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Record W2096739493 · doi:10.1109/dftvs.2005.48

Noise analysis of fault tolerant active pixel sensors

2006· article· en· W2096739493 on OpenAlex
Changsoo Jung, Mohammad Hadi Izadi, Michelle L. La Haye, Glenn H. Chapman, K. S. Karim

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFlicker noiseNoise (video)Shot noisePixelNoise measurementComputer scienceResistorNoise generatorElectrical engineeringElectronic engineeringAmplifierCMOSEngineeringNoise figureNoise reductionArtificial intelligenceVoltage

Abstract

fetched live from OpenAlex

As digital imagers grow in pixel count and area, the ability to correct for pixel defects becomes more important. A fault tolerant active pixel sensor (APS) has previously been designed and fabricated that can correct for stuck high and stuck low defects. Analyses of the pixel noise for a standard APS and a fault tolerant APS are presented that consider reset noise, photocurrent shot noise, dark current shot noise, transistor thermal noise, transistor flicker noise, operational amplifier noise, and feedback resistor thermal noise. Under worst case conditions (no illumination), the noise of the fault tolerant APS is 1.106 /spl times/ more than a standard APS. At a typical illumination level, the fault tolerant APS noise is nearly unchanged to that of a standard APS. Previous research has shown that the fault tolerant APS is more sensitive than a standard APS, thus the overall signal-to-noise ratio of the fault tolerant APS should be greater than the standard APS except under very low light conditions.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.004
GPT teacher head0.193
Teacher spread0.189 · 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

Quick stats

Citations10
Published2006
Admission routes1
Has abstractyes

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