A Bridge-Balancing Circuit for Balanced Measurement of Resonant Sensors
Bibliographic record
Abstract
We present a simple and low-cost signal-balancing circuit for application to electronic readout of resonant sensors. The circuit described is broadband and amenable to integration with a wide variety of balanced measurement schemes. We verify the operational performance of our circuit with a vector network analyzer, demonstrating achievable antiphase signal cancellation on the order of 100 dB under direct summing. We then present a number of illustrative application examples, demonstrating how our circuit can compensate for nonidealities in a number of test and measurement configurations. In the interest of making the field of microelectromechanical system (MEMS) and nanoelectromechanical system (NEMS) resonant sensors more accessible to researchers, we are releasing all design documents, lowering the barrier to entry to researchers wishing to integrate resonant sensors into their research programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".