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Record W2003152152 · doi:10.1109/emrtw.2005.195691

CMOS wavelet compression imager architecture

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

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

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score0.687

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)0.0010.000

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 head0.003
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

Quick stats

Citations9
Published2005
Admission routes1
Has abstractyes

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