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
    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
    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
    Smart irrigation system based IoT for indoor housing farming
    ( 2024-02-08)
    Hasimah Ali
    ;
    Nidzamuddin S.A.H.S.
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    Irrigation system is widely used in agriculture sector and has significant impacts to the growth of the plantation or crops. Traditional method of irrigation system always counter problems such as time consuming, human labour cost, inefficient of water usage and monitoring challenging throughout the process. Thus to address the issues, this paper proposed the development of smart irrigation system that embedded various types of sensor and Internet of Things (IoT) platform used for monitoring plant growth. In this work, there are three module have been developed which are hardware, software and integration module of the proposed system. In hardware module, Raspberry Pi is used to calculate and process the data based on the sensors parameters. Different types of sensors have been employed such as soil moisture, humidity, temperature, ultrasonic and vision sensors. In this framework, the reading of soil moisture sensor was obtained from the base station. The Raspberry Pi will receive the information and starts to pump the water from the tank until the condition of soil moisture content is normal (i.e. reach the threshold value). In addition, the DHT22 sensor will act as the monitoring system in terms of temperature and humidity data. While, the ultrasonic sensor will send the information to the microprocessor and calculate the water level. Furthermore, the webcam vision is used for monitoring the plant growth during the day and night. While, the dripping process runs in real-time application to the plant. The microcontroller ESP8266 used to control the state of ON or OFF light bulb depending on the value of LDR sensor. Based on the results and monitoring process, the proposed smart irrigation system able to works in promising environment with real time data in which it has been monitored through the IoT platform.
      1  6
  • 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.
      1
  • Publication
    Rebar Path Mapping using Ground Penetrating Radar
    ( 2023-01-01)
    Basri N.A.B.
    ;
    Hasimah Ali
    ;
    Ahmad M.R.
    ;
    Ahmad Firdaus Ahmad Zaidi
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Jusman Y.
    Ground penetrating radar (GPR) is a non-destructive device that helps to determine the position and direction of underground utilities such as rebar while preventing any inaccurate excavation process. The direction of buried rebar is usually mapped using the X-Y grid scanning method, which requires a lot of manpower and time to complete. Therefore, this paper investigated the ability of parallel scanning of B-scan to imitate the result of C-scan produced by X-Y grid scanning. Parallel scanning has been emphasised to reduce the time consumption of the data acquisition process while delivering a quality output. To develop a rebar path mapping, a data processing step has been implemented on the B-scan data for seven parallel lines that correspond to the x-axis. Next, Kirchhoff migration has been applied along with stacking and interpolation techniques to map a two-dimensional (2-D) image of the buried rebar. The obtained result was then compared with the grid scanning data of C-scan to evaluate the correlation between them. The performance of the mapped rebar path using parallel B-scan data was evaluated based on the ability of the data to give an accurate depth calculation of the buried rebar. Ultimately, the results show that this proposed method for using parallel B-scan to do mapping is verified.
      1
  • 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.
      15  2
  • 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.
      3  2
  • Publication
    Design and Development of Cascaded Current Control in DC Motor Variable Speed Drive using dSPACE
    ( 2023-01-01)
    Ahmad Firdaus Ahmad Zaidi
    ;
    Davendren T.
    ;
    Syahrul Ashikin Azmi
    ;
    Leong Jenn Hwai , Jenn
    ;
    Kamarulzaman Kamarudin
    ;
    Hassan A.
    ;
    Hasimah Ali
    ;
    Mohd Shuhanaz Zanar Azalan
    ;
    Normahira Mamat @ Mohamad Nor
    Even today, DC motors are still used in variety of applications, including home appliances, transportation, as well as industrial crane and rolling machine. However, achieving precise speed and torque control in DC drives at industry level could be challenging, as instability and reduced efficiency remains at large. This project focuses on developing a cascaded control system for a Separately Excited Brushed DC motor using dSPACE platform. The cascaded control system, designed using MATLAB Simulink, incorporates a proportional-integral (PI) controller at the speed loop and a Hysteresis controller at the current loop to improve robustness and dynamic performance. The experimental setup utilizes the dSPACE 1104 platform, an asymmetric bridge converter board, gate driver, and electrical load. Speed measurement is done using an incremental encoder, while current is measured using the ACS712 current sensor. The drive system was tested in alternate low and high speed cycle on various load level to test for stability, robustness and dynamic performance. The proposed control system was compared with PI-closed-loop control and open-loop control determine the best drive performance. Experimental results showed significant improvement in term of transient response and ripple reduction of speed and current for proposed cascaded current control over the closed-loop design.
      1
  • 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.
      1
  • Publication
    3D Reconstruction of embedded object using ground penetrating radar
    ( 2023-01-01)
    Fadil N.D.
    ;
    Hasimah Ali
    ;
    Ahmad Firdaus Ahmad Zaidi
    ;
    Kamal W.H.B.W.
    ;
    Basri N.A.M.
    Ground Penetrating Radar (GPR) is a non-destructive device widely used to locate and map underground utilities such as pipes, cables, etc. Its principle is based on the reflection signal of a transmitter-receiver antenna that strikes underground objects by means of the propagation of a short pulse of electromagnetic waves into the ground. The GPR will produce a hyperbolic curve as a result of the object's presence. Accurate interpretation of hyperbola curves is greatly important and highly depends on user expertise; thus, it is considered a challenge. To address this issue, this study aims to develop 3D reconstructions of embedded objects. In this study, C-scan images were acquired, and 3D interpolation and the Synthetic Aperture Focusing Technique (SAFT) were introduced. In this framework, the acquired data is subjected to pre-processing techniques via time-zero correction, background removal using average background subtraction, and Kirchoff's migration method. The software Reflex 3D Scan has been used to analyse and preprocess the 3D reconstruction of embedded objects. The obtained results show that 3D interpolation and SAFT methods are not only able to reconstruct 3D models but are also able to reveal information on the dimension and location of the buried object represented by voxel points in the 3D space cube.
      1  11