Prof. Lilin Yi
Project Description and Objectives
Machine learning and neural network have become very popular these years and shown its strength especially in the domain of computer vision and machine translation. Neural network also comes into view of optical communities with more layers, more intrinsic inter-layer relationship. A much more powerful tool, convolutional neural network (CNN), is now widely used in the domain of computer vision and also the key for AlphaGo to defeat various professional Go players. CNN have also shown its powerful capability in optical performance monitoring and modulation formats identification.
This project mainly focus on how machine learning can solve the signal performance distortion in optical fiber transmission, including dispersion, nonlinearities and bandwidth limitation-induced inter symbol interference. The performance of different machine learning structures such as supported-vector machine (SVM), fully-connected neuron network, CNN, recurrent neuron network (RNN) will be compared and evaluated.
Interested students should have basic knowledge on optical communications and programming.
Finish a research report.
Give two research presentations (1. Background review, 2. Technical progress).
Submit a paper to a conference or a journal as a co-author.