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Record W2771720622 · doi:10.15353/vsnl.v3i1.180

Compensated Row-Column Ultrasound Imaging System Using Edge-Guided Three Dimensional Random Fields

2017· article· en· W2771720622 on OpenAlexafffundvenue
Ibrahim Ben Daya, Albert I. H. Chen, Mohammad Javad Shafiee, John T. W. Yeow, Alexander Wong

Bibliographic record

VenueJournal of Computational Vision and Imaging Systems · 2017
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsColumn (typography)Speckle noiseEnhanced Data Rates for GSM EvolutionComputer scienceRow and column spacesSpeckle patternPoint (geometry)Noise (video)AlgorithmComputer visionMathematicsImage (mathematics)RowGeometryTelecommunications

Abstract

fetched live from OpenAlex

The row-column method is a simplification technique used to reducethe complexity of a fully addressed 2-D array. Although itgreatly reduces the number of physical connections required aswell as the amount of data to be handled, it still has limitations; itsimaging data output is sparse, it suffers from speckle noise, and itsspatially-dependant point spread function is riddled with edge artifacts.In this work, we propose a row-column ultrasound imagingsystem, termed CRUIS3D, that uses a 3-D edge-guided randomfield approach to compensate for the limitations of the row-columnmethod. Tests on CRUIS3D and previously published row-columnsystems show the effectiveness of our proposed system as a toolfor enhancing 3-D row-column ultrasound imaging.

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: Simulation or modeling · Consensus signal: none
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)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.020
GPT teacher head0.295
Teacher spread0.275 · 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 designSimulation or modeling
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

Citations0
Published2017
Admission routes3
Has abstractyes

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