Nurul Farah Adilla Zaidi, Muhammad Yusof Mohd Noor, Nur Najahatul Huda Saris, Mohd Rashidi Salim, Sumiaty Ambran, Azizul Azizan, Raja Kamarulzaman Raja Ibrahim, Fauzan Ahmad, Nurul Ashikin Daud, Norazida Ali, Norizan Mohamed Nawawi, Ian Yulianti, Gang-Ding Peng
This research presents an artificial intelligence (AI)-driven machine learning (ML) approach for accurately measuring refractive index (RI) values across both lower and higher regimes than the fiber material's RI, using a simple single multimode interference (MMI) fiber sensor. The sensor configuration consists of a no-core fiber (NCF) segment between two single-mode fiber (SMF) sections. A Bilayer Neural Network (BNN) regression model is employed to predict both low refractive index (LRI) and high refractive index (HRI) regimes, achieving a broad dynamic measurement range from 1.3000 RIU to 1.3900 RIU for LRI regime and from 1.4600 RIU to 1.5500 RIU for HRI regime. The model demonstrates 99.7% accuracy and a low root mean square error (RMSE) of 0.0044, ensuring that predicted RI values closely match actual measurements without any RI ambiguity. Furthermore, the all-silica NCF structure is inherently resistant to temperature fluctuations, enabling its deployment in environments with varying temperatures without requiring additional temperature compensation mechanisms. © 2024
Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor, Skudai, 81310, Malaysia; Malaysian-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur, Kuala Lumpur, 54100, Malaysia; Faculty of Artificial Intelligence, Universiti Teknologi Malaysia, Kuala Lumpur, Kuala Lumpur, 54100, Malaysia; Department of Physics, Faculty of Science, Universiti Teknologi Malaysia, Johor, Skudai, 81310, Malaysia; Laser Centre, Ibnu Sina Institute for Scientific and Industrial Research (ISI-SIR), Universiti Teknologi Malaysia, Johor, Johor Bahru, 81310, Malaysia; Department of Electrical Engineering, Politeknik Mersing, Johor, Mersing, 86800, Malaysia; Faculty of Electronic Engineering & Technology (FKTEN), Universiti Malaysia Perlis, Pauh Putra, Perlis, Arau, 02600, Malaysia; Physics Department, Universitas Negeri Semarang, Sekaran, Gunungpati, Central Java, Semarang, Indonesia; School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, Australia
