Optimal design of IIR frequency-response-masking filters using second-order cone programming
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Bibliographic record
Abstract
The frequency-response-masking (FRM) technique proposed by Lim (1986) has proven effectiveness for the design of very sharp digital filters with reduced implementation complexity compared to other options. In this paper, we propose a constrained optimization method for the design of basic and multistage FRM filters where the prototype filters are of infinite-impulse response (IIR) with prescribed pole radius. The design is accomplished through a sequence of linear updates for the design variables with each update carried out using second-order cone programming. Computer simulations have demonstrated that the class of IIR FRM filters investigated in the paper offers an attractive alternative to its finite-impulse response counterpart in terms of filter performance, system delay, and realization complexity.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| 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