Paper
7 August 2017 Optical transmission testing based on asynchronous sampling techniques: images analysis containing chromatic dispersion using convolutional neural network
T. Mrozek, K. Perlicki, T. Tajmajer, P. Wasilewski
Author Affiliations +
Proceedings Volume 10445, Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2017; 104451B (2017) https://doi.org/10.1117/12.2281111
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2017, 2017, Wilga, Poland
Abstract
The article presents an image analysis method, obtained from an asynchronous delay tap sampling (ADTS) technique, which is used for simultaneous monitoring of various impairments occurring in the physical layer of the optical network. The ADTS method enables the visualization of the optical signal in the form of characteristics (so called phase portraits) that change their shape under the influence of impairments such as chromatic dispersion, polarization mode dispersion and ASE noise. Using this method, a simulation model was built with OptSim 4.0. After the simulation study, data were obtained in the form of images that were further analyzed using the convolutional neural network algorithm. The main goal of the study was to train a convolutional neural network to recognize the selected impairment (distortion); then to test its accuracy and estimate the impairment for the selected set of test images. The input data consisted of processed binary images in the form of two-dimensional matrices, with the position of the pixel. This article focuses only on the analysis of images containing chromatic dispersion.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
T. Mrozek, K. Perlicki, T. Tajmajer, and P. Wasilewski "Optical transmission testing based on asynchronous sampling techniques: images analysis containing chromatic dispersion using convolutional neural network", Proc. SPIE 10445, Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2017, 104451B (7 August 2017); https://doi.org/10.1117/12.2281111
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KEYWORDS
Dispersion

Convolutional neural networks

Convolution

Image resolution

Picosecond phenomena

Error analysis

Image analysis

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