Hassrizal Hassan Basri
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Preferred name
Hassrizal Hassan Basri
Official Name
Hassan Basri, Hassrizal
Alternative Name
Basri, H. Hassan
Hassrizal, H. B.
Basri, Hassrizal Hassan
Basri, Hassrizal Hasan
Basri, H. H.
Main Affiliation
Scopus Author ID
55639350700
Researcher ID
ACS-6103-2022

Research Output

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  • Publication
    Analysis of WiFi Spatio-Temporal Data for Organic Fingerprinting-based Indoor Positioning System
    ( 2022-01-01)
    Abdul Halim Ismail
    ;
    Amirah Husna Mohd Hajazi
    ;
    Muhamad Safwan Muhamad Azmi
    ;
    Mohd Sani Mohamad Hashim
    ;
    Mohd Nasir Ayob
    ;
    Hassrizal Hassan Basri
    The mobile robot navigation is the next huge topic after positioning utilizing fingerprinting-based Wireless Positioning System (WPS). Many of recent works does not discuss this topic yet since many open problems in positioning topic are not yet solved, for instance the issues on multi-devices heterogeneity, instability of WiFi signals, granularity problems in grid-based indoor environment and many others. However, we anticipate that both positioning and navigation works must run in parallel so that the succession are guaranteed. This paper describes the analysis of spatio-temporal data of the signal obtained from the WiFi Access Point. Initial results suggest that the difference between transmitter heights have an effect on the spatio-temporal data while the handover of maximum signal strengths is inherent when three WiFi APs are used.
  • Publication
    Integrating Vision System to a Pick and Place Cartesian Robot
    ( 2021-12-01)
    Tan K.S.
    ;
    Mohd Nasir Ayob
    ;
    Hassrizal Hassan Basri
    ;
    Ismail A.H.
    ;
    Azmi M.S.M.
    ;
    Hashim M.S.M.
    ;
    Othman S.M.
    ;
    Shahriman Abu Bakar
    ;
    Low Y.H.
    Vision aided pick and place cartesian robot is a combination of machine vision system and robotic system. They communicate with each other simultaneously to perform object sorting. In this project, machine vision algorithm for object sorting to solve the problem in failure sorting due to imperfection of images edges and different types of colours is proposed. The image is acquired by a camera and followed by image calibration. Pre-processing of image is performed through these methods, which are HSI colour space transformation, Gaussian filter for image filtering, Otsu's method for image binarization, and Canny edge detection. LabVIEW edge-based geometric matching is selected for template matching. After the vision application analysed the image, electrical signal will send to robotic arm for object sorting if the acquired image is matched with template image. The proposed machine vision algorithm has yielded an accurate template matching score from 800 to 1000 under different disturbances and conditions. This machine vision algorithm provides more customizable parameters for each methods yet improves the accuracy of template matching.