Mohd Shuhanaz Zanar Azalan
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Preferred name
Mohd Shuhanaz Zanar Azalan
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Mohd Shuhanaz , Zanar Azalan
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Zanar Azalan, M. S.
Azalan, Mohd Shuhanaz Zanar
Azalan, M. S. Z.
Azalan, M. S. Zanar
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Scopus Author ID
36469819700

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Investigating the Effect of EEG Channel Location on Emotion Classification using EEG Signal

2023-01-01 , Mohd Shuhanaz Zanar Azalan , Hisham N.A.W.N.N. , Ahmad Firdaus Ahmad Zaidi , Jusman Y.

EEG channel location on emotion classification and the choice of classifier are the factor that significantly impact the emotion recognition accuracy. Currently, there is a gap in the literature concerning the identification of optimal channel locations and classifier choice for effective emotion recognition. This study aims to investigate the influence of EEG channel electrode locations and classifiers on the accuracy of emotion recognition. Emotion classification was conducted using three classifiers namely Neural Network (NN), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) - on two EEG channels configuration with 62 channels and 32 channels, as well as five separate brain regions: Frontal, Temporal, Central, Parietal, and Occipital. The analysis was performed on the SEED-IV dataset. Features were extracted using Differential Entropy. Result indicates that SVM outperformed NN and KNN across all configurations. When assessing EEG channel location, the 32-channel setup yield higher average accuracies for both SVM (91.07%) and NN (86.63%), whereas the 62-channel setup was optimal for KNN (88.28%). The most significant results were identified in the Parietal and Occipital regions. SVM achieved the highest accuracies in these regions (96.55% in the Parietal and 97.18% in the Occipital), with NN (90.17% in Parietal and 92.65% in Occipital) and KNN (82.76% in Parietal and 92.81% in Occipital). These findings emphasize the crucial roles of the Parietal and Occipital regions, associated with sensory integration and visual processing, in emotion recognition. The study highlights the importance of EEG channel location and classifier selection in enhancing the reliability of EEG-based emotion recognition systems.

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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.

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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.

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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.

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Hyperbolic detection of ground penetrating radar for buried pipes utilities using Viola Jones

2025-06 , Hasimah Ali , Mohd Shuhanaz Zanar Azalan , Nurul Syahirah Mohd Ideris , Tengku Sarah Tengku Amran

GPR (Ground Penetrating Radar) is well-known as an effective non-invasive imaging approach for shallow nature underground discovery, like finding and locating submerged objects. Although GPR has achieved some success, it is difficult to automatically process GPR images because human experts must interpret GPR images of buried objects. This can happen due to the possibility of a variety of mediums or underground noises from the environment, especially rocks and roots of trees. Thus, detecting hyperbolic echo characteristics is critical. As a result, Viola Jones detection is used to determine whether the presence of a hyperbolic signature underground indicates a pipe or not. GPR can also be used in the public works department because it is a non destructive tool. Workers, for example, should be aware of the pipe size that must be replaced when it leaks. The original GPR image already shows hyperbolic image distortion due to pipe refraction. The current method is unreliable due to its lack of flexibility. As a result, there is another method for resolving this issue. Thus, the image will be pre-processed to eliminate or reduce background noise in the GPR input image. The results of this project demonstrate that the Viola Jones algorithm can accurately detect hyperbolic patterns in GPR images.

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Fuzzy Logic Cascaded Current Control of DC Motor Variable Speed Drive using dSPACE

2023-01-01 , Ahmad Firdaus Ahmad Zaidi , Ni L.P. , Leong Jenn Hwai , Jenn , Syahrul Ashikin Azmi , Kamarulzaman Kamarudin , Hasimah Ali , Mohd Shuhanaz Zanar Azalan , Siti Nurul Aqmariah Mohd Kanafiah , Jusman Y.

Two-wheel e-scooter falls under low power segment for Battery Electric Vehicle (BEV) and has gain more popularity in urban commuting. Most entry level e-scooter is still powered by DC motor due to low cost and ease of control. However basic open-loop DC Motor control employed through throttling is plugged with limited efficiency, precision, and range of speed control. Closed-loop control enables real time adjustment according to preset speed which becomes handy during auto cruising. To ensure good dynamic response, improved robustness and stable wide speed control range, a good control scheme for the motor is essential. In this project, a variable speed control scheme, namely fuzzy logic cascaded current control system was designed using MATLAB Simulink, comprising speed control loop and a current control loop 185 W Separately Excited Brushed DC Motor. The proposed control system was tested on hardware using dSPACE DS1104 platform. The system's output speed is obtained using an incremental encoder, while the output current is measured with a current sensor. Subsequently, the control system's stability, robustness, and dynamic performance were evaluated by driving the system on 120 W electrical load at varying speed. The system performance has proved superior to closed-loop by 70% on low speed ripple reduction and is on par with PI cascaded current control scheme.

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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%.

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Characterization of Hardening Duffing Oscillator based on a Tensioned Wire System

2021-06-11 , Muhajir Ab. Rahim , Mohd Shuhanaz Zanar Azalan , Mohd Noor Arib Md Rejab

In this paper, the characterization of mechanical system that behaves as a hardening Duffing oscillator is presented. This mechanical system comprises a mass attached to a tensioned wire which exhibits a hardening stiffness behavior when the displacement of the mass is large. Firstly, the equation of motion of the system is derived to provide the relationship between the applied static force and the resulting displacement. Then, the effect of initial tension, and number of the wires on the force-displacement relationship are analyzed. It has been found that a higher tension will produce higher linear stiffness, whilst having a negligible effect on cubic stiffness. Moreover, the nonlinearity is less sensitive for small inequality between the length of wire on the left and right side of the mass. The results presented herein provide an insight of the system behavior for its application as a vibration isolator.

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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.

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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.

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