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Record W2032549074 · doi:10.1115/detc2014-34817

Actuation of a Frequency Modulated MEMS Gyroscope

2014· article· en· W2032549074 on OpenAlexaff
Michael Xie, Sangtak Park, Eihab Abdel‐Rahman, Mustafa Yavuz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBreadboardGyroscopeRLC circuitSIGNAL (programming language)Frequency modulationVoltage-controlled oscillatorFeed forwardSignal generatorElectronic circuitElectronic filterControl theory (sociology)Vibrating structure gyroscopeFrequency multiplierEngineeringElectronic engineeringComputer scienceElectrical engineeringRadio frequencyVoltageCapacitorCMOS

Abstract

fetched live from OpenAlex

This paper describes an analog actuation circuit for a novel frequency-modulated MEMS gyroscope. The circuit provides an amplitude-modulated (AM) signal as the input into a RLC resonant drive circuit, which drives the gyroscope. The actuation system is composed an automatic gain control (AGC) loop, a low pass filter, an amplitude modulation component and a resonant drive circuit. The AM signal is composed of a modulating signal that excite a natural frequency of gyroscope drive mode and a carrier signal with a frequency corresponding to the electrical resonant frequency of the RLC circuit. Both feedforward and feedback AGC configurations are used to stabilize the envelope of the signal. However, the breadboard implementations of the feedforward and feedback circuits in their current configurations have similar signal to noise ratio to that of the function generator. To improve the actuation circuit performance, we plan to include the resonant drive circuit within the AGC feedback loop and implement the actuation circuit on PCB.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.006
GPT teacher head0.198
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2014
Admission routes1
Has abstractyes

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