Bibliographic record
Abstract
An implementation of the compressive sensing (CS) method with a CMOS image sensor is presented. The conventional three-transistor active pixel sensor (APS) structure and switched capacitor circuits are exploited to develop an analog implementation of the CS encoding in a CMOS sensor. With the analog implementation, the sensing and encoding are performed in the same time interval and making a real-time encoding process to optimize the frame rate of the imager. A block readout strategy is proposed to capture the required CS measurements for different blocks of the image, rather than the common column-row readout method. All measurement circuits are placed outside the array by this readout strategy, and the imager becomes scalable for larger array sizes. Because there is no extra in-pixel element for the CS measurement process, the fill factor of the imager is the same as its corresponding APS imager without CS. The proposed structure is designed and fabricated in 0.13-\(\mu \)m CMOS technology for a\(2\times 2\)array. The experimental results confirm the validity of the design in making monotonic and appropriate CS measurements. The functionality of the block readout method and the scalability of the imager are confirmed by fabrication of a\(4\times 4\)block and a\(16\times 16\)array.
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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 | 0.000 | 0.001 |
| 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".