Fast Spectral Impedance Measurement Method Using a Structured Random Excitation
Bibliographic record
Abstract
The need for fast impedance measurement methods and instruments is necessary for accurate on-line characterization of many bio(electro)chemical systems in which most of the important processes occurs at low frequencies. However, the measurement by means of the traditional single-sine sweep signal is known to be a time consuming task particularly at ultra-low frequencies. In this work, we demonstrate the synthesis of a structured signal generated from a Gaussian white noise time series such that its power spectral magnitude is relatively flat over a wide bandwidth while maintaining a random phase. Such a signal allows the simultaneous measurement for multiple frequencies at once. When used to characterize a precision standard cell, the average deviation of the cell's spectral impedance from that measured using single-sine sweep on a research-grade BioLogic VSP-300 electrochemical station was about 1.03% over the frequency range 2 mHz to 200 kHz. Concurrently, the measurement time was reduced by a factor of 6.08 times compared to the reference instrument. The proposed methodology and processing can be readily adapted to other types of noises for fast impedance measurement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".