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Record W1966215464 · doi:10.1364/boe.5.002785

Thermally enhanced signal strength and SNR improvement of photoacoustic radar module

2014· article· en· W1966215464 on OpenAlexafffund
Wei Wang, Andreas Mandelis

Bibliographic record

VenueBiomedical Optics Express · 2014
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of Toronto
FundersSamsungNatural Sciences and Engineering Research Council of CanadaSamsung Advanced Institute of Technology
KeywordsPhotoacoustic imaging in biomedicineMaterials scienceOpticsSIGNAL (programming language)Signal-to-noise ratio (imaging)Photoacoustic spectroscopyRadarPhysics

Abstract

fetched live from OpenAlex

A thermally enhanced method for improving photoacoustic imaging depth and signal-to-noise (SNR) ratio is presented in this paper. Experimental results showed that the maximum imaging depth increased by 20% through raising the temperature of absorbing biotissues (ex-vivo beef muscle) uniformly from 37 to 43°C, and the SNR was increased by 8%. The parameters making up the Gruneisen constant were investigated experimentally and theoretically. The studies showed that the Gruneisen constant of biotissues increases with temperature, and the results were found to be consistent with the photoacousitc radar theory.

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 categoriesnone
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 score0.530
Threshold uncertainty score0.646

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)0.0000.000

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 head0.005
GPT teacher head0.196
Teacher spread0.191 · 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 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

Citations9
Published2014
Admission routes2
Has abstractyes

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