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Record W2071895082 · doi:10.1109/tuffc.2012.2351

An FPGA-based ultrasound imaging system using capacitive micromachined ultrasonic transducers

2012· article· en· W2071895082 on OpenAlexafffund
Lawrence L. P. Wong, Albert I. Chen, Andrew S. Logan, John T. W. Yeow

Bibliographic record

VenueIEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · 2012
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsUniversity of Waterloo
FundersCanadian Institutes of Health ResearchCMC MicrosystemsNational Science Foundation
KeywordsField-programmable gate arrayUltrasonic sensorCapacitive micromachined ultrasonic transducersTransducerCapacitive sensingPhased arrayEnvelope detectorAcousticsElectronic engineeringGate arraySIGNAL (programming language)EngineeringComputer hardwareComputer scienceElectrical engineeringCMOSPhysics

Abstract

fetched live from OpenAlex

We report the design and experimental results of a field-programmable gate array (FPGA)-based real-time ultrasound imaging system that uses a 16-element phased-array capacitive micromachined ultrasonic transducer fabricated using a fusion bonding process. The imaging system consists of the transducer, discrete analog components situated on a custom-made circuit board, the FPGA, and a monitor. The FPGA program consists of five functional blocks: a main counter, transmit and receive beamformer, receive signal pre-processing, envelope detection, and display. No dedicated digital signal processor or personal computer is required for the imaging system. An experiment is carried out to obtain the sector B-scan of a 4-wire target. The ultrasound imaging system demonstrates the possibility of an integrated system-in-a-package solution.

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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.246
Teacher spread0.236 · 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

Citations23
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Ultrasonics Ferroelectrics and Frequency ControlSame topicUltrasound Imaging and ElastographyFrench-language works237,207