Politeknik Tuanku Syed Sirajuddin

Putra, Malaysia

Politeknik Tuanku Syed Sirajuddin

Putra, Malaysia
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Iqbal M.N.,University Malaysia Perlis | Malek F.,University Malaysia Perlis | Ronald S.H.,University Malaysia Perlis | Shafiq M.,University Malaysia Perlis | And 2 more authors.
Progress in Electromagnetics Research | Year: 2012

Biomass used for energy, whether it is extracted from forest residues or agricultural waste, contributes in many areas, such as power production, the construction industry, and also as a major source of different organic and inorganic compounds in the petrochemical industry. In recent years, research has identified a very remarkable use of agricultural waste, especially rice husks, as a microwave absorber in a pyramidal shape. However, absorbers built in this shape are fragile and require a very high degree of care, especially near the access panels, doors, and high traffic areas of the anechoic facility. This paper presents the results of a detailed experimental investigation of a more-robust, new design that is based on the concept of impedance or dielectric grading of rice-husk material. The absorber was fabricated using multiple layers of rice-husk material with increasing dielectric loss along the incident wave propagation axis. This type of fabrication technique provides more robust design of the microwave, rice-husk absorber with less thickness, as compared to the geometricallytapered, pyramid, or wedge absorbers. Free-space transmission and radar cross section (RCS) methods have been used, to study the electromagnetic compatibility (EMC) performance over the frequency range of 4-8 GH z. After the receiving equipment was calibrated by the thru-reflect-line (TRL) calibration technique, the experiments were performed inside the anechoic chamber. The performance of the absorber was evaluated by incorporating the effects of circular-hole perforation, cross-polarized seams, and different metallic back plates. The proposed absorber demonstrated good performance (< -10 dB) for normal and 60° off the normal incident angles over the frequency range of 4-8 GH z. Reflectivity performance also was found to be comparable to one of the commercially-available absorbers.


Naufal Mansor M.,University Malaysia Perlis | Rejab M.N.,Politeknik Tuanku Syed Sirajuddin
Proceedings - 2013 IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2013 | Year: 2013

In the last recent years, non-invasive methods through image analysis of facial have been proved to be excellent and reliable tool to diagnose of pain recognition. This paper proposes a new feature vector based Local Binary Pattern (LBP) for the pain detection. Different sampling point and radius weighted are proposed to distinguishing performance of the proposed features. In this work, Infant COPE database is used with illumination added. Multi Scale Retinex (MSR) is applied to remove the shadow. Two different supervised classifiers such as Gaussian and Nearest Mean Classifier are employed for testing the proposed features. The experimental results uncover that the proposed features give very promising classification accuracy of 90% for Infant COPE database. © 2013 IEEE.


Zamri Y.B.,Politeknik Tuanku Syed Sirajuddin | Shamsul J.B.,University Malaysia Perlis | Amin M.M.,University Malaysia Perlis
International Journal of Mechanical and Materials Engineering | Year: 2011

This research studied the potential of palm oil clinker as reinforcement in composite based aluminium for tribological applications. Palm oil clinker particle (POCp) reinforced aluminium matrix composites at different weight % of POCp (0 - 20 %) were fabricated via powder metallurgy technique. Sliding wear behaviour of the composites was studied against mild steel mating surface using Pin-On-Disc configuration at different applied load (3 - 51 N), sliding distances (0 - 500 m) and sliding velocities (0.55 m/s). The analysis of worn surface and subsurface was studied using a scanning electron microscope (SEM). The results indicate that the composites exhibited better wear resistance at applied load below than 11 N. From surface morphology (SEM study), the presence of POCp in the composite enhance the wear resistance performance of aluminium matrix. From the practical perspective, POCp has highly potential to be utilised as reinforcement in order to improve wear resistance of aluminium in tribological applications.


Naufal Mansor M.,University Malaysia Perlis | Rejab M.N.,Politeknik Tuanku Syed Sirajuddin
Proceedings - 2013 IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2013 | Year: 2013

This paper discussed the crucial demand regarding the scheme to translate the silence voice from the newborn. The infant can't afford to express their feeling of pain by voice. Hence, we proudly present an infant pain recognition system to overcome this matter. We employed the Single Scale Retinex (SSR) to remove the illumination level. Secondly, Gray-Level Co-occurrence Matrix (GLCM) was adopted as the feature extraction. We determine the condition of the infants (pain/no pain) with Hybrid Genetic Algorithm Neural Network (GANN) and Linear Discriminant Analysis (LDA). Several examples were conducted to evaluate the performance of the proposed method under different illumination levels. © 2013 IEEE.


Mansor M.N.,University Malaysia Perlis | Rejab M.N.,Politeknik Tuanku Syed Sirajuddin
Proceedings - 2013 IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2013 | Year: 2013

Most of infant pain cause changes in the face. Clinicians use image analysis to characterize the pathological faces. Nowadays, infant pain research is increasing dramatically due to high demand from all medical team. This paper presents a sparse and naïve Bayes classifier for the diagnosis of infant pain disorders. Phase congruency image and local binary pattern are proposed. The proposed algorithms provide very promising classification rate. © 2013 IEEE.


Mansor M.N.,University Malaysia Perlis | Rejab M.N.,Politeknik Tuanku Syed Sirajuddin
Proceedings - 2013 IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2013 | Year: 2013

This paper discussed the crucial demand regarding the scheme to translate the silence voice from the newborn. The infant can't afford to express their feeling of pain by voice. Hence, we proudly present an infant pain recognition system to overcome this matter. We employed Saliency Using Natural statistics (SUN) Saliency Map as the feature extraction. We determine the condition of the infants (pain/no pain) with Support Vector Machine (SVM) Classifier. Several examples were conducted to evaluate the performance of the proposed method. © 2013 IEEE.


Mansor M.N.,University Malaysia Perlis | Rejab M.N.,Politeknik Tuanku Syed Sirajuddin
Applied Mechanics and Materials | Year: 2014

Late of infant pain detection on the early stage may affect newborn's growth. Regarding of this matter, different techniques have been proposed such as facial expressions, speech production variation, and physiological signals to detect the pain states of a person. For past 2 decades, the determination of pain state through images has been undergone substantial research and development. Various techniques are used in the literature to classify pain states on the basis of images. In this paper, a feature extraction method using Principal Component Analysis (PCA) was adopted for identifying the pain states of an infant. In this study images samples are taken from Classification of Pain Expressions (COPE) database. Fuzzy k-NN, k Nearest Neighbor (k-NN), Feed Forward Neural network (FFNN) and Linear Discriminant analysis (LDA) based classifier is used to test usefulness of suggested features. Experimental result shows that the suggested methods can be used to identify the pain states of an infant. © (2014) Trans Tech Publications, Switzerland.


Mansor M.N.,University Malaysia Perlis | Rejab M.N.,Politeknik Tuanku Syed Sirajuddin
Applied Mechanics and Materials | Year: 2014

Image analysis of infant pain has been proven to be an excellent tool in the area of automatic detection of pathological status of an infant. This paper investigates the application of parameter weighting for invariant moments to provide the robust representation of infant pain images. Two classes of infant images were considered such as normal images, and babies in pain. A Similar Classifier is suggested to classify the infant images into normal and pathological images. Similar Classifier is trained with different spread factor or smoothing parameter to obtain better classification accuracy. The experimental results demonstrate that the suggested features and classification algorithms give very promising classification accuracy of above 89.54% and it expounds that the suggested method can be used to help medical professionals for diagnosing pathological status of an infant from face images. © (2014) Trans Tech Publications, Switzerland.


Mansor M.N.,University Malaysia Perlis | Rejab M.N.,Politeknik Tuanku Syed Sirajuddin
Applied Mechanics and Materials | Year: 2014

Infant pain is a non-stationary made by infants in response to certain situations. This infant facial expression can be used to identify physical or psychology status of infant. The aim of this work is to compare the performance of features in infant pain classification. Fast Fourier Transform (FFT), and Singular value Decomposition (SVD) features are computed at different classifier. Two different case studies such as normal and pain are performed. Two different types of radial basis artificial neural networks namely, Probabilistic Neural Network (PNN) and General Regression Neural Network (GRNN) are used to classify the infant pain. The results emphasized that the proposed features and classification algorithms can be used to aid the medical professionals for diagnosing pathological status of infant pain. © (2014) Trans Tech Publications, Switzerland.


Mansor M.N.,Intelligent Group | Jamil S.H.-F.S.M.,Politeknik Tuanku Syed Sirajuddin | Rejab M.N.,Politeknik Tuanku Syed Sirajuddin | Jamil A.H.-F.S.M.,Politeknik Tuanku Syed Sirajuddin
Proceedings - 2012 International Symposium on Instrumentation and Measurement, Sensor Network and Automation, IMSNA 2012 | Year: 2012

This paper come out with an infant behaviour recognition scheme based on neural network. In this study, the infant face region is segmented based on the skin colour information. Two types of features, namely Singular Value Decomposition (SVD) and Power Spectrum are then calculated based on the information available from the infant face regions. Since each type of features in turn contains several different values, given a single fifteen-frame sequence, the correlation coefficients between those features of the same type can form the attribute vector of pain and normal facial expressions. Fifteen infant facial expression classes have been defined in this study. Neural Network corresponding to each type of those features has been constructed in order to classify these facial expressions. The experimental results show that the proposed method is robust and efficient. The properties of the different types of features have also been analyzed and discussed. © 2012 IEEE.

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