Automatic Identification of Impairments Using Support Vector Machine Pattern Classification on Eye Diagrams
We have demonstrated powerful new techniques for identifying the optical impairments causing the degradation of an optical channel. We use machine learning and pattern classification techniques on eye diagrams to identify the optical impairments. These capabilities can enable the development of low-...
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| Veröffentlicht in: | IEEE photonics technology letters 2006-11, Vol.18 (22), p.2398-2400 |
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| container_title | IEEE photonics technology letters |
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| creator | Skoog, R.A. Banwell, T.C. Gannett, J.W. Habiby, S.F. Pang, M. Rauch, M.E. Toliver, P. |
| description | We have demonstrated powerful new techniques for identifying the optical impairments causing the degradation of an optical channel. We use machine learning and pattern classification techniques on eye diagrams to identify the optical impairments. These capabilities can enable the development of low-cost optical performance monitors having significant diagnostic capabilities |
| doi_str_mv | 10.1109/LPT.2006.886146 |
| format | Article |
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| identifier | ISSN: 1041-1135 |
| ispartof | IEEE photonics technology letters, 2006-11, Vol.18 (22), p.2398-2400 |
| issn | 1041-1135 1941-0174 |
| language | eng |
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| source | IEEE/IET Electronic Library |
| subjects | Channels Classification Degradation Diagnostic systems Impairment Machine learning Monitoring Monitors Optical character recognition software Optical computing optical performance monitoring (OPM) Pattern classification pattern recognition Photonics Signal analysis Signal processing Support vector machine classification Support vector machines |
| title | Automatic Identification of Impairments Using Support Vector Machine Pattern Classification on Eye Diagrams |
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