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Multi-Stage Rectifier Enabled Battery-Free Sensor Platform Utilizing Ambient RF Energy

2021· article· en· W4212958138 on OpenAlexaff
Xiaoqiang Gu, Jorge Virgilio de Almeida, Ke Wu

Bibliographic record

Venue2021 IEEE MTT-S International Microwave and RF Conference (IMARC) · 2021
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsRectifier (neural networks)Battery (electricity)Electrical engineeringStage (stratigraphy)Radio frequencyEnergy harvestingEnergy (signal processing)Materials scienceOptoelectronicsComputer scienceEngineeringPower (physics)Physics

Abstract

fetched live from OpenAlex

Ambient radiofrequency (RF) energy harvester scavenges RF power “wastes” and converts them into usable dc output. Considering the limited output of an ambient RF energy harvesting device, this scheme is highly suitable for low-power and low-duty-cycle wireless sensing applications. This work presents a battery-fiee multi-function sensor platform based on a multi-stage rectifier utilizing ambient RF energy. An analytical model with equivalent circuits is developed to analyze and optimize multistage rectifiers. Guided by the theoretical analysis, a 5-stage rectifier is realized. A final experimental demonstration shows that the multi-function sensor platform can sustain itself with the power supply from the 5 -stage rectifier harvesting ambient RF energy at a practical power level.

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 score0.007

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 communication0.0000.001
Open science0.0010.000
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 head0.041
GPT teacher head0.249
Teacher spread0.208 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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