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Record W2096091194 · doi:10.1109/jssc.2009.2016693

Focal-Plane Algorithmically-Multiplying CMOS Computational Image Sensor

2009· article· en· W2096091194 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 Journal of Solid-State Circuits · 2009
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPixelCMOSQuantization (signal processing)Computer scienceBit planeKernel (algebra)Binary numberArtificial intelligenceComputer visionMathematicsElectronic engineeringEngineeringArithmetic

Abstract

fetched live from OpenAlex

The CMOS image sensor computes two-dimensional convolution of video frames with a programmable digital kernel of up to 8 times 8 pixels in parallel directly on the focal plane. Three operations, a temporal difference, a multiplication and an accumulation are performed for each pixel readout. A dual-memory pixel stores two video frames. Selective pixel output sampling controlled by binary kernel coefficients implements binary-analog multiplication. Cross-pixel column-parallel bit-level accumulation and frame differencing are implemented by switched-capacitor integrators. Binary-weighted summation and concurrent quantization is performed by a bank of column-parallel multiplying analog-to-digital converters (MADCs). A simple digital adder performs row-wise accumulation during ADC readout. A 128 times 128 active pixel array integrated with a bank of 128 MADCs was fabricated in a 0.35 mum standard CMOS technology. The 4.4 mm times 2.9 mm prototype is experimentally validated in discrete wavelet transform (DWT) video compression and frame differencing.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
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 head0.011
GPT teacher head0.247
Teacher spread0.236 · 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