Effect of 1/ <i>f</i> noise in integrating sensors and detectors
Bibliographic record
Abstract
The authors calculate the variance in the output of an integrating sensor or detector when in the presence of 1/fα noise in the input of the sensor. The calculations are based on mapping the detector onto a linear, time-invariant filter; the approach is general and can be used for any detector that can be so mapped. Formulae for the output variance and signal-to-noise ratio are given for a simple integrating detector and a detector with three different methods of background subtraction, including double sampling, that has two integrations, and triple sampling where the average of two integrations before and after the signal is subtracted from the integration during the signal. The authors consider cases in which α is unity, less than unity and more than unity, given that quite often α is not ideally unity. Also, for the case of an integrating detector that is used to sample a signal, a formula is derived for the expected variance of N samples when the input contains 1/f noise. The authors apply the treatise herein to the input stage of an a-Se based flat panel X-ray image detector and demonstrate that the 1/f noise fluctuations in the dark current of the photoconductor exceeds those because of shot noise.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".