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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Kim Gaik Tay
Universiti Tun Hussein Onn Malaysia
Computer Science & IT

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Dorsal hand vein authentication system using artificial neural network Sze Wei Chin; Kim Gaik Tay; Chew Chang Choon; Audrey Huong; Ruzairi Abdul Rahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 3: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i3.pp1837-1846

Abstract

Biometric feature authentication technology had been developed and implemented for the security access system. However, the known biometric features such as fingerprint, face and iris pattern failed to provide ideal security. Dorsal hand vein is the features beneath the skin which makes it not easily be duplicated and forged. It was expected to be used in biometric authentication technology to achieve an ideal accuracy with the uniqueness of its characteristics. In this paper, 240 images of 80 users were obtained from Bosphorus Hand Vein Database. The images were then pre-processed by cropping ROI, mean filtering, CLAHE enhancing and histogram equalizing. The ROI was then segmented by implementing binarization. The local binary pattern (LBP) features were then extracted from the segmented ROI. The extracted features were sent to an artificial neural network (ANN) for the classification of the images. The training result shows that the LBP features and ANN can recognize the dorsal hand vein pattern quite well with 99.86% accuracy. The ANN was then utilized in the MATLAB GUI program for testing 100 images (80 trained images of 80 users and 20 untrained images of 20 users) from the Bosphorus Hand Vein Database. The results revealed 100% accuracy in their matching result.
Optimization of light source wavelength for ammonia detection in water Nurfatihah Che Abd Rashid; Noran Azizan Cholan; Kim Gaik Tay; Afiqah Yaacob; Nazrah Ilyana Sulaiman; Khairulanwar Mokhiri; Nor Hafizah Ngajikin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 5: October 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i5.24079

Abstract

Optimization of light source wavelength for ammonia detection in surface water is presented in this work. For the ammonia detection, the surface water sample is mixed with sodium chloride and nessler reagent, whereas the sensor head consists of unclad plastic optical fiber. The unclad region has a length of 1 cm and the cladding is removed by immersing it in acetone solution. Experimental results indicate that the output light intensity of the sensor has linear relationship with the ammonia concentration. At the wavelength of 510 nm, the output light increases linearly as the ammonia concentration varies from 0.07 mg/L to 8.97 mg/L. At the same wavelength, the proposed sensor achieves the sensitivity of 0.0139 (mg/L)-1, accuracy of 99.59% and resolution of 0.72 µg/L. The analysis of light source wavelength reveals that a wavelength range from 450 nm to 580 nm produces the optimized performances. Within this wavelength range, the proposed sensor achieves sensitivity of higher than 0.01 (mg/L)-1, accuracy of higher than 99% and resolution of less than 1 µg/L.
Co-Authors Afiqah Yaacob Audrey Huong Chew Chang Choon Khairulanwar Mokhiri Nazrah Ilyana Sulaiman Nor Hafizah Ngajikin Noran Azizan Cholan Nurfatihah Che Abd Rashid Ruzairi Abdul Rahim Sze Wei Chin