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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 OpenAlexaff
Kyriaki Niotaki, Nuno Borges Carvalho, Apostolos Georgiadis, Xiaoqiang Gu, Simon Hemour, Ke Wu, Diogo Matos, Daniel Belo, Ricardo Pereira, Ricardo Figueiredo, Henrique Chaves, Bernardo Mendes, Ricardo Correia, Arnaldo S. R. Oliveira, Valentina Palazzi, Federico Alimenti, Paolo Mezzanotte, L. Roselli, Francesca Benassi, Alessandra Costanzo, Diego Masotti, Giacomo Paolini, Aline Eid, Jimmy Hester, Manos M. Tentzeris, Naoki Shinohara

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.011
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

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 designNot applicable
Domainnot available
GenreMethods

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

Citations103
Published2023
Admission routes1
Has abstractyes

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