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Record W2001329068 · doi:10.1121/1.4778411

High-frequency underwater acoustic communications using FH-FSK signaling in a reverberant shallow water environment

2003· article· en· W2001329068 on OpenAlexaboutno aff
Wen-Bin Yang, T. C. Yang

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2003
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsnot available
Fundersnot available
KeywordsFrequency-shift keyingUnderwater acoustic communicationAcousticsMultipath propagationUnderwaterDoppler effectComputer scienceBandwidth (computing)Bit error rateTelecommunicationsEnergy (signal processing)Decoding methodsPhysicsDemodulationGeologyMathematicsChannel (broadcasting)Statistics

Abstract

fetched live from OpenAlex

This paper describes the experimental results of frequency-hopped frequency-shift-key (FH-FSK) signaling operated at 20 kHz with a 4 kHz bandwidth for underwater acoustic communications in a reverberant shallow water environment. The data were collected during the RDS4 (Rapidly Deployable Systems) experiment in a shallow water (<80 m depth) near Halifax, Canada. The measured impulse response function showed multipaths lasting over a second, which is an order of magnitude longer than the symbol length. Time-varying Doppler shifts of 30–70 Hz were found in the data. The long multipath delay and high Doppler shift are found to have a significant impact on data processing. For example, using conventional processing that detects the symbol energy over the symbol duration, the bit error rates (BER) are of the order 30–40%. Using a longer time window allowing integration of multipath energy and using Doppler estimated from trigger signals, the uncoded BER is reduced to 10–15%. The data are error-free after error decoding using a convolutional code with a rate and constraint length of 9. Consequences for acoustic networking will be discussed. [Work supported by ONR.]

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.698
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.226
Teacher spread0.204 · 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.

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
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicUnderwater Vehicles and Communication SystemsFrench-language works237,207