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Wideband Air-coupled Piezoelectric MEMS Ultrasonic Transceiver

2024· article· en· W4405517669 on OpenAlexaff
Seyedfakhreddin Nabavi, Mathieu Gratuze, Frédéric Nabki

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsTransceiverMicroelectromechanical systemsUltrasonic sensorPiezoelectricityWidebandAcousticsPMUTElectronic engineeringElectrical engineeringMaterials scienceComputer scienceEngineeringTelecommunicationsWirelessOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Piezoelectric micromechanical ultrasonic transducers (pMUTs) operating in air have received great attention for airborne applications. Indeed, air-coupled pMUTs have lower resonant frequencies and unfortunately suffer from low fractional bandwidth. This study proposes a novel geometry for pMUT transceivers which can be fabricated using standard bulk micromachining processes. The proposed design consists of four standalone triangle-shaped flags, each positioned adjacent to the other with a mirrored orientation and an air gap size of 2 μm, forming an overall square configuration. The design is fabricated using a commercially available PiezoMUMPs process. The performance of the pMUT in both receiving and transmitting of ultrasonic waves is evaluated. Experimental results show a resonant frequency of 200 kHz and a fractional bandwidth of 30%.

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 score0.006

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 communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.010
GPT teacher head0.208
Teacher spread0.197 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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