S
Sulaiman Wadi Harun
Researcher at University of Malaya
Publications - 1170
Citations - 13374
Sulaiman Wadi Harun is an academic researcher from University of Malaya. The author has contributed to research in topics: Fiber laser & Saturable absorption. The author has an hindex of 44, co-authored 1107 publications receiving 10844 citations. Previous affiliations of Sulaiman Wadi Harun include Airlangga University & Peninsular Malaysia.
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Journal ArticleDOI
Modeling the Concentric Fiber Optic Bundle Displacement Sensor Using a Quasi-Gaussian Beam Approach
TL;DR: In this article, a reflective-based intensity modulation mechanism for displacement measurements, employing a concentric fiber optic bundle acting as the fiber optic probe, with a planar reflector is presented.
Observation of Raman Gain in Reduced Length of Bismuth Erbium Doped Fiber
TL;DR: In this article, Raman amplification of a 49 cm Bismuth oxide (Bi2O3) as a nonlinear gain medium based erbium doped fiber amplifier (EDFA) is reported in new and compact design in near infrared spectral regions.
Journal ArticleDOI
Bismuth-doped fiber Q-switcher in erbium-doped fiber laser cavity
Pei Zhang,Pei Zhang,Kaharuddin Dimyati,Mustafa Mohammed Najm,Bilal Nizamani,M. C. Paul,Shyamal Das,Anirban Dhar,Mrinmay Pal,Moh. Yasin,Sulaiman Wadi Harun +10 more
Proceedings ArticleDOI
Wideband and flat-gain amplifier using high concentration Erbium doped fibers in series double-pass configuration
Belal Ahmed Hamida,Anas Abdul Latiff,X.S. Cheng,M. A. Ismail,W. Naji,Sheroz Khan,Wajdi Fawzi Mohammed Al-Khateeb,Hijaz Ahmad,Sulaiman Wadi Harun +8 more
TL;DR: In this paper, a wide-band and flat gain erbium-doped fiber amplifier (EDFA) was demonstrated using a gain media of high concentration Silica-based EDF.
Journal ArticleDOI
Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor.
Husna Abdul Rahman,Husna Abdul Rahman,Sulaiman Wadi Harun,Hamzah Arof,Ninik Irawati,Ismail Musirin,Fatimah Ibrahim,Harith Ahmad +7 more
TL;DR: The high-classification accuracy significantly demonstrates the suitability of the proposed features and classification using SLP networks for classifying the reflected signals from teeth surfaces, enabling the sensor to accurately measure small diameters of tooth cavity of up to 0.6 mm.