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Scattering of Ultrasound

2006· book-chapter· en· W4388390564 on OpenAlexaff
R.S.C. Cobbold

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSuperposition principleScatteringPhysicsPlane waveHarmonicBoundary value problemPlane (geometry)OpticsRayleigh scatteringAcousticsMathematical analysisMathematicsGeometryQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract Because of the complex structure of most biological media, the scattered signal arising from an incident pulsed ultrasound beam is generally difficult to fully interpret, even when details of the structure are available. To address this problem, it is helpful to start by assuming a plane harmonic wave incident on a simple symmetrical scattering structure. Now any incident harmonic wave can be treated as a superposition of plane harmonic waves, and any pulse can be transformed into a spectrum of frequencies, so that a solution to the plane harmonic wave scattering problem enables a more general problem to be solved. Many methods of solution have been used, all of which are based on either exact or approximate solution of the wave equation. The boundary value method, initially used by Rayleigh [1,2] to obtain approximate solutions for spherical and cylindrical scatterers by acoustic waves, will first be studied. Small spherical scatterers are frequently used to model the structure of soft biological media [3], and in addition a simple spherical scatterer or a smalldiameter wire is sometimes used to measure the pulse-echo response of an ultrasound transducer. Moreover, an array of such objects can be used to determine the performance of an imaging system. A Green’s function approach can be used to arrive at an integral solution for the scattered wave, and this is especially helpful when the density and compressibility of the scattering region vary in a continuous manner. Since the integrand involves the sum of the incident and scattered fields, it is generally appropriate to make the Born approximation in which the scattered field is assumed to be small compared to that incident.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.003

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 head0.007
GPT teacher head0.176
Teacher spread0.169 · 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 designTheoretical or conceptual
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

Citations3
Published2006
Admission routes1
Has abstractyes

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