Bibliographic record
Abstract
This thesis is devoted to the presentation of a simplified synthesis method and its application in the development of a computer program for the synthesis of LC ladder networks. The approach developed in the thesis is based on scattering transfer matrix theory which does not seem to have been applied directly to ladder synthesis before. The most important part of this thesis is the determination of a scattering sy thesis strategy for choosing the sequence of transmission zeros. A synthesis strategy was developed by W. F. Gottlicher for application to the traditional synthesis procedure. Gottlicher's method, presented in his thesis, is adapted as the basis of the scattering strategy. In addition, this thesis introduces a set of simplified scattering calculations by appropriately modifying the details of Gottlicher's strategy. The simplified scattering algorithm involves only calculation of the reflectance and the delay at a transmission zero and at zero or infinity. For multiple transmission zeros at zero orinfinity, the second and third derivatives of polynomials ' g' and 'h' at zero or infinity are required in addition. The main results are given in 'Chapter 3'. In that chapter, the derivation of the scattering characterization for all of the circuits and realization details of the modified strategy are presented. In ' Chapter 4', the flowgraph of the simplified scattering LC ladder filter synthesis program together with several design examples is presented. The thesis concludes with a summary and recommendations.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".