Najah Ghazali
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Preferred name
Najah Ghazali
Official Name
Najah, Ghazali
Alternative Name
Ghazali, Najah
Ghazali, N.
Main Affiliation
Scopus Author ID
57219058546
Researcher ID
EVM-5966-2022

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  • Publication
    Satellite attitude estimation in simulated non-Gaussian white noise using Particle Filter and Extended Kalman Filter
    (Universiti Malaysia Perlis, 2023-11-10)
    Nor Hazadura Hamzah
    ;
    Teoh Vil Cherd
    ;
    Mohd Zamri Hasan
    ;
    Najah Ghazali
    Extended Kalman filter (EKF) has been found as most widely used algorithm for state estimation due to its simplicity for implementation and theoretically attractive in the sense that minimizes the variance of the estimation error. Nevertheless it is known that EKF algorithm strictly assumed that the nature of the noise or errors in the system is Gaussian white noise. Yet, in real world this is not always true, which will lead to less accurate estimation. However there is an estimation approach that does not require the assumption of a specific noise as EKF which is particle filter (PF), which hypothetically can provide more accurate estimation under non-Gaussian noise condition. Hence, this work will study and compare accuracy performance of both estimation algorithms in simulated non-Gaussian white noise for satellite attitude application.
      1  9
  • Publication
    Inhalation and Exhalation Detection for Sleep and Awake Activities Using Non-Contact Ultra-Wideband (UWB) Radar Signal
    ( 2021-03-01)
    Fatin Fatihah Shamsul Ariffin
    ;
    Latifah Munirah Kamarudin
    ;
    Najah Ghazali
    ;
    Nishizaki H.
    ;
    Ammar Zakaria
    ;
    Syed Muhammad Mamduh Syed Zakaria
    Respiratory is one of the vital signs used to monitor the progression of the illness that are important for clinical and health care fields. From home rehabilitation to intensive care monitoring, the rate of respiration must be constantly monitored as it offers a proactive approach for early detection of patient deterioration that can be used to trigger therapeutic procedures alarms. The use of invasive procedures based on contact transducers is typically necessary to measure the quantity. Nevertheless, these procedures might be troublesome due to the inconvenience and sensitivity of physical contact. Therefore, non-contact human breathing monitoring as a non-invasive procedure is important in long term intensive-care and home healthcare applications. In this paper, respiratory signals from two type of resting activities had been acquired and proposed a Deep Neural Network (DNN) model that can classify the respiratory signal into inhalation and exhalation signal. Several pre-processing techniques has been done onto the signal before it is implemented into the proposed model. The average recognition rate of the respiratory signal using the proposed method was 84.1% when the subject was sleeping and 83.8% when awake.
      2  17