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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 65 Documents
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Search results for , issue "Vol 37, No 2: February 2025" : 65 Documents clear
Design and evaluation of performance metrics of a pentaband broadband microstrip patch antenna for mm wave applications Jana, Subhasis; Kumar Singh, Raj; Mamta, Kumari
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp859-866

Abstract

This paper reports design and results of a microstrip patch antenna for broadband application in the millimeter wave communication with multiband features. Electromagnetic solver high-frequency structure simulator (HFSS) is employed to measure the effectiveness of the electromagnetic properties and electrical behaviour of the antenna. The proposed microstrip patch antenna (MPA) can be easily fabricated on a single substrate using standard photolithography process to attach the radiating element and feed lines to the dielectric material. On a 4.93 mm×5.86 mm metallic patch, over FR4 epoxy substrate with dielectric constant 4.4 and loss tangent 0.03, two L-shaped slots are placed along with a few micro slots of varied dimensions, and the antenna is fed with microstrip feedline with resistive load termination of 50 Ω. Pentaband resonant frequencies are realized in the K-band at 13.6 GHz, 23.2 GHz, 29.68 GHz, 32.96 GHz, and 38.56 GHz, with minimum return loss of -23.17 dB, bandwidth 2.32 GHz, omnidirectional radiation pattern, and maximum reported gain of 4.5 dB. The designed antenna achieved good electromagnetic radiation properties and electrical behaviour, and is a good choice for broadcasting over short distances, surveillance and monitoring, wireless sensor backhauls and telecommunication in the K-band networks.
EDK-LEACH: improving LEACH protocol-based machine learning in wireless sensor networks Lechani, Taous; Ourari, Samia; Rahmoune, Fayçal; Amari, Said; Termeche, Hayet
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1251-1261

Abstract

In wireless sensor networks (WSNs), many sensor devices are spread throughout the environment with the goal of collecting data and sending them to a base station (BS) for further studies. The issue of their limited battery power has aroused the interest of researchers, and several protocols were developed to optimize energy use and thus increase the network’s lifetime. The present research enhances the well-known low-energy adaptive clustering hierarchy (LEACH) protocol with a new artificial intelligence (AI) protocol named energy distance K-means LEACH (EDK-LEACH). For this purpose, an innovative clustering strategy built on the machine learning K-means algorithm is used in WSNs to improve the cluster formation process and maximise network stability. By implementing an objective function that considers each node’s residual energy and distance from the cluster centre when selecting the cluster head (CH) of each cluster, EDK-LEACH also eliminates the inherent randomness in LEACH during the CH election process. The proposed protocol has the advantage of ensuring better CH distribution throughout the network surface with a balanced load across all network nodes. In comparison with the known LEACH, the simulation results demonstrate the efficiency of our approach: the lifetime of the network is extended and the energy consumption is reduced.
Comparative study of pothole detection using deep learning on smartphone Ulul Amri, Achyar; Putra Kusuma, Gede
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp995-1004

Abstract

Potholes present a significant problem in many countries, leading to vehicle damage and traffic accidents. These road imperfections pose safety risks and impose economic burdens. Despite existing detection methods using sensors and computer vision deep learning processed on PCs, a gap remains in deploying cost-effective, widely accessible solutions. This study aims to bridge this gap by developing deep learning models optimized for smartphones, reducing costs and enhancing deployment feasibility. We developed multiple models for pothole detection, utilizing transfer learning and Bayesian hyperparameter tuning to optimize detection accuracy and resource efficiency. Our evaluations focused on computationally light models such as YOLOv8 small, YOLOv8-nano, YOLOv7 tiny, and faster R-CNN MobileNetV3. In terms of detection accuracy, YOLOv8 small and YOLOv8 nano stood out, achieving average precisions (AP) of 83.5% and 82.5%, respectively. YOLOv8 nano proved the most efficient, offering high detection accuracy, a file size three times smaller than YOLOv8 small in TFLite format, and the fastest inference time of 0.72 seconds per image. This study highlights the potential of smartphones in urban pothole detection, contributing to improved road maintenance and urban policy.
Modified-LSTM and feed forward neural network enabled resource allocation for 6G wireless networks Kamble, Pradnya; N. Shaikh, Alam
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp811-818

Abstract

The 6G wireless networks utilize terahertz (THz) frequency and intended to tremendously dynamic and diverse applications with deep learning enabled network, harvested significant attention and able to solve complex problems. Efficient resource allocation is a key requirement of next generation wireless networks. This research focuses on the resource allocation optimization challenge which includes storage, computing power, bandwidth and memory in the milieu of 6G wireless networks with device-to-device (D2D) communication enabled. The proposed model uses modified long short-term memory (mLSTM) and feed forward neural network to allocate resources to various tasks as per requirement such as information access, audio/video streaming, information access and productivity activity applications. The proposed work focuses on network parameters like channel noise, signal to noise ratio (SNR), distance from base station and includes D2D communication decisions to improve network performance. This research gives a novelty learning based solution for resource allocation for 6G wireless networks which contributes to the enhancement of next generation wireless communication networks. The lowest computing power utilized is 1%, Bandwidth utilized is 3% of total bandwidth and 2% storage.
Improved Bi-GRU for parkinson’s disease severity analysis Arunachalam, Malathi; Ramar, Ramalakshmi; Gandhi, Vaibhav; Ananthan, Bhuvanesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1140-1149

Abstract

Parkinson’s disease (PD) is a common neuro-degenerative issue, evaluated via the continuous deterioration of motor functions over time. This condition leads to a gradual decline in movement capabilities. For diagnosing clinical set of PDs, medical experts utilize medical observations. These observations are highly based on the expert’s experience and can vary among clinicians due to its subjective nature, leading to differences in evaluation. The gait patterns of individuals with PD typically exhibit distinctions from those of adults. Evaluating these gait malformations not only aids in diagnosing PD but can also enable the categorization of severity stages with respect to symptoms of motor movement. Therefore, this paper introduces a classification of gait model based on the optimized deep learning (DL) model bidirectional gated recurrent unit-artificial hummingbird optimizer (BI-GRU-AHO). The training and testing involved the sequential segmentation of the right and left instances from the signals of vertical ground reaction force (VGRF) based on the identified gait cycle. The outcomes of the proposed BI-GRU-AHO exhibits reliable and accurate assessment of PD and achieved better accuracy of 98.7 %. The proposed model is trained and tested satisfactorily; hence it can be implemented in a real-time environment by integrating the model into a software application or system capable of receiving real-time data from PD patients.
Digital afterlife: challenges and technological innovations in pursuit of immortality Ouhnni, Hamid; Ziti, Soumia; El Bouchti, Karim; Meryam, Belhiah; Lagmiri, Souad Najoua
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1387-1406

Abstract

Digital immortality, the idea of endless life and ultimate happiness in a virtual afterlife, has become a subject of human fascination. This article reports the results of a comprehensive research project focused on identifying the challenges and potential options related to digital immortality. Analyzing 39 relevant studies, our research concentrates on two main themes: the barriers to achieve the digital immortality and the tools created to preserve digital memories. Our findings reveal that the challenges associated with digital immortality are deeply rooted in legal, ethical, and social issues. Importantly, our focus is the challenges related to digital content left by the deceased, its collection method, and integrity in digital immortality research, as content forms the basis for achieving this objective. Furthermore, the research highlights the need for more advanced technology, as the number of studies is limited and current progress is primarily future-oriented. However, our analysis demonstrates that the digital content left by the deceased is paramount, as it constitutes the raw material for achieving digital immortality.
Four quadrant operation of bidirectional DC-DC converter for light electric vehicles Ann Sam, Caroline; Jegathesan, Varghese
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp740-748

Abstract

This paper discusses the closed-loop control of a bidirectional full bridge DC-DC converter which aids in the four-quadrant operation of an electric vehicle (EV). Several topologies of bidirectional converters have been recently investigated for optimizing vehicle performance. The bidirectional converters with buck and boost modes of operation aid the four-quadrant operation of drives. The proposed bidirectional converter aids buck and boost modes of operation in both forward and reverse directions of the drive. The buck/boost operation in the forward direction is suitable to operate the traction drive in motoring mode. Also, the buck/boost operation in the reverse direction aids the drive to operate in charging mode. The performance analysis of the bi-directional converter-fed EV drive is done using MATLAB/Simulink software. The different modes of operation of the converter which is utilized for the four-quadrant operation of the drive are validated using a 12-60V hardware prototype. DSP TMS2837D controller is used to control the bi-directional converter and the code generation for the controller is done in MATLAB-DSP integrated platform. The hardware results validate theoretical analysis and simulation studies.
Detection of diabetic retinopathy and classification of its stages by using convolutional neural network Gaur, Sachin; Kandwal, Anirudh; Pandey, Bhaskar
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1284-1293

Abstract

Diabetes detection is pivotal in disease management and complication prevention. Traditional screening methods, like blood tests, are invasive and time-consuming. Deep learning has emerged as a non-invasive and automated alternative for diabetes detection. Convolutional neural networks (CNNs) excel in image analysis tasks, making them ideal for this purpose. This paper employs a CNN-based method for diabetes prediction using retinal images, utilizing the DenseNet169 architecture for feature extraction and diabetic retinopathy (DR) prediction. The APTOS 2019 blindness detection dataset from Kaggle, containing around 13,000 retinal images, is used for training. Pre-processing and normalization precede feature extraction, followed by the prediction of the DR stage. The model aims to classify retinal images into five stages of DR (0 to 4), ranging from no DR to proliferative DR. Our model achieved over 82% accuracy, outperforming advanced algorithms. Model evaluation includes accuracy, precision, recall, and F1 score measures.
Early skin disease diagnosis by using artificial neural network for internet of healthcare things Wan Bejuri, Wan Mohd Yaakob; Mohamad, Mohd Murtadha; Tang, Michelle; Ahmad Khair, Aina Khairina; Adriyansyah, Yusuf Athallah; Kasmin, Fauziah; Tahir, Zulkifli
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1032-1041

Abstract

Internet of healthcare things (IoHT) represents a burgeoning field that leverages pervasive technologies to create technology driven environments for healthcare professionals, thereby enhancing the delivery of efficient healthcare services. In remote and isolated areas, such as rural communities and boarding schools, access to healthcare professionals (especially dermatologists) can be particularly challenging. However, these areas often lack the specialized expertise required for effective skin disease consultations. Thus, the purpose of this research is to design a scheme of early skin disease diagnosis for internet of healthcare things that is accessible anywhere and anytime. In this research, the image of skin disease from patient will be taken by using a mobile phone for predicting and identifying the disease. This proposed scheme will diagnose skin disease and convert it be meaningful information. As a result, it show our proposed scheme can be the most consistent in term of accuracy and loss compared to others method. Overall, this research represents a significant step toward improving healthcare accessibility and empowering individuals to manage their own health. Furthermore, the proposed scheme is anticipated to contribute significantly to the IoHT field, benefiting both academia and societal health outcomes.
Compressor performance prediction: gradient boosting regression model and sensitivity analysis Liao, Kuo-Chien; Wu, Hom-Yu; Wen, Hung-Ta; Sung, Jui-Tang; Hidayat, Muhamad; Wang, Will Wei-Juen
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1201-1208

Abstract

This study introduces the use of gradient boosting regression (GBR) models to estimate the compressor performance of aero-engines. The model exhibits a mean absolute error (MAE) of 0.078, showcasing superior performance compared to previous studies. Through sensitivity analysis, optimal values for three key parameters were determined: 280 estimators, a max depth of 9, and a learning rate of 0.085. Furthermore, a comparison with a prior study revealed an impressive MAE value lower than 0.002, highlighting the GBR model’s success in accurately predicting compressor performance. This demonstrates the model’s effectiveness and predictive accuracy, making it a valuable tool for aero-engine compressor performance estimation.

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