WDM optical network: Efficient techniques for fault-tolerant logic topology design
Bibliographic record
Abstract
The rapid increase of bandwidth intensive applications has created an unprecedented demand for bandwidth on the Internet. With recent advances in optical technologies, especially the development of wavelength division multiplexing (WDM) techniques, the amount of raw bandwidth available on the fibre links has increased by several orders of magnitude. Due to the large volume of traffic these optical networks carry, there is one very important issue---design of robust networks that can survive faults. Two common mechanisms to protect against the network failure: one is protection and another is restoration. My research focuses on studying the efficient techniques for fault-tolerant logical topology design for the WDM optical network. In my research, the goal is to determine a topology that accommodates the entire traffic flow and provides protection against any single fiber failure. I solve the problem by formulating the logical topology design problem as a MILP optimization problem, which generates the optimum logical topology and the optimum traffic routing scheme. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .S54. Source: Masters Abstracts International, Volume: 43-01, page: 0244. Adviser: Arunita Jaekel. Thesis (M.Sc.)--University of Windsor (Canada), 2004.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".