MétaCan
Menu
Back to cohort
Record W2913930082 · doi:10.1109/wpt.2018.8639488

Ambient RF Energy Harvesting for Dual-Band frequencies below 6 GHz

2018· article· en· W2913930082 on OpenAlexaff
Walid Zahra, Tarek Djerafi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMulti-band deviceSchottky diodeRadio frequencyImpedance matchingVoltageElectrical engineeringEnergy harvestingElectrical impedanceMaterials scienceDiodeOptoelectronicsFrequency bandPhysicsPower (physics)EngineeringAntenna (radio)

Abstract

fetched live from OpenAlex

In order to boost energy that could be exploited, dual or multi-band channels are needed to scavenge and harvest ambient RF energy. In this paper, a dual-band RF energy harvester is designed to operate in dual-band frequencies below 6 GHz. The signal received is harvested using a Schottky diode HSMS 2850. The circuit is optimized for low input power level using a Harmonic balance simulator. At the design phase, different dual-band frequencies are tested to show the flexibility of the proposed topology. Different methods of impedance matching have been investigated. A variety of Schottky diode implementation have been considered. The design is optimized to cover the two bands around 2.45 and 5.2 GHz. in the measurement, a shift in the dual-band to 1.95-4.8 GHz is observed. The two bands have been setup to measure their output voltage are 2-4.8 GHz. At2 GHz a maximum output voltage measured is 1.970 V at 24 dBm of the input power. For 4.8 GHz, a maximum output measured is 1.784 V at 24 dBm.

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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 communication0.0000.000
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 head0.015
GPT teacher head0.215
Teacher spread0.200 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicEnergy Harvesting in Wireless NetworksFrench-language works237,207