Realizing Non-Foster Reactive Elements Using Negative-Group-Delay Networks
Bibliographic record
Abstract
An intimate relation is established between non-Foster reactive elements and loss-compensated negative-group-delay (NGD) networks. It is shown that any possible network configuration containing a class of non-Foster elements operates as an NGD network. Likewise, it is demonstrated that a loss-compensated NGD network represents a reactive network with a non-Foster behavior. Consequently, these two properties can be intimately linked together and NGD networks can be utilized to implement non-Foster elements, such as negative capacitors and inductors. This result introduces another perspective in realizing non-Foster reactive elements, leading to new designs that are well behaved and more predictable in terms of stability and operation than traditional designs using negative impedance inverters and negative impedance converters. Based on this concept, loss-compensated NGD networks are proposed for realizing high-quality non-Foster reactive elements. Furthermore, entirely passive non-Foster elements with a limited quality ( Q) factor are proposed for which the minimum Q factor and the maximum achievable bandwidth are inversely related. It is shown that the design of non-Foster reactive elements using NGD networks can lead to the realization of standalone unilateral non-Foster reactive elements in a certain bandwidth. Examples of such non-Foster reactive elements and networks are demonstrated experimentally and shown to be stable.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".