A Novel Discrete Particle Swarm Optimization for FRM FIR Digital Filters
Bibliographic record
Abstract
This paper presents a novel discrete particle swarm optimization (PSO) for frequency response masking (FRM) finite impulse response (FIR) digital filters over the canonical signed-digit (CSD) multiplier coefficient space. A look-up table (LUT) scheme is employed to ensure that the PSO automatically searches through permissible CSD multiplier coefficient values in the course of optimization without any recourse to backtracking. This is achieved by searching through the indices of the CSD multiplier coefficient values in the LUT instead of the coefficient values themselves. In this way, the resulting multiplier coefficient values are ensured to conform to a prespecified wordlength as well as to a prespecified maximum number of non-zero digits. The salient feature of this LUT scheme is that by introducing barren layers in the LUT, there is no need to limit the search space manually in the course of PSO to prevent from going over the boundaries of the search space. Examples are given to illustrate the application of the proposed PSO to the design and optimization of a lowpass and a bandpass FRM FIR digital filters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".