Ahmad Firdaus Ahmad Zaidi
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Preferred name
Ahmad Firdaus Ahmad Zaidi
Official Name
Ahmad Firdaus, Ahmad Zaidi
Alternative Name
Ahmad Zaidi, Ahmad Firdaus
Ahmad Firdaus, A. Z.
Zaidi, Ahmad Firdaus Bin Ahmad
Firdaus, A. Z.Ahmad
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Scopus Author ID
55992689600
Researcher ID
S-8233-2019

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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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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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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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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.

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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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Hyperbolic recognition of buried pipes in ground penetrating radar images with the presence of scattering objects

2024-02-08 , Hasimah Ali , Razak M.H.A. , Nasri M.I.S. , Masuan N.A. , Amin M.S.M. , Ahmad Firdaus Ahmad Zaidi , Mohd Shuhanaz Zanar Azalan , Norasmadi Abdul Rahim

Ground Penetrating Radar (GPR) is a non-destructive test used as imaging tool for exploration of shallow subsurface such locating the buried infrastructures. Due to the existence of various subsurface material and environmental noise, such as bricks and tree branch, it is a challenging task to interpret the GPR data into a meaningful information. Thus, this project proposes the hyperbolic recognition of buried pipes in GPR images in the presence of scattering objects. In this framework, the GPR images were firstly subjected to image pre-processing. Then, the GPR images were decomposed using Discrete Wavelet Transform (DWT) to analyze the texture analysis of hyperbola signature. Then, the approximation subband of DWT were extracted and used as features to recognize the hyperbolic of buried pipes and scattering objects presence in GPR images. A series of experiment has been conducted on GPR data collection at Agency Nuclear Malaysia. Based on the results obtained, the average recognition rate of extracted approximation subband of DWT features using k-NN classifier is 99.75%, thus shows a promising results in recognizing the buried pipes in the presence of scattering objects.

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Effect of elevated temperature on the tensile strength of Napier/glass-epoxy hybrid reinforced composites

2017-11-07 , Mohd Ridzuan Mohd Jamir , Mohd Shukry Abdul Majid , Mohd Afendi Rojan , Ahmad Firdaus Ahmad Zaidi , Azduwin Khasri

The effects of elevated temperature on the tensile strength of Napier/glass-epoxy hybrid reinforced composites and its morphology of fractured surfaces are discussed. Napier/glass-epoxy hybrid reinforced composites were fabricated by using vacuum infusion method by arranging Napier fibres in between sheets of woven glass fibres. Napier and glass fibres were laminated with estimated volume ratios were 24 and 6 vol. %, respectively. The epoxy resin was used as matrix estimated to 70 vol. %. Specimens were tested to failure under tension at a cross-head speed of 1 mm/min using Universal Testing Machine (Instron) with a load cell 100 kN at four different temperatures of RT, 40°C, 60°C and 80°C. The morphology of fractured surface of hybrid composites was investigated by field emission scanning electron microscopy. The result shows reduction in tensile strength at elevated temperatures. The increase in the temperature activates the process of diffusion, and generates critical stresses which cause the damage at first-ply or at the centre of the hybrid plate, as a result lower the tensile strength. The observation of FESEM images indicates that the fracture mode is of evolution of localized damage, from fibre/matrix debonding, matric cracking, delamination and fibre breakage.

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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 , 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.

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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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