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Monga, Kaboko Jean-Jacques

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Kaboko Jean-Jacques


Université Laval. Département de génie électrique et de génie informatique



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  • PublicationAccès libre
    Optimization criteria and design of few-mode erbium-doped fibers for cladding-pumped amplifiers
    (Optical Society of America, 2023-02-23) Janvier, Pierre-Olivier; Matte-Breton, Charles; Monga, Kaboko Jean-Jacques; Wang, Lixian; Rusch, Leslie; LaRochelle, Sophie
    We propose a novel optimization method that combines two design criteria to reduce the differential modal gain (DMG) in few-mode cladding-pumped erbium-doped fiber amplifiers (FM-EDFAs). In addition to the standard criterion that considers the mode intensity and dopant profile overlap, we introduce a second criterion that ensures that all doped regions have the same saturation behavior. With these two criteria, we define a figure-of-merit (FOM) that allows the design of MM-EDFAs with low DMG without high computational cost. We illustrate this method with the design of six-mode erbium-doped fibers (EDFs) for amplification over the C-Band targeting designs that are compatible with standard fabrication processes. The fibers have either a step-index or a staircase refractive index profile (RIP), with two ring-shaped erbium-doped regions in the core. With a staircase RIP, a fiber length of 29 m and 20 W of pump power injected in the cladding, our best design leads to a minimum gain of 22.6 dB while maintaining a DMGmax under 0.18 dB. We further show that the FOM optimization achieves a robust design with low DMG over a wide range of variations in signal power, pump power and fiber length.
  • PublicationRestreint
    Machine learning implementation for unambiguous refractive index measurement using a self-referenced fiber refractometer
    (IEEE Sensors Council, 2022-06-21) Martínez-Manuel, Rodolfo; Valentín-Coronado, Luis M.; Esquivel-Hernández, Jonathan; Monga, Kaboko Jean-Jacques; LaRochelle, Sophie
    The implementation of a machine learning algorithm for measuring refractive index of liquid samples using Fresnel reflection at the tip of a fiber is proposed in order to overcome the measurement ambiguity between samples having refractive index values below and above the effective refractive index of the fiber fundamental mode. This is the first time that a machine learning algorithm is implemented in a fiber refractometer. The algorithm, used for pattern classification, is the Support Vector Machine (SVM). The sensing head is formed by two-cascaded cavities that generate an interference pattern that changes each time the fiber is immersed in a different sample. The changes in the interference pattern are classified by the proposed algorithm, which extends the sensing range and eliminates any ambiguity in the obtained RI values. The proposed system is also self-referenced, and therefore it is unaffected by any intensity change of the optical source. A theoretical model and experimental results are presented in detail to demonstrate the effectiveness of the proposed system.