Design of IIR filters with canonical signed-digit (CSD) coefficients using genetic algorithms.
Bibliographic record
Abstract
In this thesis, an optimization method is used in the design of 1-D IIR digital filters, doubly complementary filter pairs and 2-D IIR digital filters. This method uses genetic algorithm (GA) to minimize the mean square error between the desired and the designed filter responses in order to calculate the coefficients of the filter's transfer function. The 1-D IIR filters are designed using cascade structure, and constraints are imposed on the coefficients of the denominator polynomials to ensure stability. In the one-dimensional case, the method is also used to design doubly complementary filter pairs, in which case two filters that are both all-pass complementary and power complementary are designed at the cost of one filter. Based on the same stability criterion, an optimization method is presented for the design of 2-D IIR digital filters with non-separable numerator and separable denominator transfer functions. The advantage of the proposed method is that it produces filters with Canonical Signed-Digit (CSD) coefficients, which not only eliminates the quantization process in digital filter design but also make the designed filter more efficient for high speed DSP applications. Design examples of 1-D IIR filters, doubly complementary filter pairs and 2-D IIR filters are provided in order to demonstrate the usefulness of the presented method.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .L555. Source: Masters Abstracts International, Volume: 42-03, page: 1014. Adviser: M. A. Sid-Ahmed. Thesis (M.A.Sc.)--University of Windsor (Canada), 2003.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".