EM based design of large-scale dielectric resonator multiplexers by space mapping
Bibliographic record
Abstract
A novel design methodology for multiplexer design is presented. For the first time, finite element EM based simulators and space-mapping optimization are combined to produce an accurate design for manifold coupled output multiplexers with dielectric resonator (DR) loaded filters. 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. Therefore channel dispersion and spurious modes are taken into account. The DR filter channel design parameters are kept bounded during optimization. Our approach has been used to design large-scale manifold coupled output multiplexers and it has significantly reduced the overall tuning time compared to traditional techniques. The technique is illustrated through design of a 10-channel output multiplexer with 5-pole DR filter based channels.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".