A novel double sampling technique for delta-sigma modulators
Bibliographic record
Abstract
The double sampling technique is used to achieve twice the sampling frequency in sampled-data systems without extra requirements (e.g., clock rate, op-amp settling time, op-amp dc gain, etc.). In this paper, theoretical analyses of the effects of nonidealities (integrator leakage, path gain mismatch and non-uniform sampling) on the performance of double sampling delta-sigma modulators (/spl Delta//spl Sigma/M's) are given. A novel double sampling technique for /spl Delta//spl Sigma/M's which is insensitive to the path gain mismatch is also presented. This technique uses a bilinear integrator in the first stage, resulting in a first order shaping of the path gain mismatch error. Compared with a second-order single sampling /spl Delta//spl Sigma/M, this technique is able to achieve 15 dB improvement of S/N and 6 dB relaxation of op-amp dc gain.
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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 | 0.000 | 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".