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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 9,138 Documents
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Optimized in-loop filtering in versatile video coding using improved fast guided filter Lakshmi Amrutha Valli Pamidi; Purnachand Nalluri
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp911-919

Abstract

Devices with varying display capabilities from a common source may face degradation in video quality because of the limitation in transmission bandwidth and storage. The solution to overcome this challenge is to enrich the video quality. For the mentioned purpose, this paper introduces an improved fast guided filter (IFGF) for the contemporary video coding standard H.266/VVC (versatile video coding), a continuation of H.265/HEVC (high efficiency video coding). VVC includes several types of coding techniques to enhance video coding efficiency over existing video coding standards. Despite that, blocking artifacts are still present in the images. Hence, the proposed method focuses on denoising the image and the increase of video quality, which is measured in terms of peak signal-to-noise (PSNR). The objective is achieved by using an IFGF for in-loop filtering in VVC to denoise the reconstructed images. VTM (VVC test model)-17.2 is used to simulate the various video sequences with the proposed filter. This method achieves a 0.67% Bjontegaard delta (BD)-rate reduction in low-delay configuration accompanied by an encoder run time increase of 4%.
Forecast earthquake precursor in the Flores Sea Adi Jufriansah; Ade Anggraini; Zulfakriza Zulfakriza; Azmi Khusnani; Yudhiakto Pramudya
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1825-1836

Abstract

Artificial intelligence (AI) can use seismic training data to discover relationships between inputs and outcomes in real-world applications. Despite this, particularly when using geographical data, it has not been used to predict earthquakes in the Flores Sea. The algorithm will read the seismic data as a pattern of iterations throughout the operation. The output data is created by grouping based on clusters using the most effective WCSS analysis, while the input features are derived from the original international resource information system (IRIS) web service data. Given that earthquake prediction is an effort to reduce seismic disasters, this research is essential. By generating predictions, it can reduce the devastation caused by earthquakes. Using the support vector machine (SVM), hyperparameter support vector machine (HP-SVM), and particle swarm optimization support vector machine (PSO-SVM) algorithms, this study seeks to forecast the Flores Sea earthquake. According to the estimation results, the SVM algorithm’s evaluation value is less precise than that of the HP-SVM, especially the linear HP-SVM and HP-SVM Polynomial models. However, the HP-SVM RBF model’s accuracy rating is identical to that of the traditional SVM model. The improvement of the PSO-SVM model, which has the finest gamma position and a value of 9.
Large file encryption in a reduced-round permutation-based AES file management system Jerico S. Baladhay; Heidilyn V. Gamido; Edjie M. De Los Reyes
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp2021-2031

Abstract

In the rapid evolving digital landscape, the imperative to ensure data security has never been more crucial. This paper addresses the pressing challenges in data security by introducing a file encryption management system, leveraging a modified advanced encryption standard (AES) algorithm with reduced round iterations and bit permutation. This system aims to comprehensively secure various file types, providing a dependable solution for file exchange. Our findings reveal substantial improvements in both encryption and decryption processes using the reduced-round permutation-based AES (RRPBA). The adapted algorithm demonstrates a significant 38.8% acceleration in encryption time and a remarkable 44.86% improvement in decryption time, positioning it as a pivotal component for efficient file operations within the management system. Moreover, the throughput assessments showcase a remarkable 33.73% improvement in encryption and 23.72% in decryption, outperforming the original AES, emphasizing the algorithm's superior computational effectiveness, signaling positive implications for future high-performance applications. In conclusion, the study not only addresses critical security challenges but also presents a viable solution with tangible speed advantages for file encryption and decryption processes within digital file management systems.
Two-stage HOG/SVM for license plate detection and recognition Lakhdar Djelloul Mazouz; Abdelkrim Meche; Abdelaziz Ouamri; Abdel Wahab Ait Darna
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp210-223

Abstract

Automatic license plate recognition (ALPR) is one of the technologies used in intelligent transport systems (ITS) to read vehicle license plates automatically. The extracted information has various potential applications, including but not limited to an electronic payment gateway, a system for paying parking fees, road surveillance, and managing traffic flow. In this paper, we propose an efficient method to detect and identify the Algerian license plate (LP). This method consists of a two-stage algorithm that combines the histogram of oriented gradients (HOG) with the support vector machine (SVM) classifier. The purpose of the first stage of HOG/SVM is the detection of the LP, while the recognition of the digits is accomplished by the second stage of HOG/SVM. As first contribution, a dataset of standard Algerian LP not available elsewhere is built (DZLP dataset), The second is a proposal of a very efficient pre-processing step for LP detection and digit recognition. Experimental results show that the proposed approach yields very high license plate and average digits recognition rates, which of 97.5% and 99.46%, respectively.
Review of battery models and experimental parameter identification for lithium-ion battery equivalent circuit models Nouhaila Belmajdoub; Rachid Lajouad; Abdelmounime El Magri; Soukaina Boudoudouh; Mohamed Hicham Zaggaf
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1336-1346

Abstract

The growing use of electric vehicles has led to an ever-increasing demand for efficient and reliable management systems to control the behavior of lithium-ion batteries, especially with respect to heat generation and state-of-charge. Understanding these patterns constitutes a major new challenge for these batteries, as remaining ignorant of their behavior can result in decreased performance, shorter service life and even safety dangers. This review provides an overview of the different modeling techniques applied to simulate battery behavior. Different methods using equivalent electrical circuit models are discussed, covering both simple battery models and more complex equivalent electrical circuit models, with a focus on the 2RC-Thévenin circuit model. In this context, parameter approach methods for these systems are reviewed. In addition, laboratory tests are run to identify the various model parameters for a lithium-ion battery. This comprehensive study is designed to guide scientists and engineers in the selection and use of suitable tools for state-of-charge and battery health studies.
Sizing and analysis of a standalone photovoltaics system for a three-bedroom residence in Nigeria Chibuike Peter Ohanu; Godson Nnamdi Egbo; Tole Sutikno
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp1-9

Abstract

The intermittency of electricity supply from conventional sources, increase in fuel prices and constant emission of greenhouse gases by non-renewable energy sources are major challenges faced by energy users. Energy from reneweable sources have advantages over the traditional (non-renewable) sources of energy. This paper presents the sizing and analysis of a standalone photovoltaic (PV) system for a 3-bedroom residence situated at Obollo-Nsukka (6.876°N, 7.403°E, 389 m) in Nigeria. The energy requirements of such a residence are 8.14 kWh/day and analysis have shown that the cost of constructing the PV system is ₦2,838,040 Nigeria naira (NGN). The cost of maintaining such a system within a lifetime of 20 years is between 159,328 NGN/year to 1,895,918 NGN/year. Comparing the levelized cost of energy (LCOE) of enugu electricity distribution company (EEDC) which is 66.5 NGN/kWh to the LCOE of the standalone PV system which is between 102,124 NGN/kWh to 419 NGN/kWh it was found out that the cost of electricity from the PV system is more than that of the conventional grid. The PV system provides feasible solution to the intermittency issues of the conventional grid in Nigeria. Hence, this technology only technically viable for residential electrification purposes in Nigeria.
Malaria cell identification using improved machine learning and modified deep learning architecture S., Shashikiran; H. D., Sunitha
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp2078-2086

Abstract

Malaria continues to be a serious problem for public health because of its occurrence in tropical and subtropical areas with inadequate healthcare systems and few resources. For prompt intervention and treatment of malaria, effective and precise diagnosis is essential. Professional pathologists examine blood smear films by hand to get a microscopic diagnosis and another way they will do a rapid antigen malaria test which produces the result of 50% accuracy. Convolutional neural network (CNN) is a type of deep learning (DL) model that has been effectively used for a variety of image recognition applications. Our suggested approach uses, improved machine learning (IML) methods like support vector machine (SVM)+principal component analysis (PCA) fit, SVM+t-distributed stochastic neighbor embedding (t-SNE) fit, and CNN architecture with an accuracy of 86.23%, 88.27%, and 97.16% accuracy respectively, to combine feature extraction, data augmentation, and modify the layers by including the SVM algorithm in the final layer of the CNN architecture. The proposed method will significantly reduce pathologists' burden by automating the identification of malaria and improving diagnosis accuracy in resourceconstrained contexts.
Mysore sentinel-2: deep learning for image classification with optimizer exploration Sathyanarayana, Natya; Singh, Seema

Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp647-657

Abstract

The classification of Sentinel-2 image is presented in this work using a tile based methodology. The Mysore district of India's Karnataka state serves as the subject region of this research. By tiling Sentinel-2 images, we were able to construct a distinct dataset and get approximately 3,000 training samples for the five classes. These images are manually labelled and geo-referenced. Three different optimizers were employed in a thorough analysis with deep learning models such as ResNet50, MobileNetV2, ShuffleNet, and VGG16 to achieve better performance metrics. With a classification accuracy of 98.1% on RESNet50 using ADAM surpassed the others. This facilitates investigating various geographical data analytics applications of the study region.
Improved DAG in blockchain tangle for IOTA Eugene Rhee; Jihoon Lee
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp806-813

Abstract

The internet of things (IoT) enables machine-to-machine communication without human intervention. Consequently, every object connected to the internet can exchange information with each other. Internet of things application (IOTA) has undertaken a project to address the high transaction fees inherent in traditional blockchain systems and enhance the efficiency of microtransactions between machines by combining blockchain and IoT. IOTA employs its unique Tangle technology, which introduces a novel transaction consensus method, addressing the fee issues, limited scalability, and the inability to conduct offline transactions associated with traditional blockchains. This paper provides a detailed overview of the characteristics of the Tangle structure and the concepts applied in IOTA. Additionally, it explores potential approaches for integrating blockchain into IoT.
Novel printed passive ultra high frequency radio frequency identification antenna using meander technique Latifa El Ahmar; Ahmed Errkik; Jamal Zbitou; Ilham Bouzida; Aziz Oukaira; Ahmed Lakhssassi
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1474-1485

Abstract

In this paper, a novel radio frequency identification (RFID) antenna using a meander technique associated with a slotted patch is studied for RFID applications in the ultra high frequency (UHF) band [867.5-868 MHz]. The proposed RFID antenna is designed on a Kapton substrate with dielectric constant 3.5 and loss of 0.0027. It consists of two opposite meander line antennas of 8 turns each one and interconnected to ALIEH H3 microchip associated to two slotted patch’s with a global size 105×25×0.1 mm3. The proposed RFID antenna is designed and simulated using CST MWS as an electromagnetic solver. The results of the simulation show a return loss of -22.64 dB at 868 MHz, a reading distance of around 5 m, and a simulated input impedance of the antenna are 31.72+j109.68 Ω at the operating frequency 868 MHz.

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