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Record W2011470011 · doi:10.1116/1.2192526

High dynamic range active pixel sensor arrays for digital x-ray imaging using a-Si:H

2006· article· en· W2011470011 on OpenAlexafffund
Jackson Lai, Arokia Nathan, John Rowlands

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2006
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDynamic rangeWide dynamic rangeActive matrixPixelDetectorImage sensorSIGNAL (programming language)High dynamic rangeLinearityPhotocurrentSensitivity (control systems)Materials sciencePhysicsComputer scienceOpticsOptoelectronicsElectronic engineeringEngineeringNanotechnologyThin-film transistor

Abstract

fetched live from OpenAlex

Hydrogenated amorphous silicon (a-Si:H) active matrix flat panel imagers have gained considerable significance in large area digital imaging applications, in view of their large area readout capability. Current interests lie in a multifaceted a-Si:H array which is compatible with multiple x-ray imaging modalities. This concept entails a single detector system with sufficient dynamic range and variable signal gain. This article reports on an active pixel sensor (APS) array with high dynamic range and programable gain for multimodality x-ray imaging. Initial results have demonstrated sensitivity from subpicoampere to nanoampere photocurrent, which proves amenable for both low-dosage dynamic imaging and high input static imaging. In addition, the programable system signal gain alleviates problems such as output saturation and ensures signal readout linearity to further improve the exploitable dynamic range. Together with external amplification, this APS circuit delivers the performance needed in terms of signal gain, dynamic range, and readout rate entailed by fluoroscopic and radiographic imaging 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.

How this classification was reachedexpand

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 score0.862
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
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.005
GPT teacher head0.217
Teacher spread0.212 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2006
Admission routes2
Has abstractyes

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