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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 OpenAlexaff
Farhad Taghibakhsh, K. S. Karim

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2007
Admission routes1
Has abstractyes

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