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Record W2082997085 · doi:10.1063/1.3505113

Signal-to-noise analysis of biomedical photoacoustic measurements in time and frequency domains

2010· article· en· W2082997085 on OpenAlexafffund
Sergey A. Telenkov, Andreas Mandelis

Bibliographic record

VenueReview of Scientific Instruments · 2010
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChirpWaveformFrequency modulationTime domainOpticsModulation (music)Sensitivity (control systems)SIGNAL (programming language)Signal-to-noise ratio (imaging)Noise (video)Materials scienceFrequency domainAcousticsPhotoacoustic effectTransducerPhotoacoustic imaging in biomedicinePhysicsLaserTelecommunicationsComputer scienceRadio frequencyRadarElectronic engineering

Abstract

fetched live from OpenAlex

Sensitivity analysis of photoacoustic measurements is conducted using estimates of the signal-to-noise ratio (SNR) achieved under two different modes of optical excitation. The standard pulsed time-domain photoacoustic imaging is compared to the frequency-domain counterpart with a modulated optical source. The feasibility of high-SNR continuous wave depth-resolved photoacoustics with frequency-swept (chirp) modulation pattern has been demonstrated. Utilization of chirped modulation waveforms achieves dramatic SNR increase of the periodic signals and preserves axial resolution comparable to the time-domain method. Estimates of the signal-to-noise ratio were obtained using typical parameters of piezoelectric transducers and optical properties of tissue.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.242
Teacher spread0.231 · 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 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

Citations82
Published2010
Admission routes2
Has abstractyes

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