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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Muhammad Azmi Ahmed Nawawi
Universiti Teknologi Malaysia
Computer Science & IT

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Thermal Performance Analysis for Optimal Passive Cooling Heat Sink Design Nur Warissyahidah Badrul Hisham; Fatimah Sham Ismail; Muhammad Azmi Ahmed Nawawi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 2: June 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i2.6140

Abstract

Recent advances in semiconductor technology show the improvement of fabrication on electronics appliances in terms of performance, power density and even the size. This great achievement however led to some major problems on thermal and heat distribution of the electronic devices. This thermal problem could reduce the efficiency and reliability of the electronic devices. In order to minimize this thermal problem, an optimal cooling techniques need to be applied during the operation. There are various cooling techniques have been used and one of them is passive pin fin heat sink approach. This paper focuses on inline pin fin heat sink, which use copper material with different shapes of pin fin and a constant 5.5W heat sources. The simulation model has been formulated using COMSOL Multiphysics software to stimulate the pin fin design, study the thermal distribution and the maximum heat profile.
Comprehensive Pineapple Segmentation Techniques with Intelligent Convolutional Neural Network Muhammad Azmi Ahmed Nawawi; Fatimah Sham Ismail; Hazlina Selamat
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 3: June 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v10.i3.pp1098-1105

Abstract

This paper proposes an intelligent segmentation technique for pineapple fruit using Convolutional Neural Network (CNN). Cascade Object Detector (COD) method is used to detect the position of the pineapple from the captured image by returning the bounding box around the detecting pineapple. Image background such as ground, sky and other unwanted objects have been removed using Hue value, Adaptive Red and Blue Chromatic Map (ARB) and Normalized Difference Index (NDI) methods. However, the ARB and NDI methods are still producing misclassified error and the edge is not really smooth. In this case Template Matching Method (TMM) has been implemented for image enhancement process. Finally, an intelligent CNN is developed as a decision maker to select the best segmentation image ouput from ARB and NDI. The results obtained show that the proposed intelligent method has successfully verified the fruit from the background with high accuracy as compared to the conventional method.
Co-Authors Fatimah Sham Ismail Hazlina Selamat Nur Warissyahidah Badrul Hisham