Analysis of the coefficients of generalized bilinear transformation in the design of 2-D band-pass and band-stop filters and an application in image processing
Bibliographic record
Abstract
Due to rapid progress in the field of speech and image processing, there is a greater need of a digital filter which possesses variable magnitude characteristics; one such filter is proposed in this paper. The proposed 2-D band-pass and band-elimination filters are designed from a 1-D low-pass Butterworth filter by applying a low-pass to band-pass and low-pass to band-stop transformations respectively. The resulting structure is converted to 2-D analog filter by making the series arm having impedances in s/sub 1/-domain and the shunt-arm impedances in the s/sub 2/-domain. The filters so obtained are digitized using the generalized bilinear transformations, thereby giving eight variables to be adjusted, giving a large number of possible magnitude characteristics. It is further shown how these filters can be utilized in reducing the noise content in images.
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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 | 0.002 |
| 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) | 0.001 | 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".