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 <a href="https://iopscience.iop.org/bookListInfo/emerging-technologies-in-optics-and-photonics#series">IOP Series in Emerging Technologies in Optics and Photonics</a>. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.008 |
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; both teacher heads agree on what is shown here.
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".