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Record W2155973730 · doi:10.1109/tuffc.2008.799

Design of a MEMS discretized hyperbolic paraboloid geometry ultrasonic sensor microarray

2008· article· en· W2155973730 on OpenAlexafffund
Matthew Meloche, Sazzadur Chowdhury

Bibliographic record

VenueIEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · 2008
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsUniversity of Windsor
FundersCMC Microsystems
KeywordsParaboloidUltrasonic sensorDiscretizationMicroelectromechanical systemsAcousticsGeometryMathematical analysisMathematicsMaterials sciencePhysicsSurface (topology)Optoelectronics

Abstract

fetched live from OpenAlex

Design of a discretized hyperbolic paraboloid geometry beamforming array of capacitive micromachined ultrasonic transducers (CMUT) has been presented. The array can intrinsically provide a broadband constant beamwidth beamforming capability without any microelectronic signal processing. A mathematical model has been developed and verified to characterize the array response. A design methodology has been presented that enables determination of the array's physical dimensions and CMUT modeling in a straightforward manner. Developed methodology has been used to design two discretized hyperbolic paraboloid geometry beamforming CMUT arrays: one in the 2.3 MHz to 5.2 MHz frequency range and another in the 113 kHz to 167 kHz frequency range. CMUTs have been designed using a cross-verification method that involves lumped element modeling, 3-D electromechanical finite element analysis (FEA), and microfabrication simulation. The developed array has the potential to be used in real-time automotive collision-avoidance applications, medical diagnostic imaging and therapeutic applications, and industrial sensing.

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 categoriesMeta-epidemiology (narrow)
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 score0.771
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.195
Teacher spread0.182 · 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.

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

Citations16
Published2008
Admission routes2
Has abstractyes

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