Design and realization methods for IIR multiple notch filters and high-speed narrow-band and wide-band filters
Bibliographic record
Abstract
In this thesis, a direct IIR design method for real WDFs based on Gazsi's work is summarized in detail, and the cascade realization of first- and second-order allpass sections is generalized to any IIR transfer function, then a simple design method for bireciprocal lattice WDFs is given. A design and realization method for IIR multiple notch filters based on the phase of an allpass filter approximation is described. A design and realization method for high speed narrow-band and wide-band WDFs based on the IFIR technique is given, both nonlinear and approximately linear phase filters are considered; the narrow-band filter is composed of a model filter and one or several masking filters in cascade. In the case of nonlinear phase, conventional lattice and bireciprocal lattice WDFs are used for the model and masking filters; the overall narrow-band filters can be designed by separately designing the model and masking filters. The wide-band filter is composed of a narrow-band filter in parallel with a series of allpass filters, to obtain an overall wide-band filter. The narrow-band filter is designed first, and is then connected in parallel with one of the allpass filters of the narrow-band filter. In the case of approximately linear phase, the linear phase IIR filter is used for the model filter, and a maximum flat linear phase FIR filter is used for the masking filter. Several advantages of these filters over directly designed filters are that they have a substantially higher maximal sample frequency, lower roundoff noise and lower finite wordlength. Several design examples are given to demonstrate the properties of these 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".