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Data Communication Over a Novel Capacitive Resonant Wireless Power Transmission System

2019· article· en· W3015833403 on OpenAlexaff
Semion Belau, Susanna Vital de Campos de, Fabiano Cezar Domingos, Rashid Mirzavand, Pedram Mousavi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNarrowbandBit error rateComputer scienceData transmissionElectronic engineeringAdditive white Gaussian noiseChannel (broadcasting)WirelessCommunications systemTransmission (telecommunications)Capacitive couplingPower (physics)Electrical engineeringEngineeringTelecommunicationsComputer hardwarePhysics

Abstract

fetched live from OpenAlex

This paper presents the performance of a novel capacitive resonant wireless power transmission (WPT) system as a communication link. To test the system's ability to transmit data, a channel model of the implemented system was created in MATLAB, along with a non-coherent receiver model, and used in a simulation to obtain the bit error rate by passing 10,000 on-off keyed symbols through it. Because the system utilizes resonant coupling, which is detrimental to the ability to communicate data due to the inherent narrowband nature of resonance, both maximum power transfer and maximum data rate cannot be achieved simultaneously. As a result, modified Miller encoding (which is used in RFID) was used in the simulation to encode the data and the data rate was slowed down to improve the bit error rate. Both simulations have shown that the WPT system has comparable performance to an additive white Gaussian noise channel given an appropriate data rate.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.792

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 head0.017
GPT teacher head0.221
Teacher spread0.203 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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