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Record W3137880587 · doi:10.37394/23204.2020.19.19

Quadrature Sandwich Rectenna for Wireless Power Transfer

2020· article· en· W3137880587 on OpenAlexaff
Rached O. Agwil, Serioja Ovidiu Tatu

Bibliographic record

VenueWSEAS TRANSACTIONS ON COMMUNICATIONS · 2020
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsRectennaWireless power transferWirelessElectrical engineeringRadio frequencyQuadrature (astronomy)Maximum power transfer theoremPower (physics)Electronic engineeringComputer scienceEngineeringTelecommunicationsPhysicsVoltageRectification

Abstract

fetched live from OpenAlex

This novel work presents a quadrature sandwich rectenna circuit for collecting RF from four inputports at different angles. The scheme's high efficiency and increase in output power are significant for theoperation of wireless electronic devices. This work demonstrates the requirements of self-powering.Specifically, simulation efficiency is shown to achieve 77%, and input power is 0 dBm for a 5 KΩ resistanceload and dual-band operation. The results of this experiment are encouraging in terms of wireless powering ofsome devices in wireless telecommunication systems. This scheme can also be applied to radio frequencywireless power transfer and harvesting, and would be useful in industrial, scientific, and medical applications.Furthermore, its mode of operation is sustainable, and it is compact.

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)0.000
metaresearch head score (Gemma)0.000
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.236
Teacher spread0.207 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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