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Record W2008064418 · doi:10.1121/1.3479550

Locating an acoustic point source scattered by a skull phantom via time reversal matched filtering

2010· article· en· W2008064418 on OpenAlexaff
Jeffrey M. Sadler, Kiyanoosh Shapoori, Eugene Malyarenko, F. Severin, Roman Gr. Maev

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2010
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Windsor
FundersOffice of Naval Research
KeywordsImaging phantomAcousticsRefractionTransducerOpticsMaterials sciencePhysics

Abstract

fetched live from OpenAlex

This paper examines the utilization of the time reversal matched filtering method to resolve the location of an acoustic point source beneath a skull phantom (variable thickness layer), without the removal of this layer. This acoustical process is examined experimentally in a water tank immersion system containing an acoustic source, a custom-made skull phantom, and a receiving transducer in a pitch-catch arrangement. The phantom is designed to approximately model the acoustic properties of an average human skull bone (minus the diploe layer), while the variable thickness of the phantom introduces a variable time delay to the acoustic wave, relative to its entry point on the phantom. This variable delay is measured and corrected for, and a matched filtering time reversed process is used to determine the location of the point source. The results of the experiment are examined for various positions of the acoustic source behind the phantom and compared to the reference cases with no phantom present. The average distance between these two cases is found to be 4.36 mm, and within the expected deviation in results due to not accounting for the effects of refraction.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.886
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.004
GPT teacher head0.205
Teacher spread0.201 · 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.

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

Citations6
Published2010
Admission routes1
Has abstractyes

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