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Record W2101234418 · doi:10.1109/ted.2005.856192

Reset and Partition Noise in Active Pixel Image Sensors

2005· article· en· W2101234418 on OpenAlex
Jhih‐Siang Lai, Arokia Nathan

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

VenueIEEE Transactions on Electron Devices · 2005
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsReset (finance)TransistorNoise (video)Partition (number theory)PixelElectronic engineeringComputer scienceNoise measurementFlicker noiseNoise generatorNoise floorElectrical engineeringEngineeringArtificial intelligenceMathematicsCMOSNoise figureNoise reductionVoltageImage (mathematics)

Abstract

fetched live from OpenAlex

Partition noise appears in conjunction with reset noise at the integration node of active pixel sensor architectures. This brief presents the modeling and measurement of partition noise based on an improved technique for charge profile estimation in the transistor channel at any given time instant. Transistor turn off transients are integrated into the model by taking into account both drift and diffusion components of current. Using the predictions of charge profile, partition noise generated during the transistor reset operation is accurately determined and verified with measured data.

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 score0.381
Threshold uncertainty score0.662

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 head0.227
Teacher spread0.221 · 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