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A large angle, low voltage, small footprint micromirror for eye tracking and near-eye display applications

2015· article· en· W1608854558 on OpenAlexafffund
Niladri Sarkar, D. Strathearn, G. Lee, M. Olfat, Arash Rohani, Raafat R. Mansour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsUniversity of Waterloo
FundersDefense Advanced Research Projects AgencyNeuroscience Research AustraliaCMC Microsystems
KeywordsFootprintCMOSVoltageMicroelectromechanical systemsTracking (education)OpticsMaterials scienceComputer scienceOptoelectronicsElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper introduces a micromirror device that enables a compact, low-power, high-speed, high resolution eye-tracking system that may be integrated within eyeglasses or heads-up-displays. We report a CMOS-MEMS that achieves a scan range of 65 degrees (optical) in one axis and 25 degrees in another axis. The device operates at CMOS-compatible voltages (0-3.3V) and currents (<;15 mA), and employs isothermal scanning to suppress thermal excursions arising from electrothermal (ET) actuation. The footprint of the device is 40× smaller than commercially available micromirrors, at a mere 750μm × 750μm. Although the eye-tracking system is intended to operate under quasi-static conditions, its 5kHz resonant frequency may also enable QVGA resolution in near eye display applications.

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 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 score0.076
Threshold uncertainty score0.572

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 head0.054
GPT teacher head0.306
Teacher spread0.252 · 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 teacher head, 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

Citations24
Published2015
Admission routes2
Has abstractyes

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