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Record W2062404935 · doi:10.1049/iet-cds:20060217

High dynamic range 2-TFT amplified pixel sensor architecture for digital mammography tomosynthesis

2007· article· en· W2062404935 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

VenueIET Circuits Devices & Systems · 2007
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPixelDynamic rangeThin-film transistorTomosynthesisComputer scienceAmplifierImage sensorActive matrixTransistorDot pitchMammographyDigital mammographyWide dynamic rangeElectronic engineeringMaterials scienceCMOSOptoelectronicsArtificial intelligenceElectrical engineeringComputer visionEngineeringMedicine

Abstract

fetched live from OpenAlex

On-pixel amplifiers in amorphous silicon (a-Si) technology are an attractive replacement for industry standard on-pixel switch architectures in active matrix flat panel imagers in order to meet the low noise requirements of low-dose digital imaging modalities such as x-ray fluoroscopy and, more recently, 3D mammography tomosynthesis. However, implementing a-Si pixel amplifiers requires high-performance thin film transistors (TFTs) that are relatively large in size. In this research, a novel high dynamic range amplified pixel architecture using only two TFTs is introduced that is capable of amplifying the sensor value with a user controllable gain over a wide input range. Circuit operation and driving circuits required for on-pixel amplifier arrays are investigated, and simulation results are presented that indicate the feasibility of this pixel architecture for high resolution, low noise and x-ray tomosynthesis 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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.007
GPT teacher head0.208
Teacher spread0.200 · 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