EM-Based Design of Large-Scale Dielectric-Resonator Filters and Multiplexers by Space Mapping
Bibliographic record
Abstract
A novel design methodology for filter and multiplexer design is presented. For the first time, finite-element electromagnetic (EM)-based simulators and space-mapping optimization are combined to produce an accurate design for manifold-coupled output multiplexers with dielectric resonator (DR) loaded raters. Finite-element EM-based simulators are used as a fine model of each multiplexer channel, and a coupling matrix representation is used as a coarse model. Fine details such as tuning screws are included in the fine model. The DR filter and multiplexer design parameters are kept bounded during optimization. The sparsity of the mapping between the design parameters and the coupling elements has been exploited. Our approach has been used to design large-scale output multiplexers and it has significantly reduced the overall tuning time compared to traditional techniques. The technique is illustrated through design of a five-pole DR filter and a ten-channel output multiplexer.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 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".