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Record W2096185181 · doi:10.1109/jsen.2010.2089447

CMOS Active-Pixel Sensor With In-Situ Memory for Ultrahigh-Speed Imaging

2010· article· en· W2096185181 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

VenueIEEE Sensors Journal · 2010
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsMcMaster University
Fundersnot available
KeywordsImage sensorFrame rateComputer sciencePixelComputer hardwareCMOSCMOS sensorFrame (networking)ChipData acquisitionElectronic engineeringArtificial intelligenceEngineeringTelecommunications

Abstract

fetched live from OpenAlex

State-of-the-art image sensor arrays have not been able to operate at frame rates that exceed tens to hundreds of thousands of frames per second. The main bottle neck preventing imaging at higher frame rates is the time required to access the array, convert the image data from analog to digital, and transmit the data off the image sensor chip. The later is considered the most significant source of delay, mainly due to the limited number of input and output ports available on the chip. This work allows for a significant increase in image capture rate by separating the image acquisition phase from the conversion and readout phase. This was done by capturing eight frames at a high capture rate and temporarily storing the multiple frames into analog memory units that are incorporated inside the pixel. The design was implemented in a deep-submicron CMOS 130 nm technology that allows for high-speed operation. This paper discusses the tradeoffs of using in-situ frame storage and gives some recommendations.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.216
Teacher spread0.210 · 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