Bibliographic record
Abstract
The book focuses on the innovative application of Visible Light Communication (VLC) within the realm of vehicular networks to enhance autonomous driving and intelligent transportation systems (ITS). The purpose is to explore how VLC can facilitate high-speed wireless data transmission using the visible spectrum of light, which promises a huge bandwidth, low confinement, and usage of existing LED head/taillights. Advance topics such as MIMO, Optical OFDM, Precoding\Equalization and adaptive noise cancellation are studied in detail in the realms of VLC. Furthermore, it examines the potential of VLC to complement existing communication Cellular V2X standards in scenarios demanding low latency and high reliability. The integration of Artificial Intelligence (AI) and Machine Learning (ML) in VLC systems are also explored. Part of IOP Series in Emerging Technologies in Optics and Photonics. Key features • Includes detailed modular multi-reflection channel model; that can be used with the required complexity. • Includes Artificial Intelligent algorithms to alleviate the effect of bright sun light, a major concern in outdoors. • Investigates some topics for first time such as shadowing effect, coherence time and denoising schemes. • Advance topics such as MIMO, Optical OFDM, Precoding and Equalization are studied in detail in the realms of VLC. • Chapter organization provides a systematic overview of the subject, accessible to students of various levels including undergraduate and graduate students and practicing engineers.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.231 | 0.189 |
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".