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
    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
    ;
    Ahmad Firdaus Ahmad Zaidi
    ;
    Tengku Sarah Tengku Amran
    ;
    Mohamad Ridzuan Ahmad
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    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
    Assessment of Control Drive Technologies for Induction Motor: Industrial Application to Electric Vehicle
    ( 2021-06-11)
    Ahmad Firdaus Ahmad Zaidi
    ;
    Syahrul Ashikin Azmi
    ;
    Kamarulzaman Kamarudin
    ;
    Leong Jenn Hwai , Jenn
    ;
    Hasimah Ali
    ;
    Mohd Shuhanaz Zanar Azalan
    ;
    Zamri Che Mat Kasa
    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 of Body Mass Index Based Facial Images using Empirical Mode Decomposition
    ( 2021-06-11)
    Hasimah Ali
    ;
    Yee, O.S.
    ;
    Wan Khairunizam Wan Ahmad
    Human faces contain rich information. Recent studies found that facial features have relation with human weight or body mass index (BMI). Decoding "facial information"from the face in predicting the BMI could be linked to the various health marker. This paper proposed the classification of body mass index (BMI) based on appearance based features of facial images using empirical mode decomposition (EMD) as feature extraction technique. The facial images that describe the body mass index was extracted using EMD to obtain a set of significant features. In this framework, the facial image was decomposed using EMD to produce a small set of intrinsic mode functions (IMF) via sifting process. The IMF features which exhibit the unique pattern were used to classify the BMI. The obtained features were then fed into machine learning classifier such as k-nearest neighbour and support vector machines (SVM) to classify the three BMI classes namely normal, overweight and obese. The obtained results show that the IMF2 feature using SVM classifier achieved recognition rate of 99.12% which show promising result.
  • Publication
    Investigation on Body Mass Index Prediction from Face Images
    ( 2021-03-01)
    Chong Yen Fook
    ;
    Lim Chee Chin
    ;
    Vikneswaran Vijean
    ;
    Lim Whey Teen
    ;
    Hasimah Ali
    ;
    Aimi Salihah Abdul Nasir
    Body mass index is a measurement of obesity based on measured height and weight of a person and classified as underweight, normal, overweight and obese. This paper reviews the investigation and evaluation of the body mass index prediction from face images. Human faces contain a number of cues that are able to be a subject of a study. Hence, face image is used to predict BMI especially for rural folks, patients that are paralyzed or severely ill patient who unable to undergoes basic BMI measurement and for emergency medical service. In this framework, 3 stages will be implemented including image pre-processing such as face detection that uses the technique of Viola-Jones, iris detection, image enhancement and image resizing, face feature extraction that use facial metric and classification that consists of 3 types of machine learning approaches which are artificial neural network, Support Vector Machine and k-nearest neighbor to analyze the performance of the classification. From the results obtained, artificial neural network is the best classifier for BMI prediction system with the highest recognition rate of 95.50% by using the data separation of 10% of testing data and 90% of training data. In a conclusion, this system will help to advance the study of social aspect based on the body weight.
  • Publication
    Correlation Analysis of Emotional EEG in Alpha, Beta and Gamma Frequency Bands
    ( 2021-08-27)
    Choong W.Y.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Wan Azani Wan Mustafa
    ;
    Murugappan M.
    ;
    Asna Rasyidah Abdul Hamid
    ;
    Bong S.Z.
    ;
    Yuvaraj R.
    ;
    Mohd Iqbal Omar
    ;
    Ahmad Kadri Junoh
    ;
    Hasimah Ali
    ;
    Zuradzman Mohamad Razlan
    ;
    Shahriman Abu Bakar
    It is aimed at finding the correlation between EEG channels from six induced emotions in normal subjects. The multichannel EEG data was measured by Pearson's correlation coefficient to investigate the linear relationship between channel pairs in alpha, beta and gamma EEG frequency sub-bands. The EEG data were collected from 12 healthy subjects, with six induced emotions by audio-visual stimuli, which were anger, disgust, fear, happiness, sadness and surprise. The 14-channel wireless Emotiv Epoc was used for data collection. The results show that the EEG channels in alpha band was relatively higher correlation than in beta and gamma bands. The highest correlation for all emotions in alpha band were the channel pairs in right frontal region, FC6-F4 and F4-AF4. In beta and gamma bands, the highest correlation pairs involved the right frontal, occipital and parietal regions, which were FC6-F4 and O2-P8.
  • Publication
    A cascade hyperbolic recognition of buried objects using hybrid feature extraction in ground penetrating radar images
    ( 2021-08-27)
    Hasimah Ali
    ;
    Ahmad Firdaus Ahmad Zaidi
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Mohd Shuhanaz Zanar Azalan
    ;
    Tengku Amran T.S.
    ;
    Ahmad M.R.
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    Ground penetrating radar (GPR) has been acknowledged as effective nondestructive technique for imaging the subsurface. But the process of recognizing hyperbolic pattern of buried objects is subjective and mainly relies upon operator's knowledge and experience. This project proposed a hyperbolic recognition of buried objects using hybrid feature extraction in GPR subsurface mapping. In this framework, a cascade hyperbolic recognition by means of Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) are used as hybrid feature recognizing hyperbolic of buried objects. The rationale for an initial focus on cascade hyperbolic recognition is motivated by unique features exhibits by EMD and DWT behaviour in characterizing the hyperbolic pattern which make them particularly well suited to utilities detection in GPR. A series of experiments has been conducted on hyperbolic pattern based on hybrid features using four different geometrical shapes of cubic, cylindrical disc and spherical. Based on the results obtained, the hybrid features of IMF1+ wavelet transform (cH1) shows promising recognition rate in recognizing the hyperbolic that having different geometrical shapes of buried objects.
  • Publication
    GPSR Routing Performances Enhancement for VANET networks with Taguchi Optimization Mechanism
    ( 2021-07-26)
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    Dafhalla A.K.Y.
    ;
    Naimah Yaakob
    ;
    Hasimah Ali
    Routing mechanism plays an important role in the performances of Vehicular Ad Hoc Networks (VANET). Hence, various routing mechanisms are proposed to enhance VANET performances, however few researches are dedicated to optimize these routing mechanisms. In this paper an optimization mechanism is proposed to improve the performances of Greedy Perimeter stateless Routing (GPSR) protocol. Design of Experiments is used along with Taguchi Optimization method to fine tune GPSR internal routing parameters against VANET network scenarios. The target of optimization in this work is set to network performances including network throughput, delay and packet delivery ration (PDR). These targets are mathematically combined to form a single optimization target. A simulation experiments are performed to evaluate VANET performances. Obtained results showed that the proposed optimization improves the VANET performances in terms of throughput, PDR and delay. Further real-time integration of Optimization and routing mechanism can improve network performances.
  • Publication
    Autonomous Vehicle: Introduction and Key-elements
    ( 2021-08-27)
    Hafiz Halin
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Hasimah Ali
    The development of autonomous vehicles is undergoing extensive research because the autonomous system must ensure passenger safety. Consumers are concerned about vehicle safety, data privacy, system safety, and the autonomous vehicle's legal liability. Autonomous develop based on several key elements; perception, data processing, path planning, and control system. Comfort and a safe autonomous system can be achieved by creating a controller that can imitate human intelligence and decision-making ability. The proposed controller will be developed from an analysis of the human driving characteristic. The Allied Research Market forecast the autonomous vehicle industries can generate a lot of revenue in the future.
  • Publication
    Correlation Analysis of Emotional EEG in Alpha, Beta and Gamma Frequency Bands
    ( 2021-08-27)
    Choong W.Y.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Mustafa W.A.
    ;
    Murugappan M.
    ;
    Hamid A.
    ;
    Bong S.Z.
    ;
    Yuvaraj R.
    ;
    Omar M.I.
    ;
    Ahmad Kadri Junoh
    ;
    Hasimah Ali
    ;
    Zuradzman Mohamad Razlan
    ;
    Shahriman Abu Bakar
    It is aimed at finding the correlation between EEG channels from six induced emotions in normal subjects. The multichannel EEG data was measured by Pearson's correlation coefficient to investigate the linear relationship between channel pairs in alpha, beta and gamma EEG frequency sub-bands. The EEG data were collected from 12 healthy subjects, with six induced emotions by audio-visual stimuli, which were anger, disgust, fear, happiness, sadness and surprise. The 14-channel wireless Emotiv Epoc was used for data collection. The results show that the EEG channels in alpha band was relatively higher correlation than in beta and gamma bands. The highest correlation for all emotions in alpha band were the channel pairs in right frontal region, FC6-F4 and F4-AF4. In beta and gamma bands, the highest correlation pairs involved the right frontal, occipital and parietal regions, which were FC6-F4 and O2-P8.