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Record W2291985966 · doi:10.23919/oceans.2015.7404542

Acoustic Doppler compensation using feedforward retiming for underwater coherent transmission

2015· article· en· W2291985966 on OpenAlexaff
Xiao Liu, Jean‐François Bousquet

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsDalhousie University
Fundersnot available
KeywordsComputer scienceFeed forwardDoppler effectElectronic engineeringCarrier frequency offsetRetimingCompensation (psychology)Symbol rateOffset (computer science)Frequency offsetReal-time computingOrthogonal frequency-division multiplexingChannel (broadcasting)TelecommunicationsBit error rateEngineeringAlgorithm

Abstract

fetched live from OpenAlex

This paper presents a Doppler compensation architecture to maintain a reliable communication link at the receiver in highly mobile applications. It can recover both symbol timing and carrier frequency offset introduced by the Doppler effect. The standard feedback timing recovery loop has been modified into a feedforward architecture to achieve fast timing convergence and power efficiency. The timing error is tracked by a Gardner detector through all samples before decimation. Further, a control unit uses the timing error information to dynamically adjust the sampling time as well as the carrier frequency offset such that the Doppler shift is fully compensated.

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 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 score0.901
Threshold uncertainty score0.396

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 head0.101
GPT teacher head0.277
Teacher spread0.176 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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