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Record W1976141857 · doi:10.1117/12.2044808

CMOS digital pixel sensors: technology and applications

2014· article· en· W1976141857 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.

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2014
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPixelDigitizationImage sensorComputer scienceCMOSNoise (video)Artificial intelligenceDigital imagingComputer hardwareElectronic engineeringComputer visionDigital imageImage processingEngineeringImage (mathematics)

Abstract

fetched live from OpenAlex

CMOS active pixel sensor technology, which is widely used these days for digital imaging, is based on analog pixels. Transition to digital pixel sensors can boost signal-to-noise ratios and enhance image quality, but can increase pixel area to dimensions that are impractical for the high-volume market of consumer electronic devices. There are two main approaches to digital pixel design. The first uses digitization methods that largely rely on photodetector properties and so are unique to imaging. The second is based on adaptation of a classical analog-to-digital converter (ADC) for in-pixel data conversion. Imaging systems for medical, industrial, and security applications are emerging lower-volume markets that can benefit from these in-pixel ADCs. With these applications, larger pixels are typically acceptable, and imaging may be done in invisible spectral bands.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.572
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 science0.0010.000
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 head0.005
GPT teacher head0.200
Teacher spread0.195 · 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