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Real-time continuous active sonar processing

2015· article· en· W1536769779 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsSonarComputer scienceSoftwareMarine mammals and sonarSea trialReal-time computingData processingUnderwaterOn boardSignal processingComputer hardwareArtificial intelligenceEngineeringMarine engineeringRemote sensingDigital signal processingGeologyOperating system

Abstract

fetched live from OpenAlex

This work describes the development of continuous active sonar (CAS) processing at CMRE. The software uses subband processing to achieve a faster update rate than is possible with pulsed active sonar (PAS). The software development was based on CMRE's PAS processing software, CAINPro, which has been thoroughly tested during previous sea trials and in postprocessing data analysis. Computational efficiency was carefully considered and many optimizations were made so that the software can run in real-time using the constrained computing resources on board CMRE's Ocean Explorer autonomous underwater vehicles (AUV). The software was successfully tested during the REP14 Atlantic sea trial in July 2014, and was able to demonstrate real-time detection of an echo repeater on all nine sub-bands that were processed. The CAS algorithm runs in real-time on the processing board installed on the AUV.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.637
Threshold uncertainty score0.433

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 head0.024
GPT teacher head0.231
Teacher spread0.207 · 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