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Record W2023868887 · doi:10.1109/ultsym.2013.0153

S-sequence encoded synthetic aperture B-scan ultrasound imaging

2013· article· en· W2023868887 on OpenAlexaff
Roger J. Zemp, Alexander Sampaleanu, Tyler Harrison

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHadamard transformComputer scienceSynthetic aperture radarAperture (computer memory)Noise (video)Encoding (memory)Coded apertureSIGNAL (programming language)AlgorithmComputer visionAcousticsArtificial intelligenceImage (mathematics)PhysicsTelecommunicationsDetector

Abstract

fetched live from OpenAlex

Traditional synthetic aperture ultrasound scanning involves firing on one element and receiving on all elements of an array, then firing another transmit element and receiving on all elements until all transmit-receive element pairs have been sampled. It offers excellent resolution but at the expense of signal-to-noise. To remedy this difficulty we propose an aperture encoding scheme that involves firing on multiple elements sampled from a Hadamard S-matrix, then applying matrix inversion methods to decouple signals to recover the effective synthetic aperture imaging set for subsequent image reconstruction. Because more than one elements are fired at a time, signal-to-noise is improved.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.184
Teacher spread0.178 · 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 teacher head, not a consensus.

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

Citations17
Published2013
Admission routes1
Has abstractyes

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