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Record W2768352563 · doi:10.1364/oe.25.029945

Optical camera with liquid crystal autofocus lens

2017· article· en· W2768352563 on OpenAlexafffund
Tigran Galstian, Oleksandr Sova, K. Asatryan, Vladimir Presniakov, Armen Zohrabyan, Mads Vejlgaard Evensen

Bibliographic record

VenueOptics Express · 2017
Typearticle
Languageen
FieldEngineering
TopicElectrowetting and Microfluidic Technologies
Canadian institutionsUniversité Laval
FundersCanada Research Chairs
KeywordsAutofocusLens (geology)OpticsOptical transfer functionCamera phoneOptical powerSIGNAL (programming language)Image qualityFocal lengthMaterials scienceVoltageComputer scienceOptoelectronicsFocus (optics)PhysicsComputer visionElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

A mobile phone camera with an innovative electrically tunable liquid crystal lens (TLCL) concept is demonstrated. We first report the comparative theoretical and experimental analyses of the performance of a traditional "modal control" TLCL versus a TLCL using a floating (unpowered) transparent electrode (FTE). It is shown that the appropriate choice of voltage and frequency values of the driving electric signal may improve significantly (almost twice) the optical quality of the lens using the FTE. Exceptionally low spherical aberrations of the lens (< λ/10 for up to 10 diopters of optical power) and high modulation transfer functions of a mobile phone camera (using those lenses for autofocus function) are demonstrated in a very simple operation mode (frequency tuning of the lens' optical power at a fixed driving voltage). The capacity of the camera to perform high quality long distance photography and near distance bar code recognition within a short autofocus convergence time are demonstrated.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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 science0.0010.000
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 head0.010
GPT teacher head0.213
Teacher spread0.204 · 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

Citations85
Published2017
Admission routes2
Has abstractyes

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