Rapid optimization of FRM digital filters over CSD multiplier coefficient space using a diversity controlled genetic algorithm
Bibliographic record
Abstract
In a preceding paper, it was shown that the canonical signed-digit (CSD) representation of the multiplier coefficients in frequency response-masking (FRM) FIR digital filters leads to a substantial reduction in the hardware complexity of the FIR digital filter. However, a direct approximation of the infinite-precision multiplier coefficients to their CSD counterparts may cause the FIR digital filter to cease to satisfy the given filter design specifications. This paper presents a novel technique based on diversity controlled (DC) genetic algorithm (GA) for the discrete optimization of FRM FIR digital filters over the CSD multiplier coefficient space. The salient feature of the DCGA technique is that it permits external control over population diversity and parent selection pressure, giving rise to a rapid convergence to an optimal solution. It is shown that the application of the proposed DCGA technique to the optimization of a benchmark low-pass FRM digital filter over CSD multiplier coefficient space leads to an order of magnitude speed-up factor as compared to that associated with a conventional GA. Moreover, the optimized CSD FRM digital filter outperforms the corresponding infinite-precision digital filter obtained by the classical Parks-Mclellan approach.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".