Optimization of 5-Locations LDs for VLC Systems Illumination
Bibliographic record
Abstract
Visible light communication (VLC) is one of the fastest wireless communication systems compatible with 5G and beyond.Therefore, it was necessary to employ rapid modulation systems compatible with intensity modulation / direct detection (IM / DD), such as Flip-FBMC modulation technology.The distribution of illumination units across the entirety of the room poses the greatest difficulty for the VLC distribution system.Previous models suffered from dark spots or blind spots in the center of the room, which is the user's mobility area, as well as high power consumption, which is one of the most influential factors influencing the system.This is the first instance in which the illumination units are distributed in new locations by five lights installed in the ceiling in order to eradicate dark spots by using a laser diode (LD) instead of an LED due to its high-intensity illumination.The optimal semi-angle and field of view were calculated to be 43 o and 45 o , respectively, in order to obtain the best results in terms of the received optical power and the preferred performance of the SNR distribution in comparison to the previous models, as well as to improve the power consumption of the illumination units.using the optimal values of the semi-angle at 43° and FOV at 45° , the current findings indicate that Model 3 also attained the highest optical power received and the best SNR distribution performance compared to the previous models.In this way, a complete illuminate distribution is obtained for the room.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".