Mohd Nabil Muhtazaruddin

Critical Design Factors on Performance of Car Jack Lifting Operations

Applied Computing Technology (ACT), Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Batu Pahat, 86400 Johor, Malaysia. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Batu Pahat, 86400 Johor, Malaysia. Department of Management Information Systems, College of Commerce and Business Administrations, Dhofar University, Salalah, Oman.

Recognition of body segment based on human electromagnetic radiation analysis

The human body is shown have their own radiation, which emits at certain frequencies into space on their surrounding body. This study proposes an approach for recognition of human body segments based on electromagnetic radiation of human body. Four human body segments are considered called as Upper body, Torso, Arm and Lower Body. A-nearest neighbor (KNN) classifier is employed as pattern recognition technique to classify the human body segments and the performance of the classifier is evaluated using receiving operating characteristic (ROC). The results show that the proposed technique properly classifies the body segments with 93.75% accuracy. Experimental results recommend that the proposed technique is appropriate and capable to classify body segments using frequency analysis of human electromagnetic radiation.

EEG features extraction and k-NN classification during eyes closed

The aim of this study was to determine the number of EEG behaviours existed during eyes closed. The EEG signals before and after stress test were selected and computed into the EEG features known as asymmetry index (AsI). In order to determine the number EEG behaviours, the cluster centers of the EEG features were tested using subtractive clustering (SC). The maximum number of three clusters centers was identified and Fuzzy C-Means (FCM) was employed to segregate the data features into similar group. In order to test which clusters number gives highest classification, the FCM was executed separately for three clusters and two clusters. Subsequently, the k-Nearest Neighbour (k-NN) was used to classify the three clusters and two clusters and the classification rates were compared. The classification results showed that the number of two clusters give higher classification rate which was 100%.

Modelling the Effect of Applied Voltage and Frequency on Electroluminescence in Polymeric Material

N. A. Bani, Z. Abdul-Malek, F. Muhammad-Sukki, A. Abubakar Mas’ud, M. N. Muhtazaruddin, K Kamardin, J. J. Jamian, H. Ahmad Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia Institute of High Voltage and High Current (IVAT), Universiti Teknologi Malaysia, Johor, Malaysia School of Engineering, Faculty of Design and Technology, Robert Gordon University, Aberdeen, United Kingdom Faculty of Engineering, Multimedia University, Persiaran Multimedia, Cyberjaya, Selangor, Malaysia Department of Electrical and Electronic Engineering Technology, Jubail Industrial College, Saudi Arabia Advanced Informatics School, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor, Malaysia Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Malaysia