High-order tunable passive digital filters
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, high-order tunable low-pass, high-pass, band-pass, and band-stop passive digital filters for realtime sharp cutoff filtering applications is introduced. An analytical expression for each of the coefficients of the first-order and the second-order passive digital filter sections after low-pass to low-pass/high-pass frequency transformation has been derived. For the low-pass to band-pass/band-stop frequency transformation, an analytical expression for each of the coefficients of the second-order and the decomposed second-order passive digital filter sections has also been derived. These analytical expressions facilitate the real-time computation of all the required filter coefficients, from any specified new cutoff frequency for the low-pass/high-pass case, and from any specified new center frequency and/or new bandwidth for the band-pass/band-stop case.
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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 | 0.001 | 0.002 |
| 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