MétaCan
Menu
Back to cohort
Record W2906340251 · doi:10.1109/tbcas.2018.2888990

Quasi Class-DE Driving of HIFU Transducer Arrays

2018· article· en· W2906340251 on OpenAlex
C.E. Christoffersen, Thinh Ngo, Ruiqi Song, Yushi Zhou, Samuel Pichardo, Laura Curiel

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Biomedical Circuits and Systems · 2018
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsUniversity of CalgaryLakehead University
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsTransducerElectrical impedanceVoltageElectronic engineeringAcousticsPower (physics)Dynamic rangeElectrical engineeringComputer scienceMaterials scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

Recently, a method was proposed to determine the parameters for each Class DE driver in high-intensity focused ultrasound arrays for efficient operation and to compensate for variations in the impedance of each array element. This work extends that method to consider the effect of switch resistance and to provide limited control on the power delivered to the transducers with a constant supply voltage while keeping a good efficiency. The method is experimentally validated using an integrated driver developed by the authors. This paper also shows that the frequency range for efficient electrical operation is close to the frequency where the transducer array presents a peak in the conversion efficiency.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.744
Threshold uncertainty score0.548

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.018
GPT teacher head0.263
Teacher spread0.245 · 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