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Record W2117109205 · doi:10.1109/ccece.2012.6334893

Design and validation of a small-scale 5.9 GHz DSRC system for vehicular communication

2012· article· en· W2117109205 on OpenAlexaff
Fahad Kamal, Edmond Lou, Vicky Zhao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDedicated short-range communicationsTransceiverCommunications systemComputer scienceTransmission (telecommunications)WirelessSoftware-defined radioEmbedded systemTransmitterTelecommunicationsElectrical engineeringEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

Despite being recently ratified, the IEEE 802.11p Dedicated Short Range Communication (DSRC) 5.9 GHz standard for vehicle-to-vehicle communication still does not have a large number of supported radios available. This paper outlines the development of an 802.11p communication system with adjustable transmission power. The key components of the system were a Linux-powered embedded system and a 5.9 GHz mini-PCI RF transceiver. The software driver controlling the RF transceiver had been developed to facilitate communication in the DSRC frequency range of 5.85 GHz to 5.92 GHz. A spectrum analyzer was used to validate the central operating frequency of 5.9 GHz. Additionally, an indoor test confirmed communication between two units at a distance of 15 m. In a vehicle-to-vehicle test, packet loss of less than 1% was demonstrated when two vehicles traveled together at 60 km/h at a distance of approximately 150 m. In the vehicle-to-infrastructure test, an average distance of 405 m was measured.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.019
GPT teacher head0.212
Teacher spread0.193 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations6
Published2012
Admission routes1
Has abstractyes

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