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Record W2082020276 · doi:10.1109/acssc.2014.7094726

Immersion ultrasonic array imaging using a new array spatial signature in different imaging algorithms

2014· article· en· W2082020276 on OpenAlex
Nasim Moallemi, Shahram Shahbazpanahi

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.

Bibliographic record

Venue2014 48th Asilomar Conference on Signals, Systems and Computers · 2014
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsBeamformingUltrasonic sensorSignature (topology)TransducerAlgorithmComputer scienceAcousticsUltrasonic imagingMathematicsPhysicsTelecommunications

Abstract

fetched live from OpenAlex

In this paper, we consider the problem of immersion ultrasonic test when a uniform array of ultrasonic transducer is utilized. Since the sound wave propagates in water and solid with two different velocities, the imaging techniques for homogeneous materials can not be utilized. In this paper, we have used a new array spatial signature, which is derived based on distributed source modeling, in three imaging algorithms including the conventional beamforming technique, the MUSIC method, and the Capon algorithm. To show the accuracy of the proposed array spatial signature, we conducted an ultrasonic immersion test. Experimental results of the three aforementioned imaging algorithms, presented here, show the accuracy of the proposed array spatial signature.

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 categoriesMeta-epidemiology (narrow)
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 score0.898
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.011
GPT teacher head0.211
Teacher spread0.200 · 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