High and Low Light CMOS Imager Employing Wide Dynamic Range Expansion and Low Noise Readout
Bibliographic record
Abstract
A high and low light imager (HALLI) developed in a CMOS process is presented. The HALLI utilizes a single column parallel partitioned pixel amplifier with variable topology for the detection of both high and low light levels in the same frame. For high light level detection, a wide dynamic range algorithm is utilized in which multiple resets via real-time feedback are employed. Each pixel in the field of view is independent and can automatically set its exposure time according to its illumination. For low light level detection, two noise reduction techniques are employed, active reset and active column sensor readout technique. Due to the commonalities in the high and low light level readout techniques, and the fact that they occur in staggered instances of time, a single partitioned pixel amplifier which can be configured in various modes of operation is used. The advantages of using a single column parallel partitioned pixel amplifier are simplicity in the analog readout path, reduced chip size, and lower power consumption than using individual dedicated blocks for each technique. The CMOS imager was designed and fabricated in a mixed signal 0.18 μm CMOS technology. System architecture, operation and results are presented.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".