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Record W1994120153 · doi:10.1109/iscas.2014.6865276

Receiver design for CMUT-based super-resolution ultrasound imaging

2014· article· en· W1994120153 on OpenAlexaff
Parisa Behnamfar, Reza Molavi, Shahriar Mirabbasi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCapacitive micromachined ultrasonic transducersTransimpedance amplifierInterfacingUltrasonic sensorCapacitive sensingComputer scienceAmplifierElectronic engineeringTransducerVoltageCMOSAcousticsElectrical engineeringEngineeringOperational amplifierComputer hardwarePhysics

Abstract

fetched live from OpenAlex

Applications of the capacitive-micromachined ultrasonic transducers (CMUTs) operating in their fundamental frequency of vibration have been extensively studied. Recent research on asymmetric mode of vibration has shown promising results in construction of super resolution ultrasound images. This paper presents the design of a receiver circuit that supports both the fundamental and asymmetric modes of operation. The receiver includes transimpedance amplifiers that convert the current signals from the CMUT devices into voltage. Furthermore, low-power variable-gain stages are included to amplify the resulting signals and facilitate interfacing to the ultrasound imaging machine for additional processing and display. The receiver is designed and laid out in a 0.35-μm CMOS process. Post-layout simulations show that each receiver channel has a nominal gain of 110 dBΩ up to 10 MHz while consuming 925 μW from a 3.3 V supply.

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)0.001
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.017
GPT teacher head0.256
Teacher spread0.239 · 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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