A Generalized Few-Shot Transfer Learning Architecture for
In this work, we propose a generalized few-shot transfer learning architecture based on a Semi-Supervised Self-Normalizing Neural Network (SS-NN) that leverages internal EDFA
anilkrp/EDFA_Gain_Modelling_Dissertation
The research focuses on developing a DNN model to accurately predict the gain characteristics of Erbium-Doped Fiber Amplifiers (EDFAs) across 95 channels in Wavelength-Division Multiplexing
Open EDFA gain spectrum dataset and its applications in data
Using the dataset and DNN-based EDFA model described above, we first investigate for a given (pre-trained) source model, how much new data is needed from a target EDFA.
EDF submits bid to build new unit at Czech nuclear power plant
A long term partnership approach between the French and Czech nuclear industries supported by a tailor-made Czech localisation process enhancing economic value for the Czech Republic.
Transfer Learning for EDFA Gain Modeling: A Semi-Supervised
In this paper, we implement and study a novel semi-supervised, self-normalizing NN approach (hereafter referred to as the SS-NN model) that characterizes the wavelength-dependent gain of an EDFA
Transfer Learning-based ROADM EDFA Wavelength Dependent
We showed that the EDFA gain models can be transferred between different EDFAs of the same type, different gain settings on the same EDFA, and different EDFA types.
Machine learning-based EDFA Gain Model Generalizable to
In this work, we propose a simple NN-based EDFA gain model which not only accurately pre-dicts the performance of the specific physical de-vice it is trained on, but it also generalizes well to different
Modeling EDFA Gain: Approaches and Challenges
In this paper, we firstly summarize the underlying principles and structures of EDFA, and introduce the gain performance and challenges in modeling. Then, we review the EDFA gain modeling methods.
Open EDFA gain spectrum dataset and its applications in data-driven
To improve network resource utilization, machine learning (ML) is used to accurately model optical amplifiers such as erbium-doped fiber amplifiers (EDFAs), which impact end-to-end system
Optical Power Meters & Sources
High-precision power meters (Ge/InGaAs) and stabilized light sources for insertion loss and return loss testing.
OTDR & Fiber Characterization
Full-featured OTDR, fiber OTDR testers, and modular OTDR test modules for network deployment and troubleshooting.
OSA & Eye Diagram Analyzer
High-resolution OSA for DWDM and eye diagram testers for signal integrity validation.
BERT & Endface Inspection
BERT up to 800G, fiber endface inspection probes, and extinction ratio meters for comprehensive testing.