MétaCan
Menu
Back to cohort
Record W3156315956

Estimation of the Bistatic Echolocation from Underwater Target Using Ship Noise based on Normal-Mode Model

2021· article· en· W3156315956 on OpenAlexaffabout
Mojgan Mirzaei Hotkani, Seyed Alireza Seyedin, Jean‐François Bousquet

Bibliographic record

VenueSignal Processing and Renewable Energy · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsUnderwaterSonarUnderwater acoustic communicationAcousticsBroadbandComputer scienceEstimatorBistatic radarNoise (video)Human echolocationUnderwater acousticsEcho (communications protocol)Mode (computer interface)Sonar signal processingGeologyRadarTelecommunicationsArtificial intelligenceSignal processingPhysicsMathematicsStatisticsOceanographyRadar imaging
DOInot available

Abstract

fetched live from OpenAlex

In this paper, a novel application that uses the broadband noise from a ship-of-opportunity to estimate the scattering from underwater objects is reported. The propagation is based on the normal-mode model. The source localization (location of propeller) is initially realized using incoherent broadband matched-field processing. Then, by utilizing an estimator that relies on Normal-Modes, the target echo below the sea surface is calculated to evaluate the location of the target. The proposed idea is illustrated using simulation and then verified using the acoustic data from a 2019 underwater communication trial in Grand Passage, Nova Scotia in Canada. Experimental results show that the proposed technique can be a reliable signaling method and environmentally friendly that can be applied to the fields of underwater communication and ocean monitoring for a shallow water environment.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.026
GPT teacher head0.253
Teacher spread0.227 · 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 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

Citations1
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueSignal Processing and Renewable EnergySame topicUnderwater Acoustics ResearchFrench-language works237,207