Hasimah Ali
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Preferred name
Hasimah Ali
Official Name
Ali, Hasimah
Alternative Name
Ali, H.
Ali, H
Ali, Hashimah
Ali, H. I.
Bt Ali, Hasimah
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Scopus Author ID
57218540740
Researcher ID
EKZ-6160-2022

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  • Publication
    Recognition of different utility pipes size of ground penetrating radar images at different penetration depth
    ( 2024-02-08)
    Nasri M.I.S.
    ;
    Hasimah Ali
    ;
    Zaidi A.F.A.
    ;
    Shukor S.A.A.
    ;
    Ahmad M.R.
    ;
    Amran T.S.T.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Othman S.M.
    ;
    Elshaikh M.
    Ground Penetrating Radar (GPR) is a geophysical locating method that uses radio waves to capture images below the surface of the ground in a minimally invasive way. It also requires two main essential equipment which is a transmitter and a receiving antenna. To address the problem, this project proposed the hyperbolic recognition of different utility pipes of GPR images at different level of penetration depth. In this framework, the raw data of GPR images were firstly to be pre-processed. The grayscale images were cropped, resized, and enhanced to increase the contrast of the features of the image. Then, the pre-processed GPR images were extracted using the Histogram of Oriented Gradient (HOG) method with three different windows. The extracted HOG features were then used as input to the k-Nearest Neighbor classifier. A series of experiments has been conducted using 10-fold cross-validation technique for training and testing the GPR data. Based on the result obtained, it shows that at depth 20cm the average accuracy is about 99.87%, whereas at depth 40cm the average accuracy achieved 100%. Thus, the result shows that the extracted HOG features exhibit the significant information of hyperbolic signature of different pipe size with different depth of buried object. Therefore the results seem promising in recognizing the hyperbolic of utilities.
  • Publication
    Feature extraction using Radon transform and Discrete Wavelet Transform for facial emotion recognition
    ( 2017-02-08)
    Hasimah Ali
    ;
    Vinothan Sritharan
    ;
    Muthusamy Hariharan
    ;
    Siti Khadijah Za'aba
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    This paper presents a new pattern framework of using Radon and wavelet transform for facial emotion recognition. The Radon transform is translation and rotation invariants, hence it preserves the variations in pixel intensities. In this work, Radon transform has been used to project the 2D image into Radon space before subjected to Discrete Wavelet Transform (DWT). In DWT framework, the approximate coefficients (cA2) at second level decomposition are extracted and used as informative features to recognize the facial emotion. Since there are a large number of coefficients, hence the principal component analysis (PCA) is applied on the extracted features. The k-nearest neighbor classifier is adopted as classifier to classify seven (anger, disgust, fear, happiness, neutral, sadness and surprise) facial emotions. To evaluate the effectiveness of the proposed method, the JAFFE database has been employed. Based on the results obtained, the proposed method demonstrates the recognition rate of 91.3%, thus it is promising.
  • Publication
    Assessment of Control Drive Technologies for Induction Motor: Industrial Application to Electric Vehicle
    ( 2021-06-11)
    Ahmad Firdaus A.Z.
    ;
    Azmi S.A.
    ;
    Kamarulzaman Kamarudin
    ;
    Leong Jenn Hwai , Jenn
    ;
    Hasimah Ali
    ;
    Mohd Shuhanaz Zanar Azalan
    ;
    Kasa Z.C.M.
    Nowadays electric vehicle has increasingly gained much popularity indicated by growing global share market targeted at 30% by 2030 after recording 7.2million global stock in 2019. Compared to Internal Combustion Engine (ICE) counterpart, Battery Electric Vehicles (BEV) produce zero tailpipe emission which greatly reducing carbon footprints. Induction motor has been widely used and its control technology has evolved from scalar type volt/hertz to recent predictive control technology. This allows induction motor's application to expand from being the workhorse of industry to become prime mover in electric vehicle, where high performance is expected. Among vector control scheme, Direct Torque Control (DTC) has gained interest over Field Oriented Control (FOC) with simpler structure, better robustness and dynamics performance yet suffer from high torque and flux ripple. In electric vehicle applications, high ripple at low speed is highly undesirable, potentially causing torsional vibration. High performance control requires speed sensor integration, which often increase complexity in the design. The work aims to review the best control technology for induction motor in electric vehicle application through performance parameter evaluation such as improvement on dynamic response, torque and flux ripple reduction, and component optimization. Several arise issues in motor control and possible methods to circumvent are highlighted in this work. In conclusion, model predictive torque control (MPTC) is the most promising scheme for electric vehicle with excellent dynamic response, good low speed performance, and 50% torque ripple reduction compared to conventional DTC and potential integration with sliding mode observer for sensorless solution.
  • Publication
    Classification Size of Underground Object from Ground Penetrating Radar Image using Machine Learning Technique
    ( 2023-01-01)
    Mohd Shuhanaz Zanar Azalan
    ;
    Esian T.
    ;
    Hasimah Ali
    ;
    Ahmad Firdaus Ahmad Zaidi
    ;
    Amran T.S.T.
    Ground Penetrating Radar (GPR) is a useful tool in detecting subsurface object or hidden structure defects However, the time-consuming problems and high requirement of professional manpower is required to analyse the GPR data. Machine learning is a tool that endowed with the ability to learn, and it can reduce time taken for the GPR data analysing. To simplify the identification process, a framework is proposed to classify the size of underground metallic pipe by using Histogram of Oriented Gradient (HOG) as a feature extraction algorithm. Two machine learning algorithms namely Support Vector Machines (SVM) and Backpropagation Neural Network were proposed to classify the size of the underground metallic pipe. As a result, the accuracy from the identification is more than 98% for both classifier algorithm.
  • Publication
    dSPACE Implementation of Motor Drives using Asymmetric Converter
    ( 2024-02-01)
    Ahmad Firdaus Ahmad Zaidi
    ;
    Azmi S.A.
    ;
    Hwai L.J.
    ;
    Kamarudin K.
    ;
    Hasimah Ali
    ;
    Mohd Shuhanaz Zanar Azalan
    ;
    Noor A.M.
    ;
    Ramzan N.I.
    This paper deals with the dSPACE DS1104 based implementation of closed-loop motor drive system using asymmetric converter. The mathematical model of the drive has been simulated in the MATLAB/Simulink environment to analyze the performance of the drive system. The simulated results are then validated with the experimental investigation. For experimental work, the pulse width modulation (PWM) has been implemented in MATLAB environment with Simulink real-time interface. Meanwhile, the hardware implementation consisting of dSPACE digital signal processor, voltage source inverter and generator-coupled motor. Variable speed test was performed on the loaded motor in open loop and closed-loop design to obtain speed tracking response parameter as well as speed ripple. Overall performance of developed system is satisfactory where in low-speed operation, experimental results show good speed tracking performance with ripple within 20%.
  • Publication
    Ground penetrating radar for buried utilities detection and mapping: a review
    ( 2021-12-01)
    Hasimah Ali
    ;
    Ideris N.S.M.
    ;
    Ahmad Firdaus Ahmad Zaidi
    ;
    Mohd Shuhanaz Zanar Azalan
    ;
    Amran T.S.T.
    ;
    Ahmad M.R.
    ;
    Rahim N.A.
    ;
    Shazmin Aniza Abdul Shukor
    This paper presents a review on Ground Penetrating Radar (GPR) detection and mapping of buried utilities which have been widely used as non-destructive investigation and efficiently in terms of usage. The reviews cover on experimental design in GPR data collection and survey, pre-processing, extracting hyperbolic feature using image processing and machine learning techniques. Some of the issues and challenges facing by the GPR interpretation particularly in extracting the hyperbolas pattern of underground utilities have also been highlighted.
  • Publication
    Investigation of nonlinear feature extraction techniques for facial emotion recognition
    ( 2016)
    Hasimah Ali
    Over the last decades, facial emotion recognition has received a significant interest among researchers in areas of computer vision, pattern recognition and its related field. The increasing applications of facial emotion recognition have shown a sizeable impact in many areas ranging from psychology to human-computer interaction (HCI). Although facial emotion recognition has achieved a certain level of success, however its performance is far from human perception. Many approaches have been constantly proposed in the literature. In fact, the ability of facial emotion recognition to operate in fully automated with high accuracy remains challenging due to various problems such as intra-class variations, inter-class similarities and subtle changes of facial features. The adhered problem is further hampered as physiognomies of faces with respect to age, ethnicity and gender, thus increase the difficulties of recognizing the facial emotion. In order to resolve this problem, this thesis aims to develop nonlinear features extraction techniques of using Higher Order Spectra (HOS) and Empirical Mode Decomposition (EMD) separately in recognizing the seven facial emotions (anger, disgust, fear, happiness, neutral, sadness and surprise) from static images. A pre-processing step of isolating face region from different background was first employed by means of face detection. The 2-D facial image was then projected into 1-D facial signal by successive projection via Radon transform. Radon transform is translation and rotation invariant, hence preserves the variations in pixel intensities. The facial signal that describes the expression was extracted using HOS and EMD to obtain a set of significant features. In HOS framework, the third order statistic or bispectrum that captures contour (shape) and texture information was applied on facial signal. In this work, a new set of bispectral features was used to characterize the distinctive features of seven classes of emotion. While, in EMD framework, the facial signal was decomposed using EMD to produce a small set of intrinsic mode functions (IMFs) via sifting process. The IMF features which exhibit the unique pattern were used to differentiate the facial emotions.
  • Publication
    Feature Extraction based on Empirical Mode Decomposition for Shapes Recognition of Buried Objects by Ground Penetrating Radar
    ( 2021-06-11)
    Hasimah Ali
    ;
    Mohd Shuhanaz Zanar Azalan
    ;
    Zaidi A.F.A.
    ;
    Amran T.S.T.
    ;
    Ahmad M.R.
    ;
    Elshaikh M.
    Ground penetrating radar (GPR) is one of the promising non-destructive imaging tools investigations for shallow subsurface exploration such as locating and mapping the buried utilities. In practical applications, GPR images could be noisy due to the system noise, the heterogeneity of the medium, and mutual wave interactions thus, it is a complex task to recognizing the hyperbolic signature of buried objects from GPR images. Therefore, this paper aims to develop nonlinear feature extraction technique of using Empirical Mode Decomposition (EMD) in recognizing the four geometrical shapes (cubic, cylindrical, disc and spherical) from GPR images. A pre-processing step of isolating hyperbolic signature from different background was first employed by mean of Region of Interest (ROI). The hyperbolic signature that describes the shapes was extracted using EMD decomposition to obtain a set of significant features. In this framework, the hyperbolic pattern was decomposed of using EMD, to produce a small set of intrinsic mode functions (IMF) via sifting process. The IMF properties of the signature that exhibit the unique pattern was used as potential features to differentiate the geometrical shapes of buried objects. The extracted IMF features were then fed into machine learning classifier namely Support Vector Machines. To evaluate the effectiveness of the proposed method, a set data collection of GPR-images has been acquired. The experimental results show that the recognition rate of using IMF features was achieved 99.12% accuracy in recognizing the shapes of buried objects whose shows the promising result.
  • Publication
    Hyperbola detection of ground penetrating radar using deep learning
    ( 2024-02-08)
    Zahir N.H.M.
    ;
    Hasimah Ali
    ;
    Nasri M.I.S.
    ;
    Masuan N.A.
    ;
    Zaidi A.F.A.
    ;
    Mohd Shuhanaz Zanar Azalan
    ;
    Amin M.S.M.
    ;
    Ahmad M.R.
    ;
    Elshaikh M.
    Ground Penetrating Radar (GPR) is a geophysical method using high resolution electromagnetic used to acquire the information of underground. The electromagnetic (EM) waves produces from the antenna consisting of transmitter and receiver. The waves from the transmitter penetrates into the ground and reflect backs to the surface that receive by the antenna receiver. The antenna can lie within the range of 10MHz to 1000MHz to determine the shallow or deep penetration. Higher value of antenna will result in shallow penetration and otherwise for lower antenna. The process of recognition of buried objects is challenging task especially in the construction area to ensure safety and the quality of civil building. The GPR will display the mapping image on its control unit screen. If there are objects underground have detected, the image will display the hyperbola shape to indicate the target of the object. A vast number of data makes it difficult to classify each and every one of it either the image data is in which classes or categories. If there are many hyperbola present in image also makes it difficult to locate the accurate position. Due to this, deep learning technique by means of ResNet50 has been used in this research for hyperbola recognition in GPR image. A series of experiments has been conducted on the GPR dataset collected at Agency Nuclear Malaysia. Based on the results obtained, the deep learning model successfully learn the image feature. The accuracy of the model classified for this GPR data using ResNet50 gives 90% accuracy. Therefore, the proposed method for image recognition shows the promising results with all the GPR images are correctly recognize. Further, region of interest of hyperbola signature has been represented by a rectangular box indicates the hyperbola location
  • Publication
    Reconstruction hyperbola signature of underground object using GPR images for mapping applications
    ( 2024-02-08)
    Masuan N.A.
    ;
    Hasimah Ali
    ;
    Amran T.S.T.
    ;
    Ahmad Firdaus Ahmad Zaidi
    ;
    Kamarudin K.
    ;
    Ahmad M.R.
    Ground penetrating radar has been acknowledged as an effective and efficient technique for non-destructive investigation for near-subsurface exploration that is based on the reflection receiver-transmitter of the antenna when hitting buried objects. An accurate interpretation of GPR data is greatly important in locating and mapping underground objects. Although GPR research has achieved remarkable success, the interpretation of GPR raw data highly depends on the reliance of user experts. Further, unexperienced GPR users are subject to error since the hyperbola signatures may resemble each other. Therefore, this work focuses on the development of a 3D reconstruction of the hyperbola signature of underground objects using GPR images for mapping applications. In this study, 3D reconstruction has been developed based on the Synthetic Aperture Focusing Technique, also known as SAFT. At the first stage, the raw input of GPR images was subjected to zero-time correction and background elimination. Next is the projection of each hyperbola signature by means of B-Scan images to create a 3D image. Then, the resultant 3D images were stacked together, and further 3D interpolation techniques were employed on the images. The experimental studies have been done on GPR data using a metal sphere as a sample. The findings of the study highlight that the SAFT method was able to reconstruct the 3D model of the hyperbola signature and exhibit the ability to provide clues about the location of the underground object through the representation of the voxel point of the images. Based on these results, the SAFT technique provides good insight into the 3D reconstruction of hyperbola signatures using GPR images in mapping applications.