Wide Bandwidth Signals for Joint Time–Frequency Characterization of Nonlinear and Time-Varying Circuits
Bibliographic record
Abstract
In this work, we generate and use a total of six different wideband signals for joint time–frequency characterization of nonlinear time-invariant [N-shaped differential resistor (NDR)] and linear time-varying (thermistor) circuits. A data acquisition board is used for applying the signals in the form of a voltage excitation and reading the induced current. The input signals have flat power spectra, thus avoiding the need for iterative calibration loops required to obtain signals with low crest factor. Such iterative loops are unavoidable when using random, pseudorandom, or chaotic signals all of which have a wideband spectrum but not necessarily a flat one. Flat spectra help in controlling the maximum voltage within the generated signal, thus eliminating large spikes in the obtained signal. The accuracy of the measurements is compared with the measurements acquired on a Biologic VSP-300 electrochemical station. An average relative error margin of 3% at most is obtained.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".