Collaborative VLC/IROW Systems
Bibliographic record
Abstract
Dimming is an important feature of an indoor lighting system where the illumination level can be controlled by the user. Therefore, integrating a visible light communication (VLC) system with an illumination system poses some challenges. One of the main issues is that the light unit should be “ON” all the time to ensure continuous communication. To ensure acceptance and adoption of VLC systems, an important issue should be addressed: how to communicate when the lights are “OFF” or partially dimmed. In this chapter, we propose five new infrared optical wireless (IROW) systems to support VLC systems when the light is totally turned off or significantly dimmed. To take advantage of both VLC and IROW, we introduce and implement the concept of a collaborative VLC/IROW system. In addition, we investigate the impact of partial dimming on the VLC system’s performance, and we propose an adaptive rate technique (ART) to mitigate the impact of light dimming. Moreover, in the case of no dimming, the VLC and IROW systems can collaborate to increase the data rate so it is higher than that in the pure VLC system. We have achieved 10 Gbps in an indoor environment, which is a 2× increase in the data rate compared with a pure VLC system.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".