Paper
13 October 2005 Optical telecommunications system and signal analysis using data-entropy and multiple-centroid data-scatter
Author Affiliations +
Abstract
The data-entropy quality-budget developed by the authors is used as an alternative to the conventional power budget. The traditional power budget approach is not capable of providing a full analysis of a system with different noise types and specifically providing a measure of signal quality. The quality-budget addressed this issue by applying its dimensionless 'bit measure' to integrate the analysis of all types of losses. A data-entropy visualisation is produced for each set of points in a reference and test signal. This data-entropy signal is a measure of signal disorder and reflects the power loss and types of signal degradation experienced by the test signal. To analyse the differences between two signals an algorithm known as phase-coherent data-scatter (PCDS) is used to assess levels of attenuation, dispersion, jitter, etc. Practical analysis of telecommunications signals using the new multiple-centroid (MC) PCDS is presented here for the first time. MC-PCDS is then used to analyse differences between sets of data-entropy signals and digital signals. The theory behind MC data-scatter is discussed and its advantages for the quantification of signal degradations are assessed. Finally, a brief consideration is given to the use of pattern recognition algorithms to measure optical signal degrading factors.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
J. Egan, N. D. McMillan, D. Denieffe, S. Riedel, G. Doyle, and G. Farrell "Optical telecommunications system and signal analysis using data-entropy and multiple-centroid data-scatter", Proc. SPIE 5970, Photonic Applications in Devices and Communication Systems, 59701Q (13 October 2005); https://doi.org/10.1117/12.628588
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KEYWORDS
Signal attenuation

Telecommunications

Visualization

Pattern recognition

Detection and tracking algorithms

Image segmentation

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