most citedMachine Learning-based Anomaly Detection in Optical Fiber Monitoring

135 citations · 353 across the 18 of their papers we have counts for

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eess.SP20221 cited

Convolutional Neural Networks for Reflective Event Detection and Characterization in Fiber Optical Links Given Noisy OTDR Signals

Khouloud Abdelli, Helmut Griesser, Stephan Pachnicke

Fast and accurate fault detection and localization in fiber optic cables is extremely important to ensure the optical network survivability and reliability. Hence there exists a cr…

eess.SP202210 cited

Lifetime Prediction of 1550 nm DFB Laser using Machine learning Techniques

Khouloud Abdelli, Danish Rafique, Helmut Griesser +1

A novel approach based on an artificial neural network (ANN) for lifetime prediction of 1.55 um InGaAsP MQW-DFB laser diodes is presented. It outperforms the conventional lifetime…

eess.SP202261 cited

Optical Fiber Fault Detection and Localization in a Noisy OTDR Trace Based on Denoising Convolutional Autoencoder and Bidirectional Long Short-Term Memory

Khouloud Abdelli, Helmut Griesser, Carsten Tropschug +1

Optical time-domain reflectometry (OTDR) has been widely used for characterizing fiber optical links and for detecting and locating fiber faults. OTDR traces are prone to be distor…

eess.SP202211 cited

A Hybrid CNN-LSTM Approach for Laser Remaining Useful Life Prediction

Khouloud Abdelli, Helmut Griesser, Stephan Pachnicke

A hybrid prognostic model based on convolutional neural networks (CNN) and long short-term memory (LSTM) is proposed to predict the laser remaining useful life (RUL). The experimen…

eess.SP2022

Federated Learning Approach for Lifetime Prediction of Semiconductor Lasers

Khouloud Abdelli, Helmut Griesser, Stephan Pachnicke

A new privacy-preserving federated learning framework allowing laser manufacturers to collaboratively build a robust ML-based laser lifetime prediction model, is proposed. It achie…

eess.SP202215 cited

Machine Learning based Laser Failure Mode Detection

Khouloud Abdelli, Danish Rafique, Stephan Pachnicke

Laser degradation analysis is a crucial process for the enhancement of laser reliability. Here, we propose a data-driven fault detection approach based on Long Short-Term Memory (L…