MétaCan
Menu
Back to cohort
Record W2982642488 · doi:10.1109/mwscas.2019.8884978

A 2-stage, 50Ω RF-DC Charge-pump with Load Lines for high RF-DC Voltage Conversion

2019· article· en· W2982642488 on OpenAlexaff
Sichong Li, Fadhel M. Ghannouchi, Rushi Vyas

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsImpedance matchingElectrical engineeringOutput impedanceVoltageCharge pumpElectrical impedanceDiodeRadio frequencyOptoelectronicsMaterials sciencePhysicsEngineeringCapacitor

Abstract

fetched live from OpenAlex

A novel 2-stage RF-DC charge-pump circuit (RFCP) with a 50 ohm input impedance and higher RF-DC voltage conversion without an external input-matching network or output DC-filter for ambient wireless energy-harvesting (WEH) in the 2.4 GHz band is presented. The novelty of this RFCP is the use of near quarter-wavelength transmission lines on the load-side of each half-wave rectifying stage in the RFCP to induce a standing wave maxima at each of the diode inputs at 2.4 GHz. Doing so minimizes diode losses and maximize RF-DC voltage conversion of the charge-pump while still achieving a near 50 ohm input impedance. Simulations and measurements show the proposed design generating a higher DC output of 2.12V and energy efficiency of 18% with just -6dBm of RF input; an RF sensitivity of -7.2dBm for 1.8V output; and a 50Ω input impedance (reflection coefficient = -11dB) in the 2.4 GHz band.

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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.009
GPT teacher head0.200
Teacher spread0.192 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicEnergy Harvesting in Wireless NetworksFrench-language works237,207