FIR filter design using Jacobian elliptic Sn function with wavelet applications
Bibliographic record
Abstract
This thesis presents a new method of digital FIR filter design based on the Jacobian elliptic sine function. In addition to a general design, a special result is obtained for Quadrature Mirror Filters (QMF) for use in multi-resolution filter banks. These are called Quasi-Dyadic FIR filters. Most of the Quasi-Dyadic filter coefficients are alpha2-n , where alpha is the scale of the coefficients and n is a positive integer. With these special coefficients, the 'multiply' operation may be changed to a 'shift' operation in assembly coding in signal filtering. Since 'shift' is much faster than the 'multiply' instruction in most microprocessors or DSP chipsets, the Quasi-Dyadic filter is more efficient. For multi-resolution filter banks (wavelet-like), perfect reconstruction (PR) is shown to be possible by a novel strategy. In addition, simpler structures that do not provide PR are shown to be valuable in practical applications. Implementation on a DSP board is also presented. Source: Masters Abstracts International, Volume: 39-02, page: 0567. Adviser: James Soltis. Thesis (M.A.Sc.)--University of Windsor (Canada), 2000.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".