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Performances Comparison of Shottky Voltage Doubler Rectifier to support RF Energy Harvesting

2020· article· en· W3032933246 on OpenAlexaff
Alex Mouapi, Nadir Hakem, Nahi Kandil

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsVoltage doublerRectennaRectifier (neural networks)Electrical engineeringVoltageEnergy conversion efficiencyElectronic circuitFigure of meritElectronic engineeringRadio frequencyEnergy harvestingEfficient energy useEngineeringVoltage dividerComputer scienceEnergy (signal processing)PhysicsOptoelectronicsDropout voltageRectification

Abstract

fetched live from OpenAlex

Electromagnetic waves are increasingly the preferred source for charging the battery of wireless sensor nodes. Given the small amount of harvestable energy, much work has been proposed to optimize the energy efficiency of radiofrequency energy converter circuits. Among these researches, Voltage Doubler (VD) rectifier occupies a predominant place. This work then proposes to compare the performance of the two types of VD that are the Greinacher Voltage Doubler (GVD) and the Delon Voltage Doubler (DVD). Performance criteria are DC output voltage, RF/DC conversion efficiency, and matching losses. A Rectenna Figure of Merit (RFoM) that considers these three parameters is defined for an overall comparison of the rectifiers. The analyzes are carried out in the 900 MHz, 2.45 GHz ISM and 5.8 GHz frequency bands and the performances of the two circuits when they are perfectly adapted are also analyzed. As a result, it is obtained that the DVD rectifier is more suitable for miniature designs without a matching filter. The two voltage doublers when they are matched have almost similar performances in terms of DC voltage and RF/DC conversion efficiency. However, the insertion losses are higher in the GVD.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.380
Threshold uncertainty score0.879

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 head0.040
GPT teacher head0.256
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 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

Citations12
Published2020
Admission routes1
Has abstractyes

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