Analytical synthesis of <i>elliptic</i> voltage‐mode even/odd‐ <i>n</i> th‐order filter structures using DDCCs, FDCCIIs, and grounded capacitors and resistors
Bibliographic record
Abstract
Despite the recent publication of the analytical synthesis of voltage‐mode even/odd‐ n th‐order differential difference current conveyor (DDCC) and fully differential current conveyor II (FDCCII)‐grounded resistor and capacitor universal Butterworth/Chebyshev filter structures, an elliptic voltage‐mode even/odd‐ n th‐order DDCC and FDCCII‐based filter structure is yet to be presented in the literature. Under the restriction of a finite order, the elliptic filter is better at meeting the stringent cut‐off rate of a very narrow transition band as compared to other kinds of filters, thus making the synthesis of such an elliptic filter to be extremely useful. In this study, one even‐ n th‐order and two odd‐ n th‐order elliptic filter structures are analytically synthesised using DDCCs and FDCCIIs. The feasibility of such structures is validated through H‐spice simulations on the proposed elliptic third‐order low‐pass and elliptic fourth‐order low‐pass, high‐pass, band‐pass, and band‐reject filters.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".