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Record W2153913415 · doi:10.1109/iscas.2009.5118289

Design and implementation of a Low-power Intensity Pulsed-Ultrasound generator for dental tissue regeneration

2009· article· en· W2153913415 on OpenAlexafffund
Woon Tiong Ang, Cristian Scurtescu, Tarek El‐Bialy, Ying Y. Tsui, Jie Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsImpedance matchingTransducerElectrical impedancePower (physics)Generator (circuit theory)Electrical engineeringUltrasonic sensorController (irrigation)Electronic engineeringComputer scienceMaterials scienceEngineeringAcousticsPhysics

Abstract

fetched live from OpenAlex

This paper presents the design and implementation of a Low-Intensity Pulsed Ultrasound (LIPUS) generator for dental tissue regeneration. It consists of a power supply subsystem, an ultrasonic transducer, an impedance-matching circuit and an integrated circuit consisting of digital controller circuitry and driver circuit. The integrated circuit was designed and fabricated using 0.8µm High-Voltage Technology from Dalsa Semiconductor Inc. The power supply sub-system and impedance matching network are implemented using discrete components. Upon construction, the LIPUS generator was verified to function correctly and is capable of producing LIPUS power upwards of 100mW in the vicinity of the transducer';s resonance frequency. Power efficiency of the circuitry, excluding the power supply sub-system, is estimated at 70%.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 communication0.0000.000
Open science0.0010.000
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 head0.250
Teacher spread0.240 · 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
GenreMethods

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
Published2009
Admission routes2
Has abstractyes

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