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Record W2544399388 · doi:10.1109/icm.2008.5393796

Current mode active pixel sensor architectures for large area digital imaging

2008· article· en· W2544399388 on OpenAlex
K. S. Karim, Farhad Taghibakhsh, Mohammad Hadi Izadi, N. Safavian, Dongliang Wu

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 institutionsUniversity of Waterloo
Fundersnot available
KeywordsAmplifierNoise (video)Electronic circuitImage sensorPixelTransistorComputer scienceDetectorElectronic engineeringFixed-pattern noiseElectrical engineeringCMOSVoltageEngineeringArtificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

The most widely used architecture in large area flat panel imagers is the amorphous silicon passive pixel sensor (PPS), which consists of a detector and a readout switch. While the PPS has the advantage of being compact, reading small PPS output signals requires external column charge amplifiers that produce additional noise and reduce the minimum readable sensor input signal. This work compares the SNR, metastability, area, and off-panel complexity of amorphous silicon active pixel sensor (APS) readout circuits based on three and two transistor current mode and current programmed designs that perform on-pixel amplification of noise-vulnerable sensor input signals to minimize the effect of external readout noise sources associated with ¿off-chip¿ charge amplifiers. The APS circuits presented are poised to replace PPS circuits for fast readout, noise-sensitive, large area, digital medical imaging applications such as dual mode radiography/fluoroscopy and mammography tomosynthesis.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.656

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 head0.012
GPT teacher head0.237
Teacher spread0.225 · 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

Citations8
Published2008
Admission routes1
Has abstractyes

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