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Record W4405280774 · doi:10.1088/978-0-7503-6049-4ch1

Introduction

2024· book-chapter· en· W4405280774 on OpenAlexaff
Xavier Fernando, Hasan Farahneh

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsVisible light communicationWirelessInterference (communication)Computer scienceTelecommunicationsIntelligent transportation systemChannel (broadcasting)Optical wirelessEngineeringSystems engineeringComputer networkElectrical engineeringTransport engineeringLight-emitting diode

Abstract

fetched live from OpenAlex

This chapter discusses optical wireless communication (OWC) and its application in vehicular environments, particularly focusing on visible light communication (VLC). OWC encompasses technologies that transmit data via light waves, offering a viable alternative to traditional radio frequency (RF) communications, especially in scenarios where RF is congested or subject to interference. VLC utilizes the existing LED lighting in vehicles and infrastructure, enabling high-speed, shortrange data exchange among vehicles (V2V), between vehicles and infrastructure (V2I), and with networks (V2N) or pedestrians (V2P). It highlights benefits such as improved collision avoidance, traffic management, and enhanced positioning precision. This chapter also acknowledges challenges like interference from sunlight, quick channel changes, and the necessity for line-of-sight communication. Overall, it underscores the potential of VLC in enhancing intelligent transportation systems (ITS) and supporting the development of autonomous vehicles through reliable, real-time communication. The exploration of research directions and practical applications of VLC aims to address these challenges while maximizing the benefits of this emerging technology.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.792
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.007

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.

Opus teacher head0.010
GPT teacher head0.197
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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