cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Tole Sutikno
Contact Email
ijece@iaesjournal.com
Phone
-
Journal Mail Official
ijece@iaesjournal.com
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,301 Documents
 617   618   619   620   621 
Convolutional neural network-based hybrid beamforming design based on energy efficiency for mmWave M-MIMO systems Ayad, Hanane; Bendimerad, Mohammed Yassine; Bendimerad, Fethi Tarik
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5443-5452

Abstract

Millimeter-wave (mmWave) massive multiple-input multiple-output (M- MIMO) technology brings significant improvements in data transmission rates for communication systems. A key to the design of mmWave M-MIMO systems is beamforming techniques, which focus signals toward specific directions but rely on expensive, energy-intensive radio frequency (RF) chains. To address this issue, hybrid beamformers (HB) have been introduced as a partial solution, and deep learning (DL) has proven effective for HB design. However, previous works utilizing machine learning (ML) networks have primarily focused on the spectral efficiency (SE) metric for constructing HB. In this paper, we present a convolutional neural network (CNN) architecture whose loss function is defined to maximize energy efficiency (EE) directly. The network jointly learns analog and digital beamformers by evaluating EE (throughput per total power, including phase shifters, switches, digital-to-analog converters (DACs), and RF chains) and selecting the configuration that yields the highest EE. The CNN takes a channel matrix as input and outputs RF and baseband beamformer matrices. Simulation results validate the effectiveness of the proposed M-MIMO EE scheme, achieving significant EE improvements by optimizing hybrid precoding and reducing RF chain usage.
Enhancing system integrity with Merkle tree: efficient hybrid cryptography using RSA and AES in hash chain systems Fauzi, Irza Nur; Farikhin, Farikhin; Jie, Ferry
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5679-5689

Abstract

An analysis is conducted to address the growing threats of data theft and unauthorized manipulation in digital transactions by integrating \structures within hash chain systems using hybrid cryptography techniques, specifically Rivest-Shamir-Adleman (RSA) and advanced encryption standard (AES) algorithms. This approach leverages AES for efficient symmetric data encryption and RSA for secure key exchanges, while the hash chain framework ensures that each data block is cryptographically linked to its predecessor, reinforcing system integrity. The Merkle tree structure plays a crucial role by allowing precise and rapid detection of unauthorized data changes. Empirical analyses demonstrate notable improvements in both the efficiency of cryptographic processes and the robustness of data validation, underscoring the method’s applicability in high data throughput environments such as educational institutions. This research makes a substantive contribution to information security by offering a sophisticated solution that strengthens data protection practices, ensuring greater resilience against increasingly sophisticated data threats.
Evaluating clustering algorithms with integrated electric vehicle chargers for demand-side management Abida, Ayoub; Majdoul, Redouane; Zegrari, Mourad
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5837-5846

Abstract

The integration of electric vehicles (EVs) and their effects on power grids pose several challenges for distribution operators. These challenges are due to uncertain and difficult-to-predict loads. Every electric vehicle charger (EVC) has its specific pattern. This challenge can be addressed by clustering methods to determine EVC energy consumption clusters. Demand side management (DSM) is an effective solution to manage the incoming load of EVs and the large number of EVCs. Considering the challenges of peak consumptions and valleys, the adoption of vehicle-to-grid (V2G) technology requires mastering load clusters to develop energy management systems for distributors. This work used clustering algorithms (K-means, DBSCAN, C-means, BIRCH, Mean-Shift, OPTICS) to identify load curve patterns, and for performance evaluation of algorithms, it worked on metrics like the Silhouette coefficient, Calinski-Harabasz index (CHI), and Davies-Bouldin index (DBI) to evaluate results. C-means achieves the best overall clustering performance, evidenced by the highest Silhouette coefficient (0.30) and a strong Calinski-Harabasz score (543). Mean-Shift excels in the Davies-Bouldin Index (1.13) but underperforms on other metrics. BIRCH provides a balanced approach, delivering moderate results across evaluated metrics.
Design and development of home-grown biometric fingerprint device and software for attendance and access control Soyemi, Jumoke; Ige, Ogunyinka Olawale; Soyemi, Olugbenga Babajide; Ademola, Ajibodu Franklin; Jayeoba, Adaramola Ojo; Olumide, Afolayan Andrew; Amode, Habeeb O.; Akinde, Mukail Aremu
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5616-5632

Abstract

This study details the design, development, and deployment of an Android-based Biometric Fingerprint system tailored for institutional access control, attendance tracking, exam monitoring, and staff management. Developed collaboratively by the Innovation Centre and departments across engineering and information and communication technology (ICT), the system integrates custom hardware and software. Hardware includes fingerprint sensors connected to an ATMEGA8 microcontroller and Android interfaces for portability. The software uses modular architecture, comprising a Kotlin-based mobile app with Jetpack Compose, a Laravel-powered web admin panel, and a secure backend API hosted on a virtual private server (VPS). Fingerprint data is safely stored using base64 encoding, enabling accurate user authentication and real-time tracking. A functional prototype was built, tested, and refined, with 95 units deployed in a pilot phase. The system supports multiple fingerprint profiles, secure data handling, and integration with existing institutional platforms. Emphasizing customization, modularity, and adherence to ICT policies, the research also serves as a training tool for staff and students, enhancing operational efficiency and supporting local technology development. Performance evaluation showed a FAR of 0.5%, FRR of 1.2%, and an average authentication time of 2.3 seconds. Post-deployment, student attendance increased by 15%, fee compliance by 10%, and 89% of users rated the system as easy to use. This work demonstrates effective hardware-software co-design for scalable biometric authentication in educational settings.
Detection of breast cancer with ensemble learning using magnetic resonance imaging Nadkarni, Swati; Noronha, Kevin
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5371-5379

Abstract

Despite notable progress in medicine along with technology, the deaths due to breast cancer are increasing steadily. This paper proposes a framework to aid the early detection of lesions in breast with magnetic resonance imaging (MRI). The work has been carried out using diffusion weighted imaging (DWI) and dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI). Data augmentation has been incorporated to enlarge the data set collected from a reputed hospital. Deep learning has been implemented using the ensemble of convolutional neural network (CNN). Amongst the individual CNN models, the you only look once (YOLO) CNN yielded the highest performance with an accuracy of 93.4%, sensitivity of 93.44%, specificity of 93.33%, and F1-score of 93.44%. Using Hungarian optimization, appropriate selection of individual CNN architectures to form the ensemble of CNN was possible. The ensemble model enhanced performance with 95.87% accuracy, 95.08% sensitivity, 96.67% specificity, and F1-score of 95.87%.
H-shaped terahertz patch antenna with metamaterials for biomedical applications Saidi Alaoui, Kaoutar; Younes, Siraj; Jaouad, Foshi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5215-5222

Abstract

This paper presents the design and simulation of an H-shaped terahertz microstrip patch antenna integrated with a metamaterial (MTM) layer to enhance performance for biomedical sensing applications. The antenna modeled using high frequency structure simulator (HFSS), is optimized for 4.37 THz operation. While FR4 is used in simulations for baseline analysis, alternative low-loss substrates such as polyimide or quartz are recommended for practical THz applications. The antenna design uses an FR4 substrate with a dielectric constant of 4.4 and a thickness of 2 μm. Ground plane, feed line, and patch are made of copper material. The integration of the MTM enhance clearly the antenna characteristics. This integration helps to improve the antenna impedance matching; the reflection coefficients was enhanced from -25.01 to -63.10 dB. Additionally, this integration boost also the antenna radiation characteristics, increasing the gain from 2.62 to 3.86 dB and the directivity from 3.57 to 4.97 dB.
Greenhouse gas reduction system for engines using electrolyte technology Chainok, Bopit; Wasuri, Boonthong; Chainok, Piyamas
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5524-5534

Abstract

This research focuses on developing a system to reduce greenhouse gas emissions in internal combustion vehicle engines using electrolyte technology and embedded programming on an electronic board via the OBI protocol. The main objectives are to create a prototype, apply it in real-world scenarios, evaluate its efficiency, and facilitate technology transfer. The system, designed to reduce greenhouse gases from vehicles, consists of a Bluetooth on-board diagnostics (OBD) scanner connected to the electronic control unit (ECU). This scanner transmits data to an embedded microcontroller through a Bluetooth module. The microcontroller, which includes software for controlling oxygen measurement and production, operates to decrease greenhouse gas emissions. The results show that the electronic device, IC ELM327, decodes OBD into RS232, processes the oxygen output from the exhaust pipe using embedded programming on the Arduino Uno-R3 microprocessor, and controls the oxygen production unit with electrolyte technology. The system adds 9.82% oxygen to the exhaust and reduces carbon monoxide by 21.04% and carbon dioxide by 13.86%. Additionally, the technology transfer received high satisfaction with a mean score of 4.61, indicating efficient technology dissemination.
Smart wearable glove for enhanced human-robot interaction using multi-sensor fusion and machine learning Herbaz, Nourdine; El Idrissi, Hassan; Sabir, Hamza; Badri, Abdelmajid
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5162-5172

Abstract

Hand gesture recognition (HGR) using flexible sensors (flex-sensor) and the MPU6050 sensor has proved to be a key area of research in human-machine interaction, with major applications in biasing, rehabilitation, and assisted robotics. This paper proposes a wearable intelligent glove designed to operate a robotics arm in real time, relying on multi-sensor fusion and machine learning methods to enhance the system's responsiveness and precision. The proposed system enables the intuitive reproduction of hand movements and precise control of the robotic arm. In the context of Industry 4.0 and internet of things (IoT), the classification of gestures is necessary for maintaining operational efficiency. To guarantee gesture recognition, data signals from the smart glove are collected and trained by a recurrent neural network (RNN), which achieves 98.67% accuracy for real-time classification of seven gestures. Beyond industrial applications, the wearable smart glove can be exploited in a recognized circuit of all systems, including rehabilitation exercises that involve recording the progression of muscular activity for the assessment of motor functions and serve as a tool for patient recovery.
Hardware efficient multiplier design for deep learning processing unit V., Jean Shilpa; R., Anitha; S., Anusooya; P. K., Jawahar; E., Nithesh; S., Sairamsiva; K., Syed Rahaman
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5205-5214

Abstract

Deep learning models increasing computational requirements have increased the demand for specialized hardware architectures that can provide high performance while using less energy. Because of their high-power consumption, low throughput, and incapacity to handle real-time processing demands, general-purpose processors frequently fall short. In order to overcome these obstacles, this work introduces a hardware-efficient multiplier design for deep learning processing unit (DPU). To improve performance and energy efficiency, the suggested architecture combines low-power arithmetic circuits, parallel processing units, and optimized dataflow mechanisms. Neural network core operations, such as matrix computations and activation functions, are performed by dedicated hardware blocks. By minimizing data movement, an effective on-chip memory hierarchy lowers latency and power consumption. According to simulation results using industry-standard very large-scale integration (VLSI) tools, compared to traditional processors, there is a 25% decrease in latency, a 40% increase in computational throughput, and a 30% reduction in power consumption. Architecture’s scalability and modularity guarantee compatibility with a variety of deep learning applications, such as edge computing, autonomous systems, and internet of things devices.
Adaptive tilt acceleration derivative filter control based artificial pancreas for robust glucose regulation in type-I diabetes mellitus patient Dutta, Smitta Ranjan; Patra, Akshaya Kumar; Mishra, Alok Kumar; Agrawal, Ramachandra; Subudhi, Dillip Kumar; Satapathy, Lalit Mohan; Kar, Sanjeeb Kumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5297-5313

Abstract

This study proposes an Aquila optimization–based tilt acceleration derivative filter (AO-TADF) controller for robust regulation of blood glucose (BG) levels in patients with type-I diabetes mellitus (TIDM) using an artificial pancreas (AP). The primary objective is to develop a controller that ensures normo-glycemia (70–120 mg/dl) while enhancing stability, accuracy, and robustness under physiological uncertainties and external disturbances. The AO algorithm tunes the control gains of the TADF controller to minimize the integral time absolute error (ITAE), ensuring optimal insulin infusion in real time. The AO-TADF controller introduces a filtered structure to improve the dynamic response and noise rejection capability, effectively handling the nonlinear nature of glucose-insulin dynamics. Simulation results demonstrate that the proposed approach achieves a faster settling time (230 minutes), lower peak overshoot (3.9 mg/dl), and reduced noise (1%) compared to conventional proportional integral derivative (PID), fuzzy, sliding mode (SM), linear quadratic gaussian (LQG), and H∞ controllers. The closed-loop system achieves a stable glucose level of 81 mg/dl under varying meal and exercise disturbances, validating the superior performance and robustness of the AO-TADF approach.

Page 619 of 631 | Total Record : 6301

 617   618   619   620   621 

Filter by Year

2011 2026


Reset
Filter By Issues
All Issue Vol 16, No 1: February 2026 Vol 15, No 6: December 2025 Vol 15, No 5: October 2025 Vol 15, No 4: August 2025 Vol 15, No 3: June 2025 Vol 15, No 2: April 2025 Vol 15, No 1: February 2025 Vol 14, No 6: December 2024 Vol 14, No 5: October 2024 Vol 14, No 4: August 2024 Vol 14, No 3: June 2024 Vol 14, No 2: April 2024 Vol 14, No 1: February 2024 Vol 13, No 6: December 2023 Vol 13, No 5: October 2023 Vol 13, No 4: August 2023 Vol 13, No 3: June 2023 Vol 13, No 2: April 2023 Vol 13, No 1: February 2023 Vol 12, No 6: December 2022 Vol 12, No 5: October 2022 Vol 12, No 4: August 2022 Vol 12, No 3: June 2022 Vol 12, No 2: April 2022 Vol 12, No 1: February 2022 Vol 11, No 6: December 2021 Vol 11, No 5: October 2021 Vol 11, No 4: August 2021 Vol 11, No 3: June 2021 Vol 11, No 2: April 2021 Vol 11, No 1: February 2021 Vol 10, No 6: December 2020 Vol 10, No 5: October 2020 Vol 10, No 4: August 2020 Vol 10, No 3: June 2020 Vol 10, No 2: April 2020 Vol 10, No 1: February 2020 Vol 9, No 6: December 2019 Vol 9, No 5: October 2019 Vol 9, No 4: August 2019 Vol 9, No 3: June 2019 Vol 9, No 2: April 2019 Vol 9, No 1: February 2019 Vol 8, No 6: December 2018 Vol 8, No 5: October 2018 Vol 8, No 4: August 2018 Vol 8, No 3: June 2018 Vol 8, No 2: April 2018 Vol 8, No 1: February 2018 Vol 7, No 6: December 2017 Vol 7, No 5: October 2017 Vol 7, No 4: August 2017 Vol 7, No 3: June 2017 Vol 7, No 2: April 2017 Vol 7, No 1: February 2017 Vol 6, No 6: December 2016 Vol 6, No 5: October 2016 Vol 6, No 4: August 2016 Vol 6, No 3: June 2016 Vol 6, No 2: April 2016 Vol 6, No 1: February 2016 Vol 5, No 6: December 2015 Vol 5, No 5: October 2015 Vol 5, No 4: August 2015 Vol 5, No 3: June 2015 Vol 5, No 2: April 2015 Vol 5, No 1: February 2015 Vol 4, No 6: December 2014 Vol 4, No 5: October 2014 Vol 4, No 4: August 2014 Vol 4, No 3: June 2014 Vol 4, No 2: April 2014 Vol 4, No 1: February 2014 Vol 3, No 6: December 2013 Vol 3, No 5: October 2013 Vol 3, No 4: August 2013 Vol 3, No 3: June 2013 Vol 3, No 2: April 2013 Vol 3, No 1: February 2013 Vol 2, No 6: December 2012 Vol 2, No 5: October 2012 Vol 2, No 4: August 2012 Vol 2, No 3: June 2012 Vol 2, No 2: April 2012 Vol 2, No 1: February 2012 Vol 1, No 2: December 2011 Vol 1, No 1: September 2011 More Issue