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Record W3158651664 · doi:10.18280/rces.080102

A Non-Destructive Imaging Method Based on Integral Signals of Ultrasonic Pulse

2021· article· en· W3158651664 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueReview of Computer Engineering Studies · 2021
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsUltrasonic sensorSuperposition principlePhased arraySIGNAL (programming language)Eigenvalues and eigenvectorsInverse problemNondestructive testingOperator (biology)AcousticsComputer scienceMathematicsPhysicsMathematical analysisTelecommunications

Abstract

fetched live from OpenAlex

For ultrasonic phased array imaging, the most popular technique is the delay superposition algorithm of time domain signals with fixed weights. However, this technique and similar approaches cannot effectively suppress the non-scanning azimuth noise, which drags the imaging resolution. To overcome the problem, this paper proposes a nondestructive imaging method based on ultrasonic pulse integral signal, on the basis of ultrasonic phased array imaging. This method relies on the Green function to implement inverse Laplace transform on the ultrasonic pulse signal, obtains the analytical expression of the operator through mathematical derivations and calculations, and adopts the eigenvalues obtained by Laplacian matrix decomposition for image edge detection. The experimental results show that the operator is simple and effective, and better in imaging than other methods.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.765
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 head0.009
GPT teacher head0.274
Teacher spread0.264 · 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