A novel approach to the design of higher-order Bode-type variable-amplitude wave-digital equalizers
Bibliographic record
Abstract
This paper presents a novel approach to the design of higher-order Bode-type variable-amplitude (VA) wave-digital (WD) equalizers. This approach is based on, (a) the derivation of a corresponding continuous-time VA equalizer transfer function from a set of high-level system design specifications, (b) the development of an analog prototype reference network suitable for a corresponding WD equalizer realization, and (c) the transformation of the analog prototype reference network to the desired VA WD equalizer through the use of the bilinear frequency transformation. The salient feature of the resulting VA WD equalizers is that they permit the continuous variation of the WD equalizer transfer function from a shaping transfer function to its inverse while requiring one single variable digital multiplier only. In addition, they exhibit the important practical feature that a geometrically symmetric change in the value of the variable digital multiplier causes a corresponding arithmetically symmetric change in the logarithmic magnitude-frequency response of the WD equalizer. An application example is given to illustrate the main results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".