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A Receiver Array to Reduce the Effects of Misalignment in Magneto-Inductive Communication

2024· article· en· W4404688494 on OpenAlexaff
Parisa Rangraz, Hailey Dowling, Jean‐François Bousquet

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMagnetoComputer scienceElectrical engineeringRadio frequencyAcousticsElectronic engineeringTelecommunicationsPhysicsEngineeringElectromagnetic coil

Abstract

fetched live from OpenAlex

In this paper, a magneto-inductive (MI) communication link is designed to cross the air-water interface for a transmitter that is 20 meters below the surface. Simulations are used to identify the geometry of the coils, as well as an optimal transmit frequency on the order of 1 kHz, constrained on a relatively low transmit current of one A RMS. At this frequency, it is shown that the capacity is equal to 5,820 bps, due to the extremely high SNR, over 50 dB. The performance varies significantly with the distance, because the MI link operates in the near field and the bandwidth is reduced because of the conductivity of seawater. The use of an array is demonstrated to improve the SNR with and without misalignment, and a wideband sensor array is proposed to develop a low-noise link receiver. In fact, a 3-element coil array is proposed, and it is expected that the link will be able to achieve an SNR well above 0 dB for a depth of 20 meters.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.241

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 head0.006
GPT teacher head0.223
Teacher spread0.216 · 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 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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