Compact closed‐loop resonator filters with wide spurious free band and extended common‐mode noise suppression
Bibliographic record
Abstract
A theoretical analysis is presented for miniaturised filters with an extended stopband and extended common‐mode (CM) rejection, using loaded closed‐loop resonators. A numerical method is presented to solve the equations extracted from the ideal transmission line model. Selection of critical resonator parameters to minimise filter area while simultaneously maximising the differential‐mode (DM) stopband or the CM noise suppression is explored. The trade‐off between size, stopband and CM rejection is presented and used to design three filters at 1 GHz, which are designed to verify the proposed concept. First, a differential second‐order filter with an extended DM stopband ( dB) up to and extended CM noise suppression ( dB) up to 6.42 is realised to show the parameter trade‐offs. Then optimal resonators are used to develop a compact singled‐ended filter ( ) with an extended stopband ( dB) up to and the smallest size. Lastly, a second order filter with an optimal extended CM rejection ( dB) up to is presented. Measured results are presented. All three filters stand out compared to other works by exhibiting the smallest footprint relative to the operating frequency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".