International Journal of Advanced Communication Technology (IJACT)

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https://hdl.handle.net/20.500.14170/3409
International Journal of Advanced Communication Technology (IJACT) is a peer-reviewed international journal published once a year. IJACT currently registered on MyJurnal website. IJACT includes original research papers, short notes on theoretical and experimental research in English and selected conference papers in a special issue on the state of research and technical development in photonics, wireless and computing fields. Journal homepage

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Recent Submissions

Now showing 1 - 2 of 2
  • Publication
    Feasibility study on embroidered Wi-Fi antenna performances
    ( 2021)
    Che Muhammad Nor Che Isa
    ;
    Muhammad Syukri Jaafar
    ;
    Zahari Awang Ahmad
    ;
    Amir Nazren Abdul Rahim
    Performance of a dual-band coplanar patch antenna with bending and wet condition is described in this paper. The antenna structure is made from common clothing fabrics and operates at the 2.45 and 5 GHz wireless bands. The antenna's radiating element is using Nora Dell serves as a ground plane and Felt as the substrate. The final part carries out the simulation on forward, backward bending, and also the wet condition in CST software. The comparison of two bending conditions is discussed in this report.
  • Publication
    A review of Machine Learning approach for ground penetrating radar applications
    ( 2021)
    Cheah Chow Wei
    ;
    Mohd Nazri A. Karim
    ;
    Lee Yeng Seng
    ;
    Mimi Diana Ghazali
    Machine learning (ML) is a branch of artificial intelligent in which algorithms learn relationships in data. ML can be applied in predictive sense or to investigate internal relationships of dataset. The ability to give promising results bring ML been applied in various application such as imaging, signals processing, data mining, and many more. In this paper, the ML approach for Ground Penetrating Radar application is reviewed. Nowadays, Ground Penetrating Radar have some issues of accuracy of localization and the image processing due to the noise and unwanted signal from the underground. Therefore, some of the smart learning technique is proposed especially to remove the clutter signals. A comparison of ML technique such as linear regression, logistic regression, KNN, support vector machine and etc for clutter issues is presented in this paper. The most suitable technique for in GPR applications in order to solve the clutter issues is proposed