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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Indonesian Journal of Electrical Engineering and Computer Science
Ali Sophian
International Islamic University Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

 1 
Modelling of scanning pulsed eddy current testing of normal and slanted surface cracks Faris Nafiah; Ali Sophian; Md Raisuddin Khan; Ilham Mukriz Zainal Abidin
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 3: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i3.pp1297-1302

Abstract

Thanks to its wide bandwidth, pulsed eddy current (PEC) has attracted researchers of various backgrounds in the attempt to exploit its benefits in Non-destructive Testing (NDT). The ability of modelling PEC problems would be a precious tool in this attempt as it would help improve the understanding of the interaction between the transient magnetic field and the specimen, among others. In this work, a Finite Element Modelling (FEM) has been developed and experimental test data have been gathered for its validation. The investigated cases were simulated surface cracks of different sizes and angles. The study involved looking at time-domain PEC signals at different spatial distances from the cracks’ faces, which would particularly be useful for modelling scanning PEC probes. The obtained results show a good agreement between the FEM and experiment, demonstrating that the modelling technique can be used with confidence for solving similar problems.
Visual-Based Fingertip Detection for Hand Rehabilitation Dayang Qurratu’aini; Ali Sophian; Wahju Sediono; Hazlina Md Yusof; Sud Sudirman
Indonesian Journal of Electrical Engineering and Computer Science Vol 9, No 2: February 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v9.i2.pp474-480

Abstract

This paper presents a visual detection of fingertips by using a classification technique based on the bag-of-words method. In this work, the fingertips are specifically of people who are are holding a therapy ball, as it is intended to be used in a hand rehabilitation project. Speeded Up Robust Features (SURF) descriptors are used to generate feature vectors and then the bag-of-feature model is constructed by K-mean clustering which reduces the number of features. Finally, a Support Vector Machine (SVM) is trained to produce a classifier that distinguishes whether the feature vector belongs to a fingertip or not. A total of 4200 images, 2100 fingertip images and 2100 non-fingertip images, were used in the experiment. Our results show that the success rates for the fingertip detection are higher than 94% which demonstrates that the proposed method produces a promising result for fingertip detection for therapy-ball-holding hands.
Fingertip Detection Using Histogram of Gradients and Support Vector Machine Ali Sophian; Dayang Qurratu’aini
Indonesian Journal of Electrical Engineering and Computer Science Vol 8, No 2: November 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v8.i2.pp482-486

Abstract

One important application in computer vision is detection of objects. One important application in computer vision is detection of objects. This paper discusses detection of fingertips by using Histogram of Gradients (HOG) as the feature descriptor and Support Vector Machines (SVM) as the classifier. The SVM is trained to produce a classifier that is able to distinguish whether an image contains a fingertip or not. A total of 4200 images were collected by using a commercial-grade webcam, consisting of 2100 fingertip images and 2100 non-fingertip images, were used in the experiment. Our work evaluates the performance of the fingertip detection and the effects of the cell’s size of the HOG and the number of the training data have been studied. It has been found that as expected, the performance of the detection is improved as the number of training data is increased. Additionally, it has also been observed that the 10 x 10 size gives the best results in terms of accuracy in the detection. The highest classification accuracy obtained was less than 90%, which is thought mainly due to the changing orientation of the fingertip and quality of the images. 
Co-Authors Ali Aryo Bawono Amir Akramin Shafie Arselan Ashraf Dayang Qurratu’aini Dayang Qurratu’aini Faris Nafiah Hazlina Md Yusof Ilham Mukriz Zainal Abidin Md Raisuddin Khan Norfarah Nadia Ismail Sud Sudirman Teddy Surya Gunawan Wahju Sediono