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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 73 Documents
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Search results for , issue "Vol 13, No 2: April 2024" : 73 Documents clear
Design and implementation of energy-efficient hybrid data aggregation in heterogeneous wireless sensor network Al-Heeti, Mohamed Muthanna; Hammad, Jamal A.; Mustafa, Ahmed Shamil
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.5582

Abstract

Heterogeneous wireless sensor network (HWSN) is a trending technology in both the industrial and academic sectors, consisting of a large number of interconnected sensors. However, higher energy consumption and delay are significant drawbacks of this technology in applications such as military, healthcare, and industrial automation. The main objective of this research is to enhance the energy efficiency of HWSN using a clustering technique. In this article, a novel approach, namely power optimization and hybrid data aggregation (POHDA), is proposed to address these challenges in HWSN. POHDA-HWSN focuses on power optimization and congestion avoidance through effective CH selection using hybrid data aggregation based on parameters such as residual energy, distance, mobility, threshold value of the node, and latency. By weight-based effective cluster head (CH) selection, the energy consumption, end-to-end delay, and overhead during communication are reduced in this network. The POHDA-HWSN approach considers specific parameters to compare the results and outcomes with earlier research such as HCCS-WSN, FMCA-WSN, and APCC-WSN. The results prove that the proposed POHDA-HWSN approach achieves higher energy efficiency and delivery ratio.
The development and usability test of an automated fish counting system based on CNN and contrast limited histogram equalization Leong, Jing Mei; Ahmad Hijazi, Mohd Hanafi; Saudi, Azali; Kim On, Chin; Fui Fui, Ching; Haviluddin, Haviluddin
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.5840

Abstract

The aquaculture industry has rapidly grown over the year. One pertinent aspect is the ability of the aquaculture farm management to accurately count the fish populations to provide effective feeding and the control of breeding density. The current practice of counting the fish manually increased the hatchery workers workload and led to inefficiency. The presented work proposed an intelligent, web-based fish counting system to assist hatchery workers in counting fish from images. The methodology consists of two phases. First, an intelligent fish counting engine is developed. The captured image was first enhanced using the contrast limited adaptive histogram equalization. A deep learning architecture in the form of you only look once (YOLO)v5 is used to generate a model to identify and count fish on the image. Second, a web-based application is developed to implement the developed fish counting engine. When applied to the test data, the developed engine recorded a precision of 98.7% and a recall of 65.5%. The system is also evaluated by hatchery workers in the University Malaysia Sabah fish hatchery. The results of the usability and functionality evaluations indicate that the system is acceptable, with some future work suggested based on the feedback received.
Harmonics mitigation technique for asymmetrical multilevel inverter fed by photovoltaic sources Ali, Ali Riyadh; Antar, Rakan Khalil; Abdulrazzaq Abdulghafoor, Abdul Ghani
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6607

Abstract

A multilevel inverter is an electrical device that converts a DC voltage into a higher AC voltage by generating a stepped waveform with several voltage levels. Unlike traditional inverters that produce a square wave or a pulse-width modulated (PWM) waveform with only two voltage levels, multilevel inverters can generate waveforms with three or more levels, resulting in reduced harmonic distortion, improved efficiency, and decreased electromagnetic interference. The design and control of multilevel inverters are active research areas that aim to enhance their performance, reliability, and scalability. In this research, a 31-level asymmetric cascaded multilevel inverter is suggested. The proposed multilevel inverter (MLI) system employs four photovoltaic cells as dc sources with structure of (1:2:4:8) Vdc. The system is modeled by MATLAB/Simulink and total harmonic distortion (THD) values of the output voltage and current are 1.106% for resistive load, and 1.35% and 0.403% for inductive load. These outcomes demonstrate the recommended circuit's efficacy and demonstrate its suitability for medium- and high-power applications.
Challenges in data representation for efficient execution of encryption operation Afendee Mohamed, Mohamad; Garba Shawai, Yahaya; Almaiah, Mohammed Amin; Derahman, Mohd Noor; Lutfi, Abdalwali; Abu Bakar, Khairul Azmi
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.5437

Abstract

Big number operation has always been a bottleneck to computer system as it imposes high demand on computing power. With a limited power available, operations such as exponentiation and multiplication involving large integer belonging to encryption process requires grave scrutiny. One way to address this issue is by replacing an original complex computation into a sequence of small computations that in the end produces the same results. This paper takes an evolutionary approach to survey numerous articles that have contributed to the advancement of integer representation. Numerous representations were proposed, those that come into play concentrated on reducing non-zero digits and limiting non-zero spacing other than allowing subtraction operation. A comparison was made to distinguish the properties of each method from the others. This detailed outlook can be a guide for identifying the correct representation to be chosen for implementation within specific application.
Identifying deoxyribonucleic acids of individuals based on their chromosomes by proposing a special deep learning model Omar Al-Nima, Raid Rafi; Salahaldeen Al-Kaltakchi, Musab Tahseen; A. Abdulla, Hasan
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6198

Abstract

One of the most significant physiological biometrics is the deoxyribonucleic acid (DNA). It can be found in every human cell as in hair, blood, and skin. In this paper, a special DNA deep learning (SDDL) is proposed as a novel machine learning (ML) model to identify persons depending on their DNAs. The proposed model is designed to collect DNA chromosomes of parents for an individual. It is flexible (can be enlarged or reduced) and it can identify one or both parents of a person, based on the provided chromosomes. The SDDL is so fast in training compared to other traditional deep learning models. Two real datasets from Iraq are utilized called: Real Iraqi Dataset for Kurd (RIDK) and Real Iraqi Dataset for Arab (RIDA). The results yield that the suggested SDDL model achieves 100% testing accuracy for each of the employed datasets.
Image dermoscopy skin lesion classification using deep learning method: systematic literature review Nugroho, Arief Kelik; Wardoyo, Retantyo; Wibowo, Moh Edi; Soebono, Hardyanto
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6077

Abstract

Classifying skin lesions poses a significant challenge due to the distinctive characteristics and diverse shapes they can exhibit, particularly in identifying early-stage melanoma. To address the shortcomings of the prior method, a neural network-driven strategy was introduced to differentiate between two types of skin lesions based on dermoscopic images. This new approach comprises four key stages: i) initial image processing, ii) skin lesion segmentation, iii) feature extraction, and iv) classification using deep neural networks (DNNs). Computers can also provide more accurate diagnosis results. In the review process, the articles are analyzed and summarized to contribute to developing methods or application development in skin lesion diagnosis. The stages include defining the relevant theory, input data, methods used (architecture and modules), training process, and model evaluation. This review also explores information based on trends and users, emphasizing the skin lesion segmentation process, skin lesion classification process, and minimal datasets as recommendations for future research.
Miniaturization of antenna using metamaterial loaded with CSRR for wireless applications Pande, Suyog V.; Patil, Dipak P.
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6362

Abstract

This paper proposes a compact decagon antenna for wireless applications based on inspired metamaterial (MTM) loaded with a modified complementary split ring resonator (CSRR). A MTM loaded with CSRR is used to achieve a size reduction of 50% when compared to a traditional antenna. The suggested decagon antenna's ground plane has been loaded with CSRR. The antenna was made on an FR4 substrate with a thickness of 1.6 mm and εr=4.4 and has a very small dimension of 0.288 λ_0x0.272 λ_0x0.013 λ_0 (where λ_0 represent center frequency at 2.4 GHz). The given antenna has a 90 MHz bandwidth (2.40-2.50 GHz) with a peak gain of 2.36 dB. The presented design is validated by showing simulated results of the S parameter, VSWR, gain, surface current, and radiation pattern. The proposed antenna is well suited for wireless applications.
Portable smart attendance system on Jetson Nano Yose, Edward; Victor, Victor; Surantha, Nico
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6061

Abstract

The masked face recognition-based attendance management system is an important biometric-based attendance tracking solution, especially in light of the COVID-19 pandemic. Despite the use of various methods and techniques for face detection and recognition, there currently needs to be a system that can accurately recognize individuals while they are wearing a mask. This system has been designed to overcome the challenges of widespread mask use, impacting the effectiveness of traditional face recognition-based attendance systems. The proposed system uses an innovative method that recognizes individuals even while wearing a mask without the need for removal. With its high compatibility and real-time operation, it can be easily integrated into schools and workplaces through an embedded system like the Jetson Nano or conventional computers executing attendance applications. This innovative approach and its compatibility make it a desirable solution for organizations looking to improve their attendance-tracking process. The Experimental results indicates using maximum resources possible the execution time needed on Jetson Nano is 15 to 22 seconds and 14 to 18 seconds respectively and the average frame capture if there are at least one face detected on Jetson Nano is 3-4 frames.
Exploiting channel state information of WiFi signal for human activity detection: an experimental study Boudlal, Hicham; Serrhini, Mohammed; Tahiri, Ahmed
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6344

Abstract

Ubiquitous computing aims to seamlessly integrate computing into our daily lives, and requires reliable information on human activities and state for various applications. In this paper, we propose a device-free human activity recognition system that leverages the rich information behind WiFi signals to detect human activities in indoor environments, including walking, sitting, and standing. The key idea of our system is to use the dynamic features of activities, which we carefully examine and analyze through the characteristics of channel state information. We evaluate the impact of location changes on WiFi signal distribution for different activities and design an activity detection system that employs signal processing techniques to extract discriminative features from wireless signals in the frequency and temporal domains. We implement our system on a single off-the-shelf WiFi device connecting to a commercial wireless access point and evaluate it in laboratory and conference room environments. Our experiments demonstrate the feasibility of using WiFi signals for device-free human activity recognition, which could provide a practical and non-intrusive solution for indoor monitoring and ubiquitous computing applications.
Multiple-node model of wind turbine generating system for unbalanced distribution system load flow analysis Gianto, Rudy; Khwee, Kho Hie
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.5461

Abstract

This paper discusses a method to integrate a wind turbine generating system (WTGS) into a three-phase unbalanced distribution system load flow (DSLF) analysis. The proposed method is based on the single-phase multiple-node model. In the present work, the single-phase multiple-node model is extended to a three-phase multiple-node model to facilitate the load flow analysis of a three-phase unbalanced power system network. The multiple-node model (i.e., three-node model) will only modify the load flow analysis by introducing two lines and two load buses to the distribution system network where the WTGS is installed. Thus, a standard three-phase load flow program can be employed to compute the unknown quantities in the DSLF problem formulation. The proposed method is verified by incorporating the model into the load flow analysis of three-phase distribution networks. The investigation uses two representative distribution networks (i.e., 19-bus and 25-bus networks). The results of the study confirm the validity of the proposed method.

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