Latifah Munirah Kamarudin
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Preferred name
Latifah Munirah Kamarudin
Official Name
Kamarudin, Latifah Munirah
Alternative Name
Kamarudin, Latifah Munirah
Kamarudin, Latifah M.
Kamarudin, L. M.
Kamarudin, Munirah L.
Kamarudin, L.
Main Affiliation
Scopus Author ID
57192974774
Researcher ID
G-8267-2016

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  • Publication
    Non-Contact Breathing Signal Classification Using Hybrid Scalogram Image Representation Feature
    ( 2022-01-01)
    Muhammad Husaini
    ;
    Latifah Munirah Kamarudin
    ;
    Nishizaki Hiromitsu
    ;
    Kamarudin I.K.
    ;
    Ibrahim M.A.
    ;
    Ammar Zakaria
    ;
    Toyoura M.
    ;
    Mao X.
    When monitoring human vital signs, breathing is one of the most critical physiological metrics. In areas with limited resources and a shortage of trained medical professionals, automated analysis of abnormal breathing patterns may prove advantageous to healthcare systems. In this paper, we implemented the architecture of five transfer learning models to classify individuals' breathing patterns using our proposed method which uses hybrid scalogram image-based features. We implemented the Sleep Breathing Detection Algorithm (SBDA) for extracting the actual breathing signals from ultra-wideband (UWB) radar for the pre-processing method. Later, the signals were converted to hybrid scalogram image-based representations before being classified using the VGG16, DenseNet, Xception, ResNet, and MobileNet models. The performance of the proposed method was validated using two other image representations: a standard image and a spectrogram image. The overall result showed that the proposed method obtained the highest classification accuracy on the test set for all pre-trained models.
      3  13
  • Publication
    3D grape bunch model reconstruction from 2D images
    ( 2023)
    Woo Yan San
    ;
    Zhuguang Li
    ;
    Tamura Shun
    ;
    Buayai Prawit
    ;
    Nishizaki Hiromitsu
    ;
    Makino Koji
    ;
    Latifah Munirah Kamarudin
    ;
    Xiaoyang Mao
    A crucial step in the production of table grapes is berry thinning. This is because the market value of table grape production is significantly influenced by bunch compactness, bunch form and berry size, all of which are primarily regulated by this task. Grape farmers must count the number of berries in the working bunch and decide which berry should be eliminated during thinning, a process requiring extensive viticultural knowledge. However, the use of 2D pictures for automatic berry counting and identifying the berries to be removed has limitations, as the number of visible berries might vary greatly depending on the direction of view. In addition, it is extremely important to understand the 3D structure of a bunch when considering future automation via robotics. For the reasons stated, obtaining a field-applicable 3D grape bunch model is needed. Thus, the contribution of this study is a novel technology for reconstructing a 3D model of a grape bunch with uniquely identified berries from 2D images captured in the real grape field environment.
      1  21