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Record W2821145331 · doi:10.1364/ol.43.003425

3D photoacoustic imaging using Hadamard-bias encoding with a crossed electrode relaxor array

2018· article· en· W2821145331 on OpenAlexafffund
Chris Ceroici, Katherine Latham, Ryan K. W. Chee, Ben Greenlay, Quinn Barber, Jeremy A. Brown, Roger J. Zemp

Bibliographic record

VenueOptics Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsDalhousie UniversityUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchProstate Cancer Canada
KeywordsOpticsHadamard transformEncoding (memory)Materials scienceElectrodePhysicsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

A novel 3D photoacoustic imaging technique is experimentally demonstrated using a 64×64 element bias-sensitive crossed-electrode relaxor array. This technique allows for large 2D arrays to receive across all elements while using minimal channel counts. Hadamard-bias patterns are applied to column electrodes while signals are measured from row electrodes. Photoacoustic signals are measured from a crossed-wire phantom in an intralipid scattering medium. The Hadamard-bias-encoded imaging scheme showed a signal-to-noise (SNR) of 25.3 dB, while the single-column biasing strategy (or identity-matrix-bias pattern) showed a SNR of 8.8 dB.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.818
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.012
GPT teacher head0.211
Teacher spread0.200 · 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.

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

Citations17
Published2018
Admission routes2
Has abstractyes

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