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Record W2560387648 · doi:10.1109/epec.2016.7771757

Efficient rectenna with wide dynamic input power range for 900 MHz wireless power transfer applications

2016· article· en· W2560387648 on OpenAlexaff
A. M. Almohaimeed, M.C.E. Yagoub, Rony E. Amaya

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsCarleton UniversityUniversity of Ottawa
FundersQassim University
KeywordsRectennaRectifier (neural networks)Electrical engineeringWireless power transferAntenna (radio)Power (physics)RF power amplifierElectronic engineeringMaximum power transfer theoremVoltageRadio frequencyDirectivityComputer scienceEngineeringPhysicsCMOSAmplifierRectification

Abstract

fetched live from OpenAlex

This work demonstrates the design of a rectenna to operate over wide dynamic input power range. It utilizes an adaptive reconfigurable rectifier to overcome the issue of early breakdown voltage in conventional rectifiers. A depletion-mode field-effect transistor has been introduced to operate as a switch and compensate at low and high input power levels for the rectifier. In addition, a meandered monopole antenna has been exploited to collect RF energy. The rectifier design achieves 40% of RF-DC power conversion efficiency over a wide dynamic input power range from −17 dBm to 27 dBm and the antenna exhibits a directivity of 1.92 dBi as well as a return loss of −33 dB. The rectenna is designed to operate in the 900 MHz ISM band and suitable for Wireless Power Transfer (WPT) applications.

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 score0.002
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)0.0020.001

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 head0.005
GPT teacher head0.193
Teacher spread0.188 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

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