MétaCan
Menu
Back to cohort
Record W2003152152 · doi:10.1109/emrtw.2005.195691

CMOS wavelet compression imager architecture

2005· article· en· W2003152152 on OpenAlexaff
Ashkan Olyaei, Roman Genov

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWaveletHaar waveletComputer scienceQuantization (signal processing)CMOSPixelOversamplingDiscrete wavelet transformWavelet transformDelta-sigma modulationElectronic engineeringComputer visionArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

The CMOS imager architecture implements /spl Delta//spl Sigma/-modulated Haar wavelet image compression on the focal plane in real time. The active pixel array is integrated with a bank of column-parallel first-order incremental over-sampling analog-to-digital converters (ADCs). Each ADC performs column-wise distributed focal-plane sampling and concurrent signed weighted average quantization, realizing a one-dimensional spatial Haar wavelet transform. A digital delay and adder loop performs spatial accumulation over multiple adjacent ADC outputs. This amounts to computing a two-dimensional Haar wavelet transform, with no overhead in time and negligent overhead in area compared to a baseline digital imager architecture. The architecture is experimentally validated on a 0.35 micron CMOS prototype containing a bank of first-order incremental oversampling ADCs computing Haar wavelet transform on an emulated pixel array output. The architecture yields simulated computational throughput of 1.4 GMACS with SVGA imager resolution at 30 frames per second.

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: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

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)0.0070.003

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 head0.003
GPT teacher head0.179
Teacher spread0.176 · 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

Citations9
Published2005
Admission routes1
Has abstractyes

Explore more

Same topicCCD and CMOS Imaging SensorsFrench-language works237,207