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Bulletin of Electrical Engineering and Informatics

EEI
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Published by IAES Indonesia Section and Partners

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Core Subject : Engineering,

Electrical & Electronics Engineering

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Articles 627 Documents

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Configurations of memristor-based APUF for improved performance Julius Han Loong Teo; Noor Alia Noor Hashim; Azrul Ghazali; Fazrena Azlee Hamid
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

The memristor-based arbiter PUF (APUF) has great potential to be used for hardware security purposes. Its advantage is in its challenge-dependent delays, which cannot be modeled by machine learning algorithms. In this paper, further improvement is proposed, which are circuit configurations to the memristor-based APUF. Two configuration aspects were introduced namely varying the number of memristor per transistor, and the number of challenge and response bits. The purpose of the configurations is to introduce additional variation to the PUF, thereby improve PUF performance in terms of uniqueness, uniformity, and bit-aliasing; as well as resistance against support vector machine (SVM). Monte Carlo simulations were carried out on 180 nm and 130 nm, where both CMOS technologies have produced uniqueness, uniformity, and bit-aliasing values close to the ideal 50%; as well as SVM prediction accuracies no higher than 52.3%, therefore indicating excellent PUF performance.

Motor imagery classification in Brain Computer Interface (BCI) based on EEG signal by using machine learning technique N. E. Md Isa; A. Amir; M. Z. Ilyas; M. S. Razalli
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

This paper focuses on classification of motor imagery in Brain Computer Interface (BCI) by using classifiers from machine learning technique. The BCI system consists of two main steps which are feature extraction and classification. The Fast Fourier Transform (FFT) features is extracted from the electroencephalography (EEG) signals to transform the signals into frequency domain. Due to the high dimensionality of data resulting from the feature extraction stage, the Linear Discriminant Analysis (LDA) is used to minimize the number of dimension by finding the feature subspace that optimizes class separability. Five classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes, Decision Tree and Logistic Regression are used in the study. The performance was tested by using Dataset 1 from BCI Competition IV which consists of imaginary hand and foot movement EEG data. As a result, SVM, Logistic Regression and Naïve Bayes classifier achieved the highest accuracy with 89.09% in AUC measurement.

Heat distribution under microwave heating treatment Siti Zulaika Abdul Nyzam; Rosemizi Abd Rahim
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

This paper presents the process of microwave heating treatment to kill the rice weevil to improve the quality and quantity of rice for industrial storage purpose. Since many years ago, heat uniformity has been a major drawback of microwave heating application. The heat distribution in rice after undergoing four treatments with a microwave frequency of 2.4 GHz at the different power level of 540 and 900W with different time treatment of 50 and 80 seconds are shown in this paper. The samples are placed inside a square container, 8.5 cm x 8.5 cm x 2 cm. Each sample contains 15 adults of rice weevil of Sitophilus Oryzae placed randomly in the container and the mortality of the rice weevil for adult stages from each treatment are observed and interpreted in Analysis of Variance (ANOVA) technique.

Analysis of Wavelet-Based Full Reference Image Quality Assessment Algorithm Mokhtar, Faizah; Ngadiran, Ruzelita; Basheer, Taha; Nazren Abdul Rahim, Amir
Bulletin of Electrical Engineering and Informatics Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v8i1.1404

Measurement of Image Quality plays an important role in numerous image processing applications such as forensic science, image enhancement, medical imaging, etc. In recent years, there is a growing interest among researchers in creating objective image quality assessment (IQA) algorithms that can correlate well with perceived quality. A significant progress has been made for full reference (FR) IQA problem in the past decade. In this paper, we are comparing 5 selected FR IQA algorithms on TID2008 image datasets. The performance and evaluation results are shown in graphs and tables. The results of quantitative assessment showed wavelet-based IQA algorithm outperformed over the non-wavelet based IQA method except for WASH algorithm which the prediction value only outperformed for certain distortion types since it takes into account the essential structural data content of the image.

Progress in neural network based techniques for signal integrity analysis–a survey Chan Hong Goay; Azniza Abd Aziz; Nur Syazreen Ahmad; Patrick Goh
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

With the increase in data rates, signal integrity analysis has become more time and memory intensive. Simulation tools such as 3D electromagnetic field solvers can be accurate but slow, whereas faster models such as design equations and equivalent circuit models lack accuracy. Artificial neural networks (ANNs) have recently gained popularity in the RF and microwave circuit modeling community as a new modeling tool. This has in turn spurred progress towards applications of neural networks in signal integrity. A neural network can learn from a set of data generated during the design process. It can then be used as a fast and accurate modeling tool to replace conventional approaches. This paper reviews the recent advancement of neural networks in the area of signal integrity modeling. Key advancements are considered, particularly those that assist the ability of the neural network to cope with an increasing number of inputs and handle large amounts of data.

Knots timber detection and classification with C-Support Vector Machine Fakhira Iwani Muhammad Redzuan; Marina Yusoff
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Timber knots recognition is of prime importance to further determine the timber grade. The recognition is normally based on the human expert’s eyes in which can lead to some flaws based on human limitations and weaknesses. The use of X-ray can cause emits radiation and can be dangerous to the workers. This paper addresses the employment of computational methods for knot detection. A pre-processing and feature extraction methods include contrast stretching, median blur and thresholding, gray scale and local binary pattern were used. More than 400 datasets of knot images of the tropical timbers, namely Acacia and Hevea Brasiliensis have been tested using C-support vector machine as a knot classifier. The findings demonstrate different performances for three types of kernel. Linear kernel function outperformed both radial basis function and polynomial kernel functions for Acacia and Hevea Brasiliensis species. Both species classifications using linear kernel have managed to achieve a promising accuracy. Knots classification with the used of support vector machine has shown a promising result to improve the classifier and test with different types of tropical timbers.

Solving financial allocation problem in distribution system expansion planning Siti Hajar Mohd Tahar; Shamshul Bahar Yaakob; Ahmad Shukri Fazil Rahman; Amran Ahmed
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

This paper introduces a new technique to solve financial allocation in Distribution System Expansion Planning (DSEP) problem. The proposed technique will be formulated by using mean-variance analysis (MVA) approach in the form of mixed-integer programming (MIP) problem. It consist the hybridization of Hopfield Neural Network (HNN) and Boltzmann Machine (BM) in first and second phase respectively. During the execution at the first phase, this model will select the feasible units meanwhile the second phase will restructured until it finds the best solution from all the feasible solution. Due to this feature, the proposed model has a fast convergence and the accuracy of the obtained solution. This model can help planners in decision-making process since the solutions provide a better allocation of limited financial resources and offer the planners with the flexibility to apply different options to increase the profit.

Tooth segmentation using dynamic programming-gradient inverse coefficient of variation Anuar Mikdad Muad; Nur Sakinah Mohamed Bahaman; Aini Hussain; Mohd Yusmiaidil Putera Mohd Yusof
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Teeth provide meaningful clues of an individual. The growth of the teeth is correlated with the individual age. This correlation is widely used to estimate age of an individual in applications like conducting forensic odontology, immigration, and differentiating juveniles and adolescents. Current forensic dentistry largely depends on laborious investigation process that is performed manually and can be influenced by human factors like fatigue and inconsistency. Digital panoramic radiograph dental images allow noninvasive and automatic investigation to be performed. This paper presents analyses on third molar tooth segmentation for the population in Malaysia, ranging from persons age of 5 years old to 23 years old. Two segmentation techniques: gradient inverse coefficient of variation with dynamic programming (DP-GICOV) and Chan-Vese (CV) were employed and compared. Results demonstrated that the accuracy of DP-GICOV and CV were 95.3%, and 81.6%, respectively.

Haze alarm visual map (HazeViz): an intelligent haze forecaster Mohd Said Syukri Morsid; Syeril Azira Jamaluddin; Nur Azmina Hood; Norshahida Shaadan; Yap Bee Wah; Muthukkaruppan Annamalai
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

The haze problem has intensified in recent years. The particulate matter of less than 10 microns in size, PM10 is the dominant air pollutant during haze. In this paper, we present the development of HazeViz, a Haze Alarm Visual Map forecaster, which is based on PM10. The intelligent web application allows users to visualize the pattern of PM10 in a region, forecasts PM10 value and alarms bad haze condition. HazeViz was developed using HTML, Java Script, PHP, MySQL, R Programming and Fusionex Giant. The SARIMA statistical forecasting models that underlie the application were developed using R. The PM10 trend analysis, and the consequential map and chart visualizations were implemented on the Fusionex GIANT Big Data Analytics platform. HazeViz was developed in the context of the Klang Valley, our case study. The dataset was obtained from Department of Environment Malaysia, which contains a total of 157,680 hourly PM10 data for six stations in Klang Valley, for the years 2013 to 2015. The SARIMA models were developed using maximum daily PM10 data for 2013 and 2014, and the 2015 data was used to validate the model. The fitting models were determined based on the Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). While the selected models were implemented in HazeViz and successfully deployed on the web, the results show that the selected models have MAPE ranging between 35 percent and 45 percent, which implies that the models are still far from robust. Future work can consider augmented SARIMA models that can yield improved results.

Evaluating users’ emotions for Kansei-based Malaysia higher learning institution website using Kansei checklist Punitha Turumogan; Aslina Baharum; Ismassabah Ismail; Nor Azida Mohamed Noh; Nur Shahida Ab Fatah; Noorsidi Aizuddin Mat Noor
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

Emotions play a crucial role in human-computer interaction. Emotion research in the field of human-computer interaction has only started recently and continuously evolving through the investigation and understanding of emotional effects. Thus, it forms an intelligent interaction between human and computer by responding effectively to the humans’ feelings. Emotional design generates remarkable user experiences for websites as the emotional experiences create an intense impression on our long-term memory. Recent scientific findings recommend emotional elements to be considered in designing websites as emotions influences one’s perception, conception and decision-making throughout the interaction with a website. A poorly designed user interface leads to bad user interaction while rising the users’ arousal and a displeasing user experience with a website elicits dissatisfaction emotion where consecutively results in avoidance and prevents revisit to the website. This proves the importance of emotional engagement in a website design. This research evaluated users’ emotions toward a Malaysian higher learning institution website which was designed in accordance with the standard Kansei-based web design guideline. The result justified the standard Kansei-based web design guideline for website of higher learning institutions in Malaysia.

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