On the Design of DACs for Dynamic Calibration Applications using Periodic Sequences from ΣΔ Modulators
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Bibliographic record
Abstract
This paper presents a design procedure for constructing DACs for dynamic calibration applications using periodic sequences from ΣΔ modulators. These DACs are in a unique class of their own and have unique design requirements. The design procedure follows a three step process: (1) selecting the ΣΔ modulator order based on the desired output full-scale range, (2) selecting the cut-off frequency of the low pass filter based on the desired bit resolution, followed by (3) the selection of the low pass filter order that yields the lowest settling time. A design example is used through out the paper to show how this procedure can be used. A comparison with other DAC realizations will also be presented. It will be shown that the proposed procedure creates a DAC realization that has extremely low settling time (relatively speaking), which is a very important performance measure in DACs that are used in dynamic calibration applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it