MétaCan
Menu
Back to cohort
Record W2124747642 · doi:10.1109/jsen.2010.2058846

A Novel, High-Dynamic-Range, High-Speed, and High-Sensitivity CMOS Imager Using Time-Domain Single-Photon Counting and Avalanche Photodiodes

2010· article· en· W2124747642 on OpenAlexaff
Munir M. El‐Desouki, Darek Palubiak, M. Jamal Deen, Qiyin Fang, Ognian Marinov

Bibliographic record

VenueIEEE Sensors Journal · 2010
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAvalanche photodiodePhoton countingDynamic rangeSingle-photon avalanche diodeDetectorHigh dynamic rangeCMOSPhotonWide dynamic rangeSensitivity (control systems)PhotodiodeOptoelectronicsOpticsImage sensorPhysicsElectronic engineeringEngineering

Abstract

fetched live from OpenAlex

Avalanche photodiodes used in Geiger mode as single-photon counters have become very attractive imaging tools. High-speed single-photon imaging can be used in very low-light-level applications such as surveillance and security imaging, quantum computing, and biomedical imaging including bioluminescence and fluorescence lifetime imaging. However, a typical avalanche-based single-photon detector cannot offer the high dynamic range that is needed for many biomedical and surveillance applications. In this paper, we show how a single-photon detector can be used in time domain for high-dynamic-range applications. We also discuss novel techniques to implement the time-domain single-photon imager in mainstream deep-submicrometer CMOS technology. The designed imager offers high dynamic range and high sensitivity, while maintaining high-speed operation and low cost.

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)0.001
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.205
Teacher spread0.197 · 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

Citations26
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Sensors JournalSame topicCCD and CMOS Imaging SensorsFrench-language works237,207