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Record W3107175916 · doi:10.1121/1.5146976

CDMA-based multi-domain communications network for marine robots

2020· article· en· W3107175916 on OpenAlexaff
Jay Patel, Mae Seto

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2020
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCode division multiple accessComputer scienceUnderwaterComputer networkUnderwater acoustic communicationProtocol stackNetwork packetBandwidth (computing)FadingThroughputPhysical layerChannel (broadcasting)Real-time computingWireless sensor networkTelecommunicationsWireless

Abstract

fetched live from OpenAlex

A cross-domain communications network for above and below water marine robots, based on code-division multiple access (CDMA), is reported. CDMA is a promising physical layer and multiple access technique for underwater acoustic sensor networks as it: (i) is robust to frequency selective fading, (ii) compensates for multi-path effects at the receiver, and (iii) allows receivers to distinguish amongst signals simultaneously transmitted by multiple devices. Consequently, CDMA increases channel re-use and reduces packet retransmissions, which results in decreased energy consumption and increased network throughput. The proposed CDMA network for autonomous co-ordination and networking is applied to marine robots separated by extended ranges to transmit images/information from underwater to above-water. The work involves a complete communications protocol stack from the physical to the application layer. Simulations of the proposed network were performed with Network Simulator-3 (NS-3). The proposed protocol leverages CDMA properties to achieve multiple access to the scarce underwater bandwidth while previous reported work with underwater channels only consider CDMA for the physical layer encoding. Simulations shows the proposed underwater acoustic network protocol outperforms other existing ones. The next step is preliminary testing in-water.

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: Methods · Consensus signal: none
Teacher disagreement score0.588
Threshold uncertainty score0.236

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 science0.0010.000
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 head0.035
GPT teacher head0.254
Teacher spread0.218 · 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
GenreMethods

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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

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