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Record W4362588637 · doi:10.1109/jmw.2023.3255581

RF Energy Harvesting and Wireless Power Transfer for Energy Autonomous Wireless Devices and RFIDs

2023· article· en· W4362588637 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Journal of Microwaves · 2023
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsMcGill UniversityPolytechnique Montréal
Fundersnot available
KeywordsWirelessEnergy harvestingWireless power transferEnergy transferPower (physics)Energy (signal processing)Electrical engineeringComputer scienceTelecommunicationsEngineeringPhysicsEngineering physics

Abstract

fetched live from OpenAlex

Radio frequency (RF) energy harvesting and wireless power transmission (WPT) technologies —both near-field and far-field—have attracted significant interest for wireless applications and RFID systems. We already utilize near-field WPT products in our life and it is expected that RF EH and far-field WPT systems can drive the future low-power wireless systems. In this article, we initially present a brief historical overview of these technologies. The main technical challenges of rectennas and WPT transmitters are discussed. Furthermore, this paper presents the recent advances on the development of these technologies, including the possibility of powering RFID systems through the millimeter wave power from 5G networks, the trends in flexible rectennas design and the technological developments on the simultaneous wireless information and power transfer (SWIPT).

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.222
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.210
Teacher spread0.199 · 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