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Record W2990305754 · doi:10.1109/tim.2019.2954146

A Bridge-Balancing Circuit for Balanced Measurement of Resonant Sensors

2019· article· en· W2990305754 on OpenAlexafffund
Henry Brausen, Jeremy C. Sit

Bibliographic record

VenueIEEE Transactions on Instrumentation and Measurement · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanoelectromechanical systemsBroadbandMicroelectromechanical systemsElectronic engineeringRLC circuitComputer scienceElectrical engineeringElectronic circuitCircuit designSIGNAL (programming language)Circuit extractionEngineeringEquivalent circuitTelecommunicationsPhysicsCapacitorOptoelectronics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.640
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.254
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2019
Admission routes2
Has abstractyes

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