THE FORWARD TRANSMISSION MATRIX (FTM) METHOD FOR S-PARAMETER ANALYSIS OF MICROWAVE CIRCUITS AND THEIR METAMATERIAL COUNTERPARTS
Bibliographic record
Abstract
In classical Electromagnetics textbooks, microwave circuits such as circulators, couplers, and filters are solved by non-systematic approaches such as even-odd mode analysis.Hence an electrical engineering student coming from the conventional circuit theory background encounters difficulties in understanding and solving microwave circuits.In this paper, we propose a modified node voltage analysis method in which the circuit branches are represented by their forward transmission matrices so that the electromagnetic wave propagation is taken care of.The Kirchhoff's current rule, tailored for high frequencies, is applied to formulate the simultaneous node voltage equations which are subsequently solved by matrix inversion.The proposed forward transmission matrix (FTM) method is applied to evaluate the S-parameters of some well-known microwave devices including the recently-developed metamaterial-based circuits.The FTM node analysis is a natural extension of the classical node analysis which is taught in the early stages of an Electrical Engineering program.Hence we anticipate that the proposed method will ease up the conceptual transition of electrical engineering students and academicians from the low-frequency alternating current circuits to high frequency RF and microwave circuits.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".