parameterized using field measurements collected between September and December 2024. The optimization problem was formulated as a mixed-integer nonlinear programming (MINLP) model and solved using the interior point method implemented in Pandapower. The proposed framework simultaneously minimizes active power losses and mitigates phase imbalance while respecting voltage and thermal operating constraints. Simulation results demonstrate significant improvements in network performance. The minimum phase-to-neutral voltage increased from 198 V to 210 V, while the maximum voltage decreased from 232 V to 226 V, improving compliance with power quality standards. The maximum current phase was reduced by 15%, and total active power losses decreased by 30%. Furthermore, the voltage unbalance factor (VUF) was reduced from 8% to 3% through optimized phase allocation and power redistribution. These results demonstrate that unbalanced three-phase OPF constitutes an effective and practical solution for improving power quality, reducing technical losses, and enhancing the operational reliability of heavily loaded low-voltage networks in developing urban environments.
Volume: 16
Issue: 5
Page: 2304-2320
Publish at: 2026-10-01

Information architecture debt: Why legacy platform schema decisions constrain enterprise AI capability

10.11591/ijece.v16i5.pp2473-2482
Mihir Shah
Enterprise platforms accumulate a specific category of technical debt this paper terms information architecture debt. Schema decisions optimized for transactional efficiency in earlier computing eras produce structural constraints that limit what artificial intelligence can accomplish on those platforms, largely independent of which models are selected or how they are orchestrated. This paper positions information architecture debt as a category distinct from code debt and infrastructure debt, and it argues that the distinction matters because remediation locality and coordination cost differ sharply. A five-indicator diagnostic framework is proposed, covering duplicate canonical entities, broken semantic chains, provenance gaps, enforcement asymmetry, and consumer assumption divergence. The framework supports a capability ceiling hypothesis: substrate defects impose an upper bound on AI outcomes that no model choice appears able to exceed. A sequenced remediation approach follows, prioritizing entity resolution, semantic alignment, and provenance instrumentation by AI-capability impact rather than ease of fix. Grounded in practitioner experience structuring enterprise information architecture across multi-language, multi-system platforms, the framework offers leaders diagnostic and sequencing tools, and it establishes a conceptual foundation for later quantitative and sector-specific refinement.
Volume: 16
Issue: 5
Page: 2473-2482
Publish at: 2026-10-01

A compact tri-band THz patch antenna using slot-loaded radiator and defected ground structure

10.11591/ijece.v16i5.pp2549-2558
Tran-Thi Bich Ngoc , Truong Thi Phuong Nhi
Terahertz (THz)- based antennas are essential for future 6G sensing and short range communications, especially when multi-band flexibility and compact di mensions are required. This paper presents a compact microstrip patch an tenna designed for THz applications with tri-band operation. The antenna is formed out of a rectangular patch filled with slots, a defective ground structure (DGS), and a microstrip feedline. Slot loading and the DGS generate various current paths, exciting several resonant modes and enhancing impedance match ing across the resonant frequency range. The proposed design is implemented on a 30 µm thick RO3003 substrate with a relative permittivity of 3 and loss tan gent of 0.001. The antenna has physical dimensions of 0.27×0.28×0.03 mm3. Simulation results show the antenna operates at three frequencies with a remark ably low return loss of up to-61.35 dB over the range of 1.59-3.2 THz. It offers tri-band gains of 6.01 dBi, 7.30 dBi, and 7.31 dBi, with a voltage standing wave ratio (VSWR) close to the ideal value of 1 for most bands. Furthermore, field distributions and power breakdown calculations are performed to better under stand the radiation mechanisms and efficiency characteristics at THz frequen cies. The antenna radiated power across the band, achieving up to 86.5% of the power accepted. These results indicate that the suggested design provides tri-band capabilities while being a simple, compact and efficient candidate for the demand for improved antennas in next-generation wireless communication systems.
Volume: 16
Issue: 5
Page: 2549-2558
Publish at: 2026-10-01
1 2 3
...
2080 2081
Show 5 of 2081

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration