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Record W2728520874 · doi:10.22215/etd/2015-11154

Simulation of Mobile Hydroacoustic Communications in Underwater Acoustic Sensor Networks

2015· dissertation· en· W2728520874 on OpenAlexafffund
Bita Hasannezhad

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaPublic Works and Government Services Canada
KeywordsBit error rateMATLABUnderwaterUnderwater acoustic communicationNoise (video)Spectral densityComputer scienceMobility modelElectronic engineeringAttenuationAcousticsEnergy (signal processing)TelecommunicationsEngineeringGeographyPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

We study the software simulation of underwater acoustic communications with mobility in UWASNs. We simulate the mobility of sensors and their communications, moving according to the Meandering Current Mobility (MCM) model. OMNeT++ and MATLAB are used to model and integrate three protocol layers of UWASNs: network, link, and physical. The effects of noise and attenuation on the underwater communications are considered in the physical layer model. Three major metrics are calculated in this work: Bit Error Rate (BER), distance, and signal energy per bit over noise power spectral density (Eb/N0). Finally, the performance of communicating sensors moving according to the MCM model is evaluated. The simulation results illustrate that the BER for digital data signals is an increasing function of the distance. In addition, the BER for digital data signals is a decreasing function of the Eb/N0 ratio.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score1.000

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 head0.030
GPT teacher head0.291
Teacher spread0.261 · 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.

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
Published2015
Admission routes2
Has abstractyes

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