CMOS Image Sensor Architecture for Primal-Dual Coding
Bibliographic record
Abstract
A CMOS image sensor architecture for primal-dual coding (PDC), the developed image sensor and the sensing side of the system, as well as preliminary sensor test results are presented in this paper. The architecture proposed in this work uses pixels with the embedded 2-bit latches which are responsible for the pre-loading and storing of the exposure codes. The subsequent exposure code (mask) can therefore be loaded while the current mask is being used for exposure, resulting in a pipelined coding operation which does not interfere with the pixel exposure time. The mask loading is done serially via a vertical metal line (one line per-column), making both the imager architecture and the pixel array scalable towards high pixel resolutions. The sensor is designed using a 0.35µm image sensor optimized CMOS process resulting in the total pixel pitch of 25µm. The pixel includes a photo-gate based photodetector, two 1-bit latches, required logic gates, two charge collection buckets (floating diffusions) and corresponding symmetric readout with two source-followers (one for each bucket), resulting in a pixel fill-factor of 20.5%. Every pixel column features a programmable gain amplifier whose outputs are time-multiplexed over 3 analog output pads. Analog-to-digital conversion is performed off-chip by 3 16-bit ADCs. The 60x80 pixel imager consumes 7mW of power while operating at 25 fps. The sensor measurement results show that the loading of the complete PDC mask for the whole array can be performed in 30µs, resulting in a large number of masks that can be applied during a single exposure time, therefore creating a very promising platform for an effective and optimal use of PDC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".