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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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An open toolbox for generating map of actively confirmed SARS-CoV-2 or COVID-19 cases in Vietnam Duc Chung Tran
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2621

Abstract

The recent outbreak of novel coronavirus, SARS-CoV-2 or COVID-19, discovered in late 2019, being continued to spread across regions worldwide, has resulted in 1,914,916 “confirmed” cases with up to 123,010 deaths, as in situation report –85 by World Health Organization (WHO). Most of the developed disease monitoring and tracking tools currently available only present the reported cases up to country-level and not detail down to provincial- or state-, city- level within the countries. This is insignificant for supporting activities in quickly reducing and preventing the spread of the disease within a certain country because further detail potential infectious locations are not provided for people to avoid traveling or passing by there. Thus, this work presents an open toolbox for generating map of actively “Confirmed” cases in a country, i.e., Vietnam, given a dataset containing their statuses and current locations, detail down to provincial-or state-, city-level. The newly released algorithm reduced approximately 24.41% of processing time of the preceding one. In addition, the algorithm can be easily extended for supporting other countries given suitable datasets.
A triple band modified F-shaped monopole antenna for RFID application Spoorti Barigidad; Aishwarya C. Yeshawant; Sridevi Rao; Tharunya C. A.; Tanweer Ali; Sameena Pathan
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2623

Abstract

Radio frequency identification (RFID) is a very prominent technology and is used in object-attached identification and tracking tags. In this paper a triple band monopole antenna is designed to work at 2.2-2.6 GHz (lower RFID band), 5.3-6.8 GHz and 8.7-9.5 GHz (upper RFID band) frequency ranges. The antenna design resembles a modified F-shaped radiator and is built on a low cost easily available FR4 dielectric substrate. Initially an F-shaped radiator with partial ground plane is studied which exhibits the operation at 2.6 and 6.5 GHz. Further, modifying this F-shaped radiator exhibits an additional resonance at 9.2 GHz. Fundamental characteristics such as reflection coefficient (S11), radiation pattern and 3D gain have been analyzed and good results have been obtained. Parametric analysis is carried out to fix the optimized antenna dimensions. All the simulations are carried out using the high frequency structure simulator software (HFSS). The antenna structure is easy to design and produce, and ideal for use in RFID applications.document quickly and accurately, to determine its relevance to their interests, and thus to decide whether to read the document in its entirety.
Multilayer extreme learning machine for hand movement prediction based on electroencephalography Khairul Anam; Cries Avian; Muhammad Nuh
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2626

Abstract

Brain computer interface (BCI) technology connects humans with machines via electroencephalography (EEG). The mechanism of BCI is pattern recognition, which proceeds by feature extraction and classification. Various feature extraction and classification methods can differentiate human motor movements, especially those of the hand. Combinations of these methods can greatly improve the accuracy of the results. This article explores the performances of nine feature-extraction types computed by a multilayer extreme learning machine (ML-ELM). The proposed method was tested on different numbers of EEG channels and different ML-ELM structures. Moreover, the performance of ML-ELM was compared with those of ELM, Support Vector Machine and Naive Bayes in classifying real and imaginary hand movements in offline mode. The ML-ELM with discrete wavelet transform (DWT) as feature extraction outperformed the other classification methods with highest accuracy 0.98. So, the authors also found that the structures influenced the accuracy of ML-ELM for different task, feature extraction used and channel used.
Photoacoustic technology for biological tissues characterization Hui Ling Chua; Audrey Huong
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2624

Abstract

The existing photoacoustics (PA) imaging systems showed mixed performance in imaging characteristic and signal-to-noise ratio (SNR). This work presents the use of an in-house assembled PA system using a modulating laser beam of wavelength 633 nm for two-dimensional (2D) characterization of biological tissues. The differentiation of the tissues in this work is based on differences in their light absorption, wherein the produced photoacoustic signal detected by a transducer was translated into phase value that corresponds to the peak amplitude of optical absorption of tissue namely fat, liver and muscle. This work found fat tissue to produce the strongest PA signal with mean ± standard deviation (SD) phase value = 2.09 ± 0.31 while muscle produced the least signal with phase value = 1.03 ± 0.17. This work discovered the presence of stripes pattern in the reconstructed images of fat and muscle resulted from their structural properties. In addition, a comparison is made in an attempt to better assess the performance of the developed system with the related ones. This work concluded that the developed system may use as an alternative, noninvasive and label-free visualization method for characterization of biological tissues in the future.
High performance of excitation system for synchronous generator based on modeling analysis Yasir Abdulhafedh Ahmed; Yousif I. M. Al-Mashhadany; Mustafa Ahmed Nayyef
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2627

Abstract

Mathematical description of electromechanical systems operation is powerful parameter to get high performance with practical implement of the systems. This paper describes a mathematical presentation for the behavior excitation system of synchronous generator based on the optimal values of the parameters. The study of the mathematical modeling for dynamics of excitation system required the knowledge for the effect of each parameter to get the typical values provided by the manufacturer implementing. The simulation of the final model which obtained was conducted on Matlab version 2019b. The final results of simulation for the mathematical model are satisfactory, and it proves the ability of independence this model as practical implement.
Non-invasive glucose monitoring devices: A review Muhammad Farhan Affendi Mohamad Yunos; Anis Nurashikin Nordin
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2628

Abstract

Diabetes is a growing chronic disease that affect millions of people in the world. Regular monitoring of blood glucose levels in patients is necessary to keep the disease under control. Current methods of blood glucose monitoring devices are typically invasive, causing discomfort to the patients. Non-invasive glucose monitoring devices are a possible game changer for diabetic patients as it reduces discomfort and provides continuous monitoring. This manuscript presents a review of non-invasive glucose biosensors with particular focus on leading technologies available in the market, such as microwave sensing, near-infrared spectroscopy, iontophoresis, and optical methods. This paper intends to describe non-invasive blood glucose monitoring methods using various biological fluids (sweat, saliva, interstitial fluid, urine), highlighting the advantages and drawbacks in latest device development. This review also discusses future trends of glucose detection devices and how it will improve patients’ quality of life.
Performance evaluation of decision tree classification algorithms using fraud datasets Eddie Bouy B. Palad; Mary Jane F. Burden; Christian Ray Dela Torre; Rachelle Bea C. Uy
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2630

Abstract

Text mining is one way of extracting knowledge and finding out hidden relationships among data using artificial intelligence methods. Surely, taking advantage of different techniques has been highlighted in previous researches however, the lack of literature focusing on cybercrimes implies the lack of utilization of data mining in facilitating cybercrime investigations in the Philippines. This study therefore classifies computer fraud or online scam data coming from Police incident reports as well as narratives of scam victims as a continuation of a prior study. The dataset consists mainly of unstructured data of 49,822 mainly Filipino words. Further, 5 decision tree algorithms namely, J48, Hoeffding Tree, Decision Stump, REPTree, and Random Forest were employed and compared in terms of their performance and prediction accuracy. The results show that J48 achieves the highest accuracy and the lowest error rate among other classifiers. Results were validated by Police investigators where J48 was likewise preferred as a potential tool to apply in cybercrime investigations. This indicates the importance of text mining in the field of cybercrime investigation domains in the country. Further work can be carried out in the future using different and more inclusive cybercrime datasets and other classification techniques in Weka or any other data mining tool.
Automatic whole-body bone scan image segmentation based on constrained local model Ema Rachmawati; Jondri Jondri; Kurniawan Nur Ramadhani; Achmad Hussein Sundawa Kartamihardja; Arifudin Achmad; Rini Shintawati
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2631

Abstract

In Indonesia, cancer is very burdensome financially for sufferers as well as for the country. Increasing the access to early detection of cancer can be a solution to prevent the situation from worsening. Regarding the problem of cancer lesion detection, a whole-body bone scan image is the primary modality of nuclear medicine for the detection of cancer lesions on a bone. Therefore, high segmentation accuracy of the whole-body bone scan image is a crucial step in building the shape model of some predefined regions in the bone scan image where metastasis was predicted to appear frequently. In this article, we proposed an automatic whole-body bone scan image segmentation based on constrained local model (CLM). We determine 111 landmark points on the bone scan image as the input for the model building step. The resulting shape and texture model are further used in the fitting step to estimate the landmark points of predefined regions. We use the CLM-based approach using regularized landmark mean-shift (RLMS) to lessen the effect of ambiguity, which was struggled by the CLM-based approach. From the experimental result, we successfully show that our proposed image segmentation system achieves higher performance than the general CLM-based approach.
Optimized multimodal biometric system based fusion technique for human identification Muthana H. Hamd; Rabab A. Rasool
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2632

Abstract

This paper presents three novelty aspects in developing biometric system-based face recognition software for human identification applications. First, the computations cost is greatly reduced by eliminating the feature extraction phase and considering only the detected face features from the phase congruency. Secondly, a motivation towards applying a new technique, named mean-based training (MBT) is applied urgently to overcome the matching delay caused by the long feature vector. The last novelty aspect is utilizing the one-to-one mapping relationship for fusing the edge-to-angle unimodal classification results into a multimodal system using the logical-OR rule. Despite some dataset difficulties like unconstrained facial images (UFI) which includes varying illuminations, expressions, occlusions, and poses, the multimodal system has highly improved the accuracy rate and achieved a promising recognition result, where the decision fusion is classified correctly (84, 92, and 72%) with only one training vector per MBT in contrast to (80, 62, and 68%) with five training vectors for normal matching. These results are measured by Eucledian, Manhattan, and Cosine distance measure respectively.
Autonomous microgrid based parallel inverters using droop controller for improved power sharing Siddaraj Siddaraj; Udaykumar R. Yaragatti; Nagendrappa H.; Vikash Kumar Jhunjhunwala
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2663

Abstract

The existing microgrid has become a challenge to the sustainable energy source to provide a better quality of power to the consumer. To build a reliable and efficient microgrid, designing a droop controller for the microgrid is of utmost importance. In this paper, multiple voltage source inverters connected in parallel using an active power-frequency/reactive power-voltage droop scheme. The proposed method connected to two distributed generators local controllers, where each unit consists of a droop controller with an inner voltage-current controller and a virtual droop controller. By adding this controller to the microgrid reliability and load adaptability of an islanded system can be improved. This concept applied without any real-time communication to the microgrid. Thus, simulated using MATLAB/Simulink, the obtained results prove the effectiveness of the autonomous operation's microgrid model.

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