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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 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. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 2,901 Documents
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Optimizing neural radiance field: a comprehensive review of the impact of different optimizers on neural radiance fields Pinjarkar, Latika; Nittala, Aditya; P. Mattada, Mahantesh; Pinjarkar, Vedant; Neole, Bhumika; Kogundi Math, Manisha
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8315

Abstract

Neural radiance field (NeRF) is a form of deep learning model that may be used to depict 3D scenes from a collection of photos. It has been demonstrated that NeRF can produce photorealistic photographs of fresh perspectives on a scene even from a small number of input images. However, the optimizer that is employed can have a significant impact on the quality of the final reconstruction. Finding an effective optimizer is one of the biggest challenges while learning NeRF models. The optimizer is responsible for making changes to the model's parameters to minimize the discrepancy between the model's predictions and the actual data. We cover the many optimizers that have been used to train NeRF models in this study. We present research results contrasting the effectiveness of multiple optimizers and examine the benefits and drawbacks of each optimizer. For training NeRF models, four different optimizers viz. Adaptive moment estimation (Adam), AdamW, root mean square propagation (RMSProp), and adaptive gradient (Adagrad) are trained. The most effective optimizer for a given assignment will vary depending on a variety of elements, including the size of the dataset, the complexity of the scene, and the level of accuracy that is required.
X-band and Ku-band PIN diode loaded reflectarray unit cells with adaptive frequency switching Inam Abbasi, Muhamamad; Dali Khan, Sher; Hashim Dahri, Muhammad; Mohd Ibrahim, Imran; Hafizah Sulaiman, Noor
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8798

Abstract

The fast advancement of intelligent new applications has led to the creation of high-performance antennas. Reflectarrays (RAs), also known as planar reflectors, are seen as promising antennas for several such modern-day applications. This work presents a comprehensive investigation of frequency switchable RA antennas operating in the X-band and Ku-band frequency ranges. Various strategic configurations of combined slots have been suggested, using integrated P-layer, I-layer, and N-layer (PIN) diodes, with the purpose of creating unit cells in RAs that may switch frequencies and exhibit a gradual change in phase distribution. The frequency variation achieved in X-band for the ON state of PIN diodes is from 8.13 GHz to 11.69 GHz, whereas for the OFF state it is from 8.13 GHz to 11.68 GHz. Similarly, for Ku-band ON and OFF states of PIN diodes provided frequency variations of 13.6 GHz to 17.1 Ghz and 12.8 Ghz to 16.6 GHz respectively. Frequency tunability of 0.85 GHz and 0.72 GHz has been successfully achieved in X-band and Ku-band.
Distributed denial-of-service attack detection short review: issues, challenges, and recommendations Ahasan Habib, AKM; Imtiaz, Ahmed; Tripura, Dhonita; Omar Faruk, Md.; Anwar Hossain, Md.; Ara, Iffat; Sarker, Sohag; Zainul Abadin, A F M
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8377

Abstract

An attacker can attack a network in several methods when there are a lot of device connections. Distributed denial-of-service (DDoS) attacks could result from this circumstance, which could damage resources and corrupt data. Therefore, irregularity in traffic data must be detected to identify malicious behavior in a network, which is critical for maintaining the integrity of current cyber-physical systems (CPS) as well as network security. This article attempts to study and compare various approaches to detecting DDoS attacks and expresses data paths for packet filtering for high-speed networks (HSN) performance, using machine or deep learning techniques used in intrusion detection systems (IDSs) and flow-based IDSs. The study presents a comprehensive DDoS attack taxonomy, categorizes detection strategies, and highlights the HSN accuracy assessment features. By exposing the problems and difficulties associated with DDoS attacks on HSN, several investigation paths are proposed to assist researchers in determining and developing the best solution.
Intrusion detection system in lightweight devices: issues and challenges Musa Shanono, Nuruddeen; Muslim, Zulkiflee; Azman Abu, Nur; Rahayu Selamat, Siti; Nahar, Haniza
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8038

Abstract

Intrusion detection system (IDS) is a crucial component in ensuring the security of computer networks. It helps in identifying and responding to unauthorized access attempts or malicious activities within a network. The focus of this systematic review is on IDS specifically designed for lightweight devices. This systematic review aims to provide an abstract understanding of the current state of IDSs for lightweight devices. It involves a comprehensive analysis of existing research papers, evaluating the methodologies, techniques, and performance metrics used in these IDS solutions. The goal of the systematic review is to provide a critical assessment and analysis of the literature on IDS in lightweight devices, closing the research gap in this field. The review analyzed and evaluated 55 studies out of 678 initially identified. The findings of the study are presented in the paper, which includes insights into the state-of-the-art proposals in the field, challenges and limitations of existing solutions, and recommendations for future research directions. The outcome of this paper can help the advancement of IDS for lightweight devices.
Analyzing 5G performance: investigating altitude-induced variations Daengsi, Therdpong; Sriamorntrakul, Pakkasit; Chatchalermpun, Surachai; Phanrattanachai, Kritphon
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8425

Abstract

Since the launch of fifth generation (5G) services in Thailand in 2020, there have been continuous improvements in 5G coverage. Currently, 5G coverage extends to most areas throughout the country. However, coverage issues persist not only in rural areas but also in high-rise buildings in urban areas. Consequently, a study was conducted within such buildings. This paper assesses the performance of 5G at different altitude test points. The chosen location for the field tests was a high-rise building within a crowded public hospital, which receives numerous patients every weekday, in the major urban area of Bangkok. Two smartphones from the same manufacturer, both supporting 5G technology and equipped with the Speedtest application, were employed as tools for this study. Tests were carried out on the third and twenty-fourth floors of the high-rise building for data collection. The primary finding of this study reveals that download speeds exhibited a significant decrease with increasing altitude of the test points, as evidenced by statistical analysis (p-values0.001). This implies an issue with altitude-induced variations, indicating a need for the improvement of indoor 5G coverage in high-rise buildings.
Metamaterial inspired miniaturized ultra-wideband monopole hexagonal antenna with triple band-filter functions Elhabchi, Mourad; Bour, Mohamed; Atouf, Issam; Zaarane, Abdelmoghit
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7568

Abstract

In this letter, a new technique to the design of an ultra-wideband (UWB) monopole hexagonal antenna with triple band-rejected functions and to restrict the interferences with the exist bands is proposed, the design has the form of a hexagonal patch and a ground plane having rectangular shaped etched in the back side of the substrate to achieve the UWB behavior. The triple-band filter feature is generated by inserting a metamaterial (MTM) as a split ring resonator slots (SRRs) and a complementary split ring resonators (CSRRs) strip, thus no extra size is needed. The triple band-elimination is for 3.3-3.9 GHz centered at 3.5 GHz for 5G band, 4.99-5.4 GHz centered at 5.2 GHz for wireless local area network (WLAN) band, and 6.2-6.8 GHz centered at 6.5 GHz for IEEE INSAT/Supra-extended C-band. The antenna dimension has a compact size of 20×25×1.6 mm3. Current distribution on the antenna is used to analyze the effect of MTMs on the antenna operations. The simple structure and small size of the antenna makes it suitable for most of the wireless communication systems.
Optimal deployment of solar PV power plants as fast frequency response source for a frequency secure low inertia power grid K. Wamukoya, Brian; K. Kaberere, Keren; M. Muriithi, Christopher
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8548

Abstract

Modern power systems are witnessing increased uptake of solar photovoltaic power plants (SPVPPs) replacing conventional synchronous generators (SGs). SPVPPs lack any rotating parts resulting in no natural rotational inertia contribution to the grid. Reduced inertia makes the power system more dynamic, making it susceptible to frequency instability caused by minor disturbances. This problem is majorly addressed by limiting the penetration of SPVPPs to ensure a minimum level of critical inertia is maintained or by providing additional virtual inertia from an energy storage system. However, the SPVPPs can be configured to operate below maximum power point tracking (MPPT) (deloaded mode) to provide a reserve capacity that can rapidly be deployed as fast frequency response (FFR) in case of a frequency event. This paper presents a strategy to optimize the FFR capacity of a deloaded SPVPP using particle swarm optimization (PSO) algorithm. DIgSILENT PowerFactory was used to model the deloaded SPVPP and run time domain simulations. PSO algorithm was implemented using a Python script in PowerFactory. The proposed strategy was applied on a modified IEEE 39 bus test system. The results show that optimal deloading of SPVPP can help to successfully arrest frequency decline, reduce power curtailment while adhering to the prescribed constraints.
Progression of polymeric nanostructured fibres for pharmaceutical applications Abu Owida, Hamza; I. Al-Nabulsi, Jamal; M. Turab, Nidal; Al-Ayyad, Muhammad; Alazaidah, Raed; Alshdaifat, Nawaf
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7315

Abstract

Electrospinning has emerged as a simple and cost-effective technique for producing polymer nanofibers, offering a versatile approach for creating nanostructured fibers from a wide range of polymer materials. The pharmaceutical field has particularly welcomed the advent of electrospun nanofibers, as they hold immense potential for revolutionizing drug delivery systems. The recent surge of interest in electrospun nanofibers can be attributed to their unique characteristics, including elasticity and biocompatibility, which make them highly suitable for various biomedical applications. By incorporating functional ingredients into blends of nanostructured fibers, the capabilities and reliability of drug delivery devices have been significantly enhanced. This review aims to provide a comprehensive summary of recent research endeavors focusing on the concept of nanofibrous mesh and its multifaceted applications. With an emphasis on the simplicity of fabrication and the virtually limitless combinations of materials achievable through this approach, nanofibrous meshes hold the promise of transforming specific treatment modalities. By streamlining the delivery of therapeutic agents and offering enhanced control over drug release kinetics, nanofibrous meshes may herald a new era in targeted and personalized medicine.
Chronic disease prediction chatbot using deep learning and machine learning algorithms Sia, Mandy; Ng, Kok-Why; Haw, Su-Cheng; Jayaram, Jayapradha
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8462

Abstract

Ever since the rise of human civilization, more and more diseases have been discovered with the rapid growth of medical knowledge. This sheer volume of information makes it hard for humans to memorize or even utilize it efficiently. Thus, machine learning emerged as a powerful tool for complex calculations by offering a solution to this challenge. This paper intends to use deep learning and machine learning algorithms to develop a predictive model that can recognize potential diseases based on symptoms. The model is then seamlessly integrated into a text-based disease prediction assistant chatbot that serves as a communication platform between the users and the system. The algorithms researched for the disease prediction models are k-nearest neighbours (KNN), support vector machines (SVM), random forest, and neural networks. After that, a chatbot application is created by integrating long short-term memory (LSTM), natural language toolkit (NLTK) libraries, and Telegram. As a result, the SVM models demonstrated excellent performance by achieving an accuracy of 92.24%, closely followed by random forest with 92.23%, KNN with 91.57%, and artificial neural network (ANN) with 91.52% accuracy. In short, this paper presents a potential solution for a more accurate disease prediction tool by implementing the best disease prediction model with the chatbot models together.
A stereo-vision system for real-time person detection in ADAS applications using a fine-tuned version of YOLOv5 Rachidi, Oumayma; Ed-Dahmani, Chafik; Bououlid Idrissi, Badr
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8417

Abstract

Pedestrian detection holds significant importance in advanced driver assistance systems (ADAS) applications, and presents a challenging task in this field. While the advent of deep learning has facilitated the introduction of various pedestrian detectors characterized by accuracy and low inference speed, there persists a need for further improvements. Notably, ADAS requires accurate detection of pedestrians in various environmental conditions that can adversely impact the model’s performance, such as poor lighting, and bad weather. Furthermore, an imperative requirement involves the incorporation of distance estimation in conjunction with pedestrian detection, with an extension of detection capabilities to encompass cyclists and riders, who are equally crucial for ensuring road safety. Therefore, this paper introduces a stereovision system designed for the detection of pedestrians, cyclists, and riders. The initial phase, involves improving the performance of you only look once version 5 (YOLOv5s) through a fine-tuning process with a custom dataset integrating augmentation techniques to common objects in context (COCO) dataset. The detector is trained using Google Colab, and tested in real-time with a Raspberry Pi 4 model B, 8 G RAM. A comparative analysis is conducted between the YOLOv5s and the fine-tuned model to prove the accuracy of our approach. The results showcase a high performance of the detector reaching an accuracy exceeding 79%.

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