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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 72 Documents
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Search results for , issue "Vol 12, No 6: December 2023" : 72 Documents clear
Developments in scan shift power reduction: a survey Sontakke, Vijay; Dickhoff, John
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5668

Abstract

While power reduction during testing is necessary for today's low-power devices, it also lowers test costs. Scan-based methods are the most widely used approach for testing integrated circuits (IC). Test vectors are shifted into and out of scan chains bit by bit during shift operation. The time required for shift operation dominates the test time. With the geometries shrinking (7 nm→5 nm→3 nm→1.8 nm), ICs are required to be tested for newer defects, increasing test time. The most effective way to reduce test time for scan operation is to increase the frequency of the shift operation. Reduction in shift power enables scan operation to be performed with increased frequency, reducing test time, and test cost. This paper presents a survey of techniques proposed recently for shift power reduction. Various techniques, including special flip-flop usage, segmentation, reordering, and low-pass filter, are being reviewed. The techniques are organized based on main attributes to underscore their similarities and differences. Pros and cons in terms of complexities involved in their implementation are discussed. We believe this paper will provide a point of reference for further studies in scan shift power reduction and will be helpful to both industry and academia.
Ultra low loss and dual polarized SPR-PCF sensor based on refractive index Irawan, Dedi; Ramadhan, Khaikal; Saktioto, Saktioto; Fitmawati, Fitmawati; Hanto, Dwi; Widiyatmoko, Bambang; Marwin, Azwir; Azhar, Azhar
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.4293

Abstract

In this paper presents a numerical simulation using the finite element method (FEM) to analyze the performance of a photonic crystal fiber (PCF) integrated with plasmonic material sensor components. The sensor comprises silica and Au layers with a thickness of 45 nm, arranged in a simple geometric structure. Our proposed sensor component exhibits ultra-low loss, distinguishing it from previous studies that have focused on wavelength-sensitive (WS) and amplitude-sensitive (AS) measurement techniques. The refractive index (RI) range of the sensor component spans from 1.32 to 1.38 RIU. The maximum WS and AS values achieved are 6,000 nm/RIU, -373.4 1/RIU (x-polarization), and -385.4 1/RIU (y-polarization), respectively. Moreover, we demonstrate an ultra-low loss of 0.00117 dB/cm (x-polarized) and 0.00307 dB/cm (ypolarized). In terms of sensor resolution, this design achieves a remarkable resolution of 1.6×10-7 RIU for both x-and y-polarized measurements
Classification and keyword extraction of online harassment text in Thai social network Hemtanon, Siranuch; Phetkrachang, Ketsara; Yangyuen, Wachira
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5939

Abstract

Online harassment in social network services (SNS) is a type of cyberbullying issue that needs to be addressed and required preventive measures. In this paper, we develop a detection of cyberbullying regarding harassment textual posts in Thai on the Facebook SNS. We collect public posts and ask experts to label the post as positive or negative regarding harassment posts or not. The annotated data are trained for binary classification considering words in the centre as features to predict malicious intent to insult and threaten other users. The information gain score obtained in generating a prediction model is ranked for the top 20 words with the highest score as significant words involving online harassment. From experiments, the results show that the detection performance obtained a 0.78 f1 score on average. The result analysis indicated that the word surface approach helps detect insulting post decently, but some posts with metaphor to tone down the malicious intent may not be detected as harmful semantic intent are hidden behind word form. Top-20 significant words for bullying showed that bullying posts were body-shaming and lower social status.
Design and performance analysis of Cobb angle measurement from X-ray images Adibatti, Spurthi; Sudhindra, K. R.; Manisha Shivaram, Joshi
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5335

Abstract

Scoliosis seems to be the most frequently used type of spinal abnormality. Cobb angle measurement essentially relates to the quantification of the spinal stenosis in degrees, also it is a globally standardized approach for assessing scoliosis. Based on a recent survey, there really is no accurate and comprehensive technique for calculating the Cobb angle programmatically. This problem is crucial in the medical field pertaining to Cobb angle measurement to identify the exact position of Cobb in X-ray images. However, multiple investigations have demonstrated that there is inter and intra-observer variance when assessing the Cobb angle physically. The goal is to create a computer-assisted solution to reduce user-based Cobb angle measuring errors. The preprocessing filters and semi-automatic methods determine the overall architectural curved spine. Using the Cobb technique, results are inaccuracies and improper in the estimation of a scoliotic curvature's peak or bottom vertebra. The curve-fitting approach was used in this investigation to reduce uncertainty. Every patient had a digitally recorded poster anterior radiography image. The polynomial estimation is fitted using user-defined curvature midpoints that correspond to vertebral intersection points. The Cobb angle is computed by taking the first characteristic of the fitted polynomial function and dividing it by the number of vertebrae.
A fine tune robust transfer learning based approach for brain tumor detection using VGG-16 Islam, Rakibul; Akhi, Amatul Bushra; Akter, Farzana
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5646

Abstract

Brain tumor recognition by magnetic resonance imaging (MRI) is crucial because it improves survival rates and allows them to plan treatments accordingly. An accumulation of abnormal cells known as a brain tumor can spread to nearby tissues and endanger the patient. Magnetic resonance imagery is the primary imaging technique which determines the extent of brain tumors. Deep learning techniques rapidly grew in computer vision due to ample data for model training and improved designs on applications. MRI has shown promising results when using deep learning approaches to identify and classify brain tumors. This study uses MRI data and a convolutional neural network (CNN) to create a reliable transfer learning model that classifies tumors under four classes. Brain tumors' unwanted parts are excised, the quality is improved, and the cancer is coloured. By eliminating artefacts, decreasing noise, and boosting the image. The number of MRI images has increased using two augmentation techniques. A number of CNN architectures, including VGG19, VGG16, MobileNet, InceptionV3, and MobileNetV2 analyzed the augmented dataset. Where VGG-16 provides the accuracy of highest level. The best model underwent a hyperparameter ablation investigation, which led to the suggested hyper-tuned VGG16 obtaining 99.21% test and validation accuracy and 99.01% test accuracy.
The use of CaO:Eu3+ and Zn2SiO4:Mn2+ phosphors to increase the color quality and illumination intensity of WLEDs Thanh Tung, Ha; An Nguyen Thi, Dieu; Xuan Le, Phan
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.4754

Abstract

The lumen efficacy in remote phosphor structure displayed remarkable enhancements, which is notable for the development of white light-emitting diodes (WLEDs). Nevertheless, its quality of colour is deemed not as good as that of the conformal or in-cup phosphor structure. Therefore, the goal of this research is to achieve a better quality of colour and significant luminous flux value for remote phosphor configuration by using extra phosphor layers. In particular, the two-layer and three-layer structures with the implementation of green and red phosphors are proposed. Comparing these two structures can help pinpoint the best suited for the WLED production. The assessment of each structure’s effect on the WLEDs’ optical parameters was determined under various correlated temperatures of colour (5,600-8,500 K). The outcomes indicated that the three-layer structure enhanced the quality of colour with greater efficiency compared to the two-layer structure due to the increased color rendering index (CRI), color quality scale (CQS), and photoluminescence (PL), and reduced colour deviation. The scattering improvement of the three-layer structure is a key factor of these accomplishments, proven by the scattering theory of Mie. Therefore, the three-layer structure is potential for developing WLED production.
A comparative study of Gaussian mixture algorithm and K-means algorithm for efficient energy clustering in MWSN Ahmad, Iman Ameer; Jawad Al-Nayar, Muna Mohammed; Mahmood, Ali M.
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5707

Abstract

Wireless sensor networks (WSNs) represent one of the key technologies in internet of things (IoTs) networks. Since WSNs have finite energy sources, there is ongoing research work to develop new strategies for minimizing power consumption or enhancing traditional techniques. In this paper, a novel Gaussian mixture models (GMMs) algorithm is proposed for mobile wireless sensor networks (MWSNs) for energy saving. Performance evaluation of the clustering process with the GMM algorithm shows a remarkable energy saving in the network of up to 92%. In addition, a comparison with another clustering strategy that uses the K-means algorithm has been made, and the developed method has outperformed K-means with superior performance, saving energy of up to 92% at 4,500 rounds.
A novel automated feature selection based approach to recognize cauliflower disease Shakil, Rashiduzzaman; Akter, Bonna; Javed Mehedi Shamrat, F M; Haider Noori, Sheak Rashed
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5359

Abstract

Cauliflower disease is a primary cause of reduced cauliflower yield. Preventing cauliflower disease requires early diagnosis. In the scope of this study, we suggested an agro-medical expert system that would make it easier to diagnose cauliflower disease. In this method, a digital image must be taken off the phone or handled device to diagnose cauliflower sickness. A data augmentation technique was initially used to construct a vast data set. The disease-affected parts of the cauliflower were then segmented using k-means clustering. Following that, ten statistical and gray-level co-occurrence matrix (GLCM) features were retrieved from the segmented pictures. After choosing the top n features (N ranged from 5 to 10), the synthetic minority oversampling technique (SMOTE) approach was used to handle training datasets with different amounts of each feature. After that, we utilized five machine learning (ML) algorithms and evaluated their performance using seven performance evaluation matrices for both augmented and non-augmented datasets. The same procedure was performed on both datasets. Then, we use both datasets to test how well the classifier works. Logistic regression (LR) is the most accurate method for the top nine features in the augmented dataset (90.77%).
A novel method for estimating propagation pathloss in millimeter-wave communication systems Thanh Quang, Vu; Huu Duc, Do; Van Yem, Vu; Phuong Thao, Hoang Thi
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.6614

Abstract

In millimeter wave communication systems, the construction of a path loss model that is close to empirical one plays an important role in planning coverage, system capacity, and link budgets. In this paper, we propose a novel approach applied unsupervised machine learning for propagation pathloss estimation in millimeter wave communication systems based on the iterative procedure of cooperative and iterative evaluation exchange (Co-IEE) algorithm. The propagation path loss models of the time city–an urban area in the center of Hanoi, Vietnam in both line-of-sight (LOS) and non line-of-sight (NLOS) are estimated and calculated using the proposed approach and then they are compared to the minimum mean square error (MMSE). The estimated results of the proposed model show the approximation with the optimum one returned by MMSE method. Moreover, the proposed model of estimating path loss can solve the problem of sensitivity with outliers existing in MMSE and give more choices for path loss exponents.
Fake account detection in social media using machine learning methods: literature review Kerrysa, Nalia Graciella; Utami, Ika Qutsiati
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5334

Abstract

With the rapid development of emerging technologies in the industrial revolution 4.0 or 5.0, social media has become one of the social environments to carry out social activities, both socializing and advertising. However, since it is an open platform by nature, cybercrime occurrence in social media is inevitable. Currently, more than a million fake accounts are existing on Instagram, Twitter, and Facebook, intending to increase followers, spread hoaxes, and spam. On one hand, it is difficult to manually eliminate these accounts on social media platforms. On the other hand, research on automatic fake account detection has been carried out for more than a decade. This study provides literature reviews aiming to deliver information about several methods and machine learning algorithms with the performances measured in identifying fake accounts on three well-known social media platforms: Twitter, Instagram, and Facebook.

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