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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 70 Documents
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Search results for , issue "Vol 15, No 4: August 2025" : 70 Documents clear
High-speed field-programmable gate array implementation for mmWave orthogonal frequency-division multiplexing transmitters: design and evaluation Puntsri, Kidsanapong; Bunsri, Bussakorn; Suthisopapan, Puripong
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3813-3823

Abstract

This paper presents a field-programmable gate array (FPGA)-based implementation of an orthogonal frequency-division multiplexing (OFDM) transmitter signal processing chain optimized for high-speed millimeter wave (mmWave) communication systems. The design prioritizes real-time processing efficiency and flexibility. A high-throughput 2048-point inverse fast Fourier transform (IFFT) module, realized using a Radix-2 algorithm, forms the core of the design, showcasing efficient hardware resource utilization. The implementation further includes cyclic prefix (CP) insertion and configurable support for various quadrature amplitude modulation (QAM) modulation orders and pilot arrangements. The design is implemented in VHSIC Hardware Description Language (VHDL) using Vivado 2020 and evaluated on the Zynq UltraScale+ RFSoC ZCU111 evaluation kit. The processing pipeline employs eight parallel lanes for concurrent data computation. Experimental results demonstrate a mean squared error (MSE) of only 0.00013 between the FPGA-generated waveform and its MATLAB-simulated counterpart. Additionally, post-implementation resource utilization analysis shows efficient usage of FPGA resources. These findings validate the efficacy and real-time capability of the proposed FPGA-based OFDM transmitter leverages parallelism and high-speed architecture to efficiently process massive data streams, making it suitable for a wide range of mmWave OFDM applications. In contrast to recent works that focus on lower-order IFFT modules, this paper employs a high-throughput IFFT computation, showcasing efficient hardware resource utilization for high-speed mmWave applications.
Enhancing mobile agent protection using a hybrid security framework combining pretty good protocol and code obfuscation Zraqou, Jamal; Alkhadour, Wesam; Baklizi, Mahmoud; Omar, Khalil; Fakhouri, Hussam
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3913-3927

Abstract

The security of mobile agents, which are autonomous software entities capable of migrating between computers to execute tasks, remains a critical concern in modern information technology. Cybersecurity has been a central component of this technological revolution and continues to be one of the most essential requirements for any software or platform. Despite advances in security measures, protecting mobile agents, particularly those carrying sensitive data, while they transmit over networks remains challenging. This research proposes a novel hybrid security technique, abbreviated as pretty good privacy and code obfuscation framework (PGF), which combines pretty good privacy (PGP) with code obfuscation. PGF is designed specifically to protect mobile agents, focusing on systems like Aglets. The technique aims to safeguard the integrity and confidentiality of the agent's data during transmission. Based on the mobile agent Aglets and the PGF technique, the proposed model enhances security by introducing additional protection layers during agent creation and transmission using PGP and code obfuscation. The comparative analysis demonstrated that PGF outperformed other algorithms in terms of time efficiency and security, effectively handling large data sizes through its hybrid cryptographic approach, which combines asymmetric and symmetric encryption. The model was implemented using the Aglets framework in Java development kit (JDK) and NetBeans and showed high reliability and practicality. However, its current design is tailored to Aglets, and future work could focus on adapting the model to other platforms and optimizing its resource efficiency for constrained environments.
Instance segmentation for PCB defect detection with Detectron2 Chaithanya, Aravalli Sainath; Devi, Lavadya Nirmala; Srividya, Putty
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4172-4180

Abstract

Printed circuit boards (PCBs) are essential in modern electronics, where even minor defects can lead to failures. Traditional inspection methods struggle with complex PCB designs, necessitating automated deep learning techniques. Object detection models like Faster R-CNN and YOLO rely on bounding boxes for defect localization but face overlap issues, limiting precise defect isolation. This paper presents a segmentation-based PCB defect detection model using Detectron2’s Mask R-CNN. By leveraging instance segmentation, the model enables pixel-level defect localization and classification, addressing challenges such as shape variations, complex structures, and occlusions. Trained on a dataset of 690 COCO-annotated images, the model underwent rigorous experimentation and parameter tuning. Evaluation metrics, including loss functions and mean average precision (mAP), assessed performance. Results showed a steady decline in loss values and high precision for defects like mouse bites and missing holes. However, performance was lower for complex defects like spurs and spurious copper. This study highlights the effectiveness of instance segmentation in PCB defect detection, contributing to improved quality control and manufacturing automation.
Sensitivity factors based computationally efficient approach for evaluation and enhancement of available transfer capability Sureban, Manjula S.; Ankaliki, Shekhappa G.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3556-3565

Abstract

Available transfer capability (ATC) is an indication of the capability of the transmission system to efficiently increase power transmission for further commercial trading between two areas or two points. ATC plays an important role in operating power systems economically, reliably, and securely. As the deregulation in the power system can cause overload in the transmission system, ATC evaluation and enhancement are required for secure and reliable operation. The advancements in power generation techniques and switching from centralized generation to distributed generation (DG) with more emphasis on renewable sources have resulted in various approaches to enhance ATC. In this work, a computationally efficient sensitivity-based methodology for evaluating and improving ATC with the presence of renewable generation is proposed. The developed approach is implemented on the IEEE 30 bus system and the outcome is compared with the existing methods in the literature.
An approach for predicting brain tumor with machine learning techniques Shashank, PSRB; Anand, L.; Pitchai, R.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4332-4340

Abstract

The medical industry relies heavily on image processing for tumor diagnosis. Medical imaging is an ever-evolving and intricate field. Brain tumor (BT) is extremely frequent and may cause death. A BT develops when brain cells divide and grow out of control. The prognosis for people with BT can be greatly improved and the survival rate can be increased if the tumor is detected early. A single individual's brain magnetic resonance imaging (MRI) scan comprises of multiple slices through the 3D anatomical perspective. As a result, extracting tumor from MRI scans is a difficult and time-consuming laborious task. Because of the risks associated with biopsies, an MRI-based automated BT categorization is a safer alternative. The scientific profession has worked tirelessly from the beginning of the millennium to develop an automatic BT segmentation and classification system. Therefore, there is a large body of work in the field dedicated to the study of BT research through machine learning (ML) techniques. The review paper summaries the publicly accessible benchmark datasets typically used and compares various processing approaches, feature extraction (FE), segmentation, and classification algorithms for BT. The report also emphasizes the challenges of BT detection. Our hope is that this survey will provide researchers, clinicians, and other interested parties will gain an in-depth understanding of BT segmentation and classification using ML.
Shearlet-based texture analysis and deep learning for osteoporosis classification in lumbar vertebrae Ramakrishna, Poorvitha Hullukere; Muddaraju, Chandrakala Beturpalya; Jayaramu, Bhanushree Kothathi; Narasimhamurthy, Shobha
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4318-4331

Abstract

Osteoporosis is a bone disorder characterized by reduced bone density and increased fracture risk. It challenges society's health, remarkably among the elderly population. This research proposed an innovative method by combining Shearlet-transform (ST) spectral analysis with a deep learning neural network (DLNN) and a convolutional neural network (CNN), for osteoporosis classification in lumbar vertebrae (LV) L1-L4 of spine X-ray images. The ST enables precise extraction of texture features from images by capturing significant information regarding trabecular bone micro-architecture and bone mineral density (BMD) variations revealing in osteoporosis regions. These extracted features serve as input to a DLNN for automated classification of osteoporotic and non-osteoporotic vertebrae. Similarly, without extracting any features from ST image is directly used as an input to the CNN to classify the images. The experimental results highlight the framework's effectiveness, achieving 96% accuracy in osteoporosis image classification using CNN. Early and precise detection of osteoporosis, particularly in the lumbar vertebrae, is vital for effective treatment and fracture prevention. This study particularly emphasizes the potential and effectiveness of integrating image spectral analysis technique with NN, to improving diagnostic accuracy and clinical decision-making in osteoporosis management.
Comparative of prediction algorithms for energy consumption by electric vehicle chargers for demand side management Abida, Ayoub; Majdoul, Redouane; Zegrari, Mourad
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4192-4201

Abstract

This study focuses on demand side management (DSM), specifically managing electric vehicle (EV) charging consumption. Power distributors must consider numerous factors, such as the number of EVs, charging station availability, time of day, and EV user behavior, to accurately predict EV charging demand. We utilized machine learning algorithms and statistical modeling to predict the energy required by EV users for a specific charger and compared algorithms like K-Nearest Neighbors, XGBoost, random forest regressor, and ridge regressor. To contribute to the existing literature, which lacks studies on future energy prediction for a specific period, we conducted predictions for the next year 2024 on the energy consumption of electric vehicles for an electric vehicle charging point in a Moroccan city. These predictions can be generalized to other chargers as well. Our results showed that K-nearest neighbors (KNN) outperformed other algorithms in accuracy. This study provides valuable insights for distribution operators to manage energy resources efficiently and contributes to the DSM field by highlighting the effectiveness of KNN in predicting EV charging demand.
Ensemble of convolutional neural network and DeepResNet for multimodal biometric authentication system Kailas, Ashwini; Girimallaih, Madhusudan; Madigahalli, Mallegowda; Mahadevachar, Vasantha Kumara; Somashekarappa, Pranothi Kadirehally
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4279-4295

Abstract

Multimodal biometrics technology has garnered attention recently for its ability to address inherent limitations found in single biometric modalities and to enhance overall recognition rates. A typical biometric recognition system comprises sensing, feature extraction, and matching modules. The system’s robustness heavily relies on its capability to effectively extract pertinent information from individual biometric traits. This study introduces a novel feature extraction technique tailored for a multimodal biometric system utilizing electrocardiogram (ECG) and iris traits. The ECG helps to incorporate the liveliness related information and Iris helps to produce the unique pattern for each individual. Therefore, this work presents a multimodal authentication system where data pre-processing is performed on image and ECG data where noise removal and quality enhancement tasks are performed. Later, feature extraction is carried out for ECG signals by estimating the Heart rate variability feature analysis in time and frequency domain. Finally, the ensemble of convolution neural network (CNN) and DeepResNet models are used to perform the classification. The overall accuracy is reported as 0.8900, 0.8400, 0.7900, 0.8932, 0.87, and 0.97 by using convolutional neural network-long short-term memory (CNN-LSTM), support vector machine (SVM), random forest (RF), CNN, decision tree (DT), and proposed MBANet approach respectively.
Integration of strain gauge sensor in biceps muscle movement detection using LabView Kristyawati, Desy; Soerowirdjo, Busono; Christina, Erma Triawati; Harahap, Robby Kurniawan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3696-3706

Abstract

Muscle injuries caused by sports can have a serious impact on sportsmen, to avoid injuries during sports can be prevented by detecting the wrong movement using a strain gauge sensor attached to the muscle which in this study is devoted to the biceps muscle. The strain gauge will detect muscle movement, and the output generated at the strain gauge will be converted into the form of voltage and current which will be used to be processed using machine learning to get data patterns so that they can be grouped into data patterns of wrong movements and correct movements. The strain gauge movement pattern here is simulated using LabView by using a gauge resistance of 120 Ω, strain configuration Quarter Bridge 1, gauge factor 2.05, Vex is the excitation voltage given to the Wheatstone bridge is 5 V and the initial voltage -180.08 µV, the strain gauge output pattern is obtained in the form of Excel and with this data can be converted into voltage and current.
Indonesian speech emotion recognition: feature extraction and neural network approaches Afifah, Izza Nur; Santoso, Tri Budi; Dutono, Titon
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3769-3778

Abstract

This study explored the challenges of emotion recognition in Indonesian speech using deep learning techniques, addressing the complex nuances of emotional expression in spoken language that posed significant difficulties for automatic recognition systems. The research focused on the application of feature extraction methods and the implementation of convolutional neural networks (CNN) and a hybrid convolutional neural networks-long short-term memory (CNN-LSTM) model to identify emotional states from speech data. By analyzing key features of speech signals, including mel frequency cepstral coefficient (MFCC), zero crossing rate (ZCR), root mean square energy (RMSE), pitch, and spectral centroid, the study evaluated the models’ ability to capture both spatial and temporal patterns in the data. Testing was conducted using an Indonesian dataset comprising 200 samples. The CNN model, utilizing four features (MFCC, ZCR, RMSE, and pitch), and the CNN-LSTM model, which used three features (MFCC, ZCR, and RMSE), both achieved an emotion classification accuracy of approximately 88%. The result showed that the CNN-LSTM model achieved comparable performance with a simpler feature set compared to the CNN model. This highlighted the significance of choosing the appropriate techniques in feature extraction and classification to enhance the accuracy of identifying emotions from speech data while also managing computational complexity.

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