Design of FRM Digital Filters Over the CSD Multiplier Coefficient Space Employing Genetic Algorithms
Bibliographic record
Abstract
It is well known that the use of canonical signed-digit (CSD) multiplier coefficients in combination with sub-expression sharing and elimination leads to a substantial reduction in the hardware complexity of FIR digital filters. This paper presents a genetic algorithm for the design and optimization of frequency-response masking (FRM) FIR digital filters over the CSD multiplier coefficient space. This is based on designing a corresponding infinite-precision-coefficient digital filter seed (through a conventional continuous optimization), and on quantizing the resulting multiplier coefficients into CSD coefficients via a look-up table. The resulting digital filter is subsequently encoded into a chromosome which is perturbed to form an initial population for the genetic algorithm. The salient feature of the resulting genetic algorithm is that it automatically leads to legitimate CSD-coefficient offspring digital filters after the operations of crossover and mutation, i.e. without any recourse to gene repair. Application to the design of a bandpass FIR digital filter produces a CSD-coefficients digital filter with very close performance to that obtained by the corresponding continuous infinite-precision optimization
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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 | 0.000 | 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".