Dispersion Properties and Applications of the Coifman Scaling Function Based S-MRTD
Bibliographic record
Abstract
We illustrate some salient dispersion properties of the Coifman scaling function based multiresolution time domain (MRTD) technique (Coifman S-MRTD) and discuss its applicability to modeling problems of interest in microwave and wireless communication engineering. Having been recently introduced, this method presents advantages similar to those of the Daubechies-based MRTD, namely highly linear numerical dispersion and finite support of the basis functions involved. It is additionally shown that inherent accuracy-computational complexity trade-offs related to with its dispersion properties can be utilized to accelerate its execution, without compromising its accuracy. Since the Coifman basis function is non-symmetric, the modeling of perfect electric conducting boundaries cannot be pursued via the image theory approach presented in the past. Therefore, a modified approach, along with its computationally efficient implementation, is proposed and validated. Several case studies and comparisons with the conventional finite-difference time-domain method demonstrate the usefulness of Coifman S-MRTD as a time-domain analysis and design tool
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".