Abstract
Techniques from Bayesian machine learning and digital coherent detection are applied to perform frequency noise characterization. Significant advantages of the presented techniques are high-sensitivity and direct access to the uncertainty of the frequency noise measurement.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 2018 Conference on Lasers and Electro-Optics (CLEO) |
| Number of pages | 2 |
| Publisher | Optical Society of America |
| Publication date | 2018 |
| Pages | 1-2 |
| ISBN (Print) | 9781943580422 |
| DOIs | |
| Publication status | Published - 2018 |
| Event | 2018 Conference on Lasers and Electro-Optics (CLEO) - San Jose Convention Center, San Jose, United States Duration: 13 May 2018 → 18 May 2018 |
Conference
| Conference | 2018 Conference on Lasers and Electro-Optics (CLEO) |
|---|---|
| Location | San Jose Convention Center |
| Country/Territory | United States |
| City | San Jose |
| Period | 13/05/2018 → 18/05/2018 |
Bibliographical note
From the session: Machine Learning for Communication (STh1C)Keywords
- Laser noise
- Laser modes
- Measurement by laser beam
- Machine learning
- Bayes methods
- Photonics
- Optical transmitters
Fingerprint
Dive into the research topics of 'Learning of Laser Dynamics using Bayesian Inference'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver