Fathinul Syahir Ahmad Sa'ad
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Preferred name
Fathinul Syahir Ahmad Sa'ad
Official Name
Fathinul Syahir, Ahmad Sa'ad
Alternative Name
Ahmad Saad, Fathinul Syahir
Sa’ad, Fathinul Syahir Ahmad
Main Affiliation
Scopus Author ID
57205307496
Researcher ID
R-5360-2019

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  • Publication
    Cloud-based System for University Laboratories Air Monitoring
    ( 2020-09-21)
    Abu Hassan Abdullah
    ;
    Sukhairi Sudin
    ;
    Mustafa M.H.
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Khairul Azwan Ismail
    ;
    Muhammad Aizat Abu Bakar
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    Abdul Ghapar Ahmad
    ;
    Zahari Awang Ahmad
    ;
    Sara Yasina Yusuf
    Indoor air such as house, shopping complex, hospital, university, office and hotel should be monitor for human safety and wellbeing. These closed areas are prone to harmful air pollutants i.e. allergens, smoke, mold, particles radon and hazardous gas. Laboratories in university are special room in which workers (student, technician, teaching/research assistants, researcher and lecturer) conduct their works and experiment. The activities and the environment will generate specific air pollutant which concentration depending to their parameters. Anyone in the environment that exposure to these pollutants may affect safety and health issue. This paper proposes a study of development of a cloud-based electronic nose system for university laboratories air monitoring. The system consists of DSP33-based electronic nose (e-nose) as nodes which measure main indoor air pollutant along with two thermal comfort variables, temperature and relative humidity. The e-noses are placed at five different laboratories for acquiring data in real time. The data will be sent to a web server and the cloud-based system will process, analyse using Neuro-Fuzzy classifier and display on a website in real time. The system will monitor the laboratories air pollutants and thermal comfort by predict the pollutant concentration and dispersion in the area i.e. Air Pollution Index (API). In case of air hazard safety (e.g., gas spills detection and pollution monitoring), the system will alert the security by activate an alarm and through e-mail. The website will display the API of the area in real-time. Results show that the system performance is good and can be used to monitor the air pollutant in the university laboratories.
      51  2
  • Publication
    A deep neural networks-based image reconstruction algorithm for a reduced sensor model in large-scale tomography system
    (Elsevier Ltd, 2022-12-01)
    Lee C.C.
    ;
    Mohd Hafiz Fazalul Rahiman
    ;
    Leow P.L.
    ;
    Rahim R.A.
    ;
    Fathinul Syahir Ahmad Sa'ad
    Image reconstruction for soft-field tomography is a highly nonlinear and ill-posed inverse problem. Owing to the highly complicated nature of soft-field, the reconstructed images are always poor in quality. One of the factors that affect image quality is the number of sensors in a tomography system. It is commonly assumed that increasing the number of sensors in a tomography system will improve the ill-posed condition in image reconstruction and hence improve image quality. However, as the number of sensors increases, challenges such as more complicated and expensive hardware, slower data acquisition rates, longer image reconstruction times, and larger sensitivity matrices will arise, resulting in a greater ill-posed condition. Since deep learning (DL) is capable of expressing complex nonlinear functions, the majority of research efforts have been directed toward developing a robust DL-based inverse solver for image reconstruction. However, no study has been conducted to solve the inverse problem and improve the quality of the reconstructed image using a reduced sensor model for a large-scale tomography system. This paper proposed an image reconstruction algorithm based on Deep Neural Networks (DNN) to investigate its feasibility in solving the ill-posed inverse problem caused by the reduced sensor model for a large-scale tomography system. The proposed DNN model is based on a supervised, feed-forward, fully connected, backpropagation network. It comprises an input layer, three hidden layers and an output layer. Also, it was trained using large data samples obtained from COMSOL simulation. The relationship between the scattered electromagnetic field measurement and the corresponding true electromagnetic field distribution vector is determined. During the image reconstruction process, the untrained scattered electromagnetic field measurement samples are used as inputs to the trained DNN model, and the model output is an estimate of the electromagnetic field distribution. The results show that the proposed DNN can accurately describe the distribution of electromagnetic field and boundary shape of phantom compared to traditional algorithms (LBP, FBP, Noser and Tikhonov), regardless of the size and number of phantoms within the monitoring area. Hence, the proposed DNN is more robust and has a high degree of generalization.
      2  5