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Record W2972195769 · doi:10.1364/ao.58.007181

Preliminary design and characterization of a low-cost and low-power visible light positioning system

2019· article· en· W2972195769 on OpenAlex
Jordan Lui, Anna Maria Vegni, Lorenzo Colace, Alessandro Neri, Carlo Menon

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied Optics · 2019
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsOpticsVisible spectrumMaterials scienceCharacterization (materials science)Stray lightComputer sciencePhysics

Abstract

fetched live from OpenAlex

Designs and results for a low-power and economical fingerprint visible light positioning (VLP) system are discussed in this paper. A system using four white LEDs and one photodiode was deployed. The LEDs are independently controlled and modulated by individual transmitter circuits, removing the requirement for a large, expensive signal generator. The receiver circuit filters and converts the received signal to DC, allowing a simple microcontroller to save the received signal. We propose the creation of a fingerprint database by recording signal data, fitting a two-dimensional Gaussian distribution to the data, and then generating the fingerprint database for all positions in the evaluation system. System positioning accuracy of 13.44+/-0.36 mm was observed, corresponding to a relative error of 2.8% with respect to the system dimensions. This result presents an improvement on positioning accuracy for fingerprint positioning VLP systems, which build their own transmitters.

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.

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 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: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.445

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)0.0000.000

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 head0.006
GPT teacher head0.185
Teacher spread0.180 · 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