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Record W2041851313 · doi:10.1109/oceans.2003.178223

Improving arrays for multi-angle swath bathymetry

2003· article· en· W2041851313 on OpenAlexaff
John Bird, S. Asadov, P. Kraeutner

Bibliographic record

VenueOceans 2003. Celebrating the Past ... Teaming Toward the Future (IEEE Cat. No.03CH37492) · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBathymetryTransducerSonarMultipath propagationAngle of arrivalAcousticsCrosstalkBandwidth (computing)Computer scienceGeologyOpticsPhysicsTelecommunicationsAntenna (radio)

Abstract

fetched live from OpenAlex

Multi-angle swath bathymetry sonar systems use the range and estimates of the angle of signal arrival to determine bottom depth. The accuracy of angle of arrival estimates is influenced by a number of factors including noise levels, the presence of multipath arrivals, signal bandwidth, pulse length etc. Angle estimation accuracy is also affected by the integrity of the transducer array employed to receive the returning signals. This paper addresses some of the issues associated with transducer design for multi-angle swath bathymetry systems including inter-element crosstalk and housing interactions. Beampatterns and relative angle responses are presented for transducer arrays that show evidence of crosstalk and housing interactions. It is shown that the crosstalk and housing interactions can be reduced through the use of different inter-element materials and housing configurations. Beampatterns and relative angle responses are presented for an improved array and compared to the previous patterns and phase responses. The discussion and experimental results are limited to arrays constructed with piezo-ceramic bars resonant at 300 kHz, and intended for shallow water use.

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)0.003
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.025
GPT teacher head0.247
Teacher spread0.222 · 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

Citations7
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueOceans 2003. Celebrating the Past ... Teaming Toward the Future (IEEE Cat. No.03CH37492)Same topicUnderwater Acoustics ResearchFrench-language works237,207