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Wideband, low-noise accelerometer with open loop dynamic range of better than 135DB

2017· article· en· W2744113815 on OpenAlexafffund
Fatemeh Edalatfar, S. Azimi, Abdul Qader Ahsan Qureshi, Bahareh Yaghootkar, Behraad Bahreyni

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsSimon Fraser University
FundersSimon Fraser UniversityCMC MicrosystemsUniversity of Michigan
KeywordsAccelerometerWidebandBandwidth (computing)Capacitive sensingComputer scienceNoise (video)Electronic engineeringDynamic rangeSensitivity (control systems)AcousticsEngineeringPhysicsTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper reports a high performance capacitive accelerometer with a noise level of 350ng Hz , bandwidth of 4.5kHz, and 135 dB open loop dynamic range at 1Hz bandwidth. The accelerometer was designed in a mode-tuning structural platform. This platform is developed to address the issue between sensitivity and bandwidth for the low-noise capacitive in-plane accelerometers. The platform proposes to substitute the accelerometer's proof mass with a moving frame. Furthermore, the elastic elements and anchor points are located both inside and outside the moving frame. As such, the mode-tuning structural platform contributes in tuning the resonance frequencies and mode shapes of the accelerometer to acquire higher bandwidths. The proposed platform also addresses the issue of sensitivity by increasing the number of sensing elements located inside and outside the moving frame. To the best of the authors' knowledge, no MEMS accelerometer has been able to offer such a combination of high performance metrics, making this platform a viable candidate for developing high performance accelerometers.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.414

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 communication0.0000.001
Open science0.0010.000
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 head0.016
GPT teacher head0.254
Teacher spread0.238 · 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

Citations6
Published2017
Admission routes2
Has abstractyes

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