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
Main Affiliation
Scopus Author ID
55992689600
Researcher ID
S-8233-2019

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  • Publication
    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.
  • 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
    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.
      1