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An Adaptive Acoustic Transceiver Testbed on System-on-Chip for Underwater Communication

2024· article· en· W4404688814 on OpenAlexaff
Boris Bam Nges, Ananya Bhattacharya, Hunter Alloway, Jean‐François Bousquet

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTestbedTransceiverUnderwater acoustic communicationUnderwaterComputer scienceChipCommunications systemEmbedded systemElectronic engineeringComputer architectureTelecommunicationsComputer networkEngineeringWirelessGeology

Abstract

fetched live from OpenAlex

This paper describes a test setup to support the development of a bi-directional acoustic communication link relying on adaptive transceivers. The physical layer relies on the JANUS protocol for discovery of new nodes and assessment of the channel conditions. The transmitter has a bandwidth of 5 kHz at a center frequency of 27.5 kHz, while the receiver relies on broadband hydrophones. The embedded system allows to transmit in diverse underwater scenarios, and the System-on-Chip is programmed to transmit various waveforms including through coded Orthogonal Frequency Division Multiplexing (OFDM). Key elements of the program are described to maintain real-time operation on the embedded systems. At the receiver, the data is sampled at 96 kHz and a 4 Mbps cabled link to a host interface is developed to diagnose the communication link reliability.

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)0.001
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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.029
GPT teacher head0.250
Teacher spread0.220 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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