Saidatul Norlyana Azemi
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Preferred name
Saidatul Norlyana Azemi
Official Name
Saidatul Norlyana, Azemi
Alternative Name
Azemi, Saidatu Norlyana
Azemi, S. N.
Azemi, S. A.
Saidatul, N. A.
Azemi, Saidatul N.
Azemi, Saidatul Norlyna
Main Affiliation
Scopus Author ID
55204806400
Researcher ID
DUZ-0499-2022

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  • Publication
    Gain Enhancement of CPW Antenna for IoT Applications using FSS with Miniaturize Unit Cell
    ( 2021-07-26)
    Saidatul Norlyana Azemi
    ;
    Mohd Rashidi Che Beson
    ;
    Amiza Amir
    ;
    Azhari M.S.B.A.
    ;
    Jiunn N.K.
    Wireless connectivity is a critical enabler for many IoT applications. Antennas are often required to be installed inside the device cover, which usually occurs in small sizes with optimal performance. On the other hand, a suitable antenna should also have high efficiency, gain and adequate bandwidth covering the desired frequency range. Here, we proposed new type of Frequency Selective Surface (FSS) with miniaturized resonator element to enhance the gain of an CPW antenna. Furthermore, the miniaturization of the Frequency Selective Surface unit cell is attained by coupling the two meandered wire resonators. The wire resonator is separated by thin and single substrate layer. The structure of the FSS is shown to have a FSS unit cell dimension that is miniaturized to 0.057λ. The CPW antenna size is only 28.8mm × 46.5mm operating at 2.45 GHz frequency. With the additional of the FSS, the antenna's gain reaches up from 1.8 dBi to 2.6 dBi with omnidirectional radiation pattern.
  • Publication
    A Free-Space measurement system for microwave materials at Kuband
    ( 2022-01-01)
    Sivakumar Renukka
    ;
    Lee Y.S.
    ;
    Saidatul Norlyana Azemi
    ;
    Zahari Awang Ahmad
    ;
    Jack Soh P.
    ;
    You K.Y.
    One of the non-resonant techniques is the free-space measurement technique, which is popular due to its many advantages compared to the other techniques. It allows the transmission and reflection measurements without any physical contact with the sample. This paper discusses the free-space material measurement system in Ku-band which uses the NRW algorithm and Keysight (Formerly Agilent) 85071E software in determining the dielectric properties of materials. The permittivity and permeability of Teflon, FR4, PVC, ABS, Acrylic, polypropylene, polycarbonate, and epoxy were determined using free space measurement setup. For the first, a free-space measurement for Ku-Band is setup. It consists of a vector network analyzer, two horn antennas, sample holder, and Keysight 85071E software. The different role of transmission and reflection measurements on the achievable results is analyzed about experimental uncertainties and different noise scenarios. Results from the two strategies are analyzed and compared. Good agreement between simulation, measurement, and literature was obtained.
  • Publication
    Microstrip Sensor Based on Ring Resonator Coupled with Double Square Split Ring Resonator for Solid Material Permittivity Characterization
    ( 2023-04-01)
    Masrakin K.
    ;
    Siti Zuraidah Ibrahim
    ;
    Hasliza A Rahim @ Samsuddin
    ;
    Saidatul Norlyana Azemi
    ;
    Soh P.J.
    ;
    Tantiviwat S.
    This paper analyzes a microwave resonator sensor based on a square split-ring resonator operating at 5.122 GHz for permittivity characterization of a material under test (MUT). A single-ring square resonator edge (S-SRR) is coupled with several double-split square ring resonators to form the structure (D-SRR). The function of the S-SRR is to generate a resonant at the center frequency, whereas D-SRRs function as sensors, with their resonant frequency being very sensitive to changes in the MUT’s permittivity. In a traditional S-SRR, a gap emerges between the ring and the feed line to improve the Q-factor, but the loss increases as a result of the mismatched coupling of the feed lines. To provide adequate matching, the microstrip feed line is directly connected to the single-ring resonator in this article. The S-SRR’s operation switches from passband to stopband by generating edge coupling with dual D-SRRs located vertically on both sides of the S-SRR. The proposed sensor was designed, fabricated, and tested to effectively identify the dielectric properties of three MUTs (Taconic-TLY5, Rogers 4003C, and FR4) by measuring the microwave sensor’s resonant frequency. When the MUT is applied to the structure, the measured findings indicate a change in resonance frequency. The primary constraint of the sensor is that it can only be modeled for materials with a permittivity ranging from 1.0 to 5.0. The proposed sensors’ acceptable performance was achieved through simulation and measurement in this paper. Although the simulated and measured resonance frequencies have shifted, mathematical models have been developed to minimize the difference and obtain greater accuracy with a sensitivity of 3.27. Hence, resonance sensors offer a mechanism for characterizing the dielectric characteristics of varied permittivity of solid materials.
      1
  • Publication
    Evaluating Tree-based Ensemble Strategies for Imbalanced Network Attack Classification
    ( 2024-01-01)
    Soon H.F.
    ;
    Amiza Amir
    ;
    Nishizaki H.
    ;
    Nik Adilah Hanin Zahri
    ;
    Latifah Munirah Kamarudin
    ;
    Saidatul Norlyana Azemi
    With the continual evolution of cybersecurity threats, the development of effective intrusion detection systems is increasingly crucial and challenging. This study tackles these challenges by exploring imbalanced multiclass classification, a common situation in network intrusion datasets mirroring realworld scenarios. The paper aims to empirically assess the performance of diverse classification algorithms in managing imbalanced class distributions. Experiments were conducted using the UNSW-NB15 network intrusion detection benchmark dataset, comprising ten highly imbalanced classes. The evaluation includes basic, traditional algorithms like the Decision Tree, KNearest Neighbor, and Gaussian Naive Bayes, as well as advanced ensemble methods such as Gradient Boosted Decision Trees (GraBoost) and AdaBoost. Our findings reveal that the Decision Tree surpassed the Multi-Layer Perceptron, K-Nearest Neighbor, and Naive Bayes in terms of overall F1-score. Furthermore, thorough evaluations of nine tree-based ensemble algorithms were performed, showcasing their varying efficacy. Bagging, Random Forest, ExtraTrees, and XGBoost achieved the highest F1-scores. However, in individual class analysis, XGBoost demonstrated exceptional performance relative to the other algorithms. This is confirmed by achieving the highest F1-scores in eight out of the ten classes within the dataset. These results establish XGBoost as a predominant method for handling multiclass imbalance classification with Bagging being the closest feasible alternative, as Bagging gains an almost similar accuracy and F1-score as XGBoost.
      3