Advanced Coupling Matrix and Admittance Function Synthesis Techniques for Dissipative Microwave Filters
Bibliographic record
Abstract
In this paper, novel approaches to synthesize admittance function polynomials and canonicalN+2 coupling matrices for narrowband lossy filters are presented. The methods are simpler and more general than the ones found in the literature. The polynomial synthesis approach is fully analytical and also very useful for lossless polynomial synthesis with simpler derivations. The coupling matrix synthesis method is based on a lossy transversal network model, which can also accommodate direct source to load coupling. Unlike the lossless transversal coupling matrix, the lossy coupling matrix model requires the assumption of complexJ-inverters and additional resistive elements in the network. The complexJ-inverter circuit model is defined and explained in detail in this paper. The lossy transversalN+2 matrix can be systematically rotated to obtain a number of practical realizations. Parallel-coupled pairs and folded lossy configurations are shown as examples. Moreover, the synthesis of novel networks with different return-loss levels at source and load is presented. A performance comparison with a predistorted filter is also included in this paper.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".