High resolution tri-linear colour TDI CCD image sensor with programmable responsivity gain
Bibliographic record
Abstract
We developed a 6032 element, 32 stage Tri-linear Time Delay and Integration Focal Plane Array for high resolution colour imaging applications. The sensor offers an improvement of a factor of 10 over comparable line scan CCD sensors. The imager architecture utilizes three individual TDI arrays, with a new multi-layer dielectric interference film (DIF) color filter or a dyed polyamide patterned monolithic filter that helps achieve accurate spectral separation in the three primary colour bands centered at 450 nm, 550 nm and 650 nm respectively. We also incorporated in the sensor a new programmable responsivity mechanism. This is achieved by an on-chip imaging area size selection mechanism. The sensors operate in the time-delay-and-integration mode. Depending on the level of illumination the user is able to dynamically select the number of TDI stages per sensor. The total number of TDI gain stages can be selected in blocks of 32, 16, 8, or 4. This property provides improvement in the dynamic range of the sensor by extending the response of the CCD over a wide range of illumination levels. Depending on the light energy incident on the sensor, the user can dynamically vary the number of TDI stages used for integrating an incident scene. The result is a programmable responsivity depending on the level of incident illumination. Such technique optimizes the performance of the CCD colour sensor.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".