Optical orthogonal code construction using rejected delays reuse for increasing SubWavelength-switching capacity
Bibliographic record
Abstract
Using a mathematical proof, the authors establish that in element-by-element greedy algorithms based on extended set representation of optical orthogonal codes (OOCs), smaller delay elements rejected during a construction step can be accepted in later steps. They design a novel algorithm that exploits this property and call it the rejected delays reuse (RDR) greedy algorithm. They show that employing the RDR method leads to code lengths that are shorter than those achieved for OOCs constructed using the classical greedy algorithm for the same code weight and the same number of simultaneous codes constraints. They then define a quantitative measure (factor) for OOCs efficiency based on its ability to expand subwavelength-switching capacity. They call this factor the expansion efficiency factor. They use this factor to show that reducing the code length, for the same code constraints, enhances the capacity of subwavelength optical code switched networks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".