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Record W2760739162 · doi:10.1109/iscas.2017.8050545

Always-on CMOS image sensor pixel design for pixel-wise binary coded exposure

2017· article· en· W2760739162 on OpenAlexafffund
Yi Luo, Shahriar Mirabbasi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsPixelImage sensorCMOSComputer scienceBinary numberEncoding (memory)ChipArtificial intelligenceComputer hardwareCMOS sensorElectronic engineeringComputer visionEngineeringTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

Pixel-wise coded exposure, as an emerging field of research in computational imaging, is widely implemented in applications such as high-speed imaging, compressive sensing, and focal-stack imaging. In this paper, a complementary metal-oxide-semiconductor (CMOS) pixel design for binary coded exposure is presented. Based on an always-on charge distribution structure, the proposed pixel achieves programmable charge selection. A prototype image sensor chip, fabricated in a 0.13-μm CMOS process demonstrates pixel's capability of binary exposure encoding in 60 fps while operating from a 3.3 V supply.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
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 integrity0.0000.000
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 head0.029
GPT teacher head0.252
Teacher spread0.223 · 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

Citations4
Published2017
Admission routes2
Has abstractyes

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