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Record W2981103351 · doi:10.1364/oe.27.031475

CMOS computational camera with a two-tap coded exposure image sensor for single-shot spatial-temporal compressive sensing

2019· article· en· W2981103351 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueOptics Express · 2019
Typearticle
Languageen
FieldEngineering
TopicSparse and Compressive Sensing Techniques
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsImage sensorPixelComputer scienceCMOS sensorImage resolutionCompressed sensingCMOSArtificial intelligenceChipComputer visionFrame rateTemporal resolutionFrame (networking)OpticsComputer hardwarePhysicsOptoelectronicsTelecommunications

Abstract

fetched live from OpenAlex

We present a CMOS computational camera with on-chip compressive sensing technique. Through per-pixel programmable charge modulation, camera exposure is spatial-temporally encoded by a CMOS image sensor without utilization of superfluous optical modulators. Each sensor pixel incorporates a two-tap charge modulator and exposure code memory cells, and a proof-of-concept image sensor (128×128 pixels) is capable of per-frame spatial-temporal coded exposure in either full resolution or designated region of interest. After reconstruction, high-speed videos at various temporal resolutions are recovered, while the prototype camera operates at 10 fps. Comparing to previous works, this camera design provides a power-efficient solution for compressive sensing related 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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.424
Threshold uncertainty score1.000

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 head0.018
GPT teacher head0.238
Teacher spread0.220 · 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