MétaCan
Menu
Back to cohort

Antenna Array for Wireless Power Transfer to Deep-Brain Implants

2022· article· en· W4296558889 on OpenAlexaff
Mohammad Abdolrazzaghi, Roman Genov, George V. Eleftheriades

Bibliographic record

Venue2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI) · 2022
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsCanada Research ChairsUniversity of Toronto
Fundersnot available
KeywordsWireless power transferWirelessBattery (electricity)Maximum power transfer theoremAntenna (radio)Electrical engineeringPower (physics)MicrowaveElectronic circuitComputer scienceTransmission (telecommunications)Antenna arrayElectronic engineeringTransceiverPhase (matter)Data transmissionMicrowave transmissionEngineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

Implantable medical devices, enabled by continually reducing the size of integrated circuits, offer therapeutic solutions to many intreatable disorders. Yet, the large battery size and short lifetime remain a major challenge. In this work, we present a microwave transmitting array (4×4) operating at 2.1 GHz that focuses propagating fields inside the brain tissue. This mode of transmission enables reaching deep in the brain. Analytical derivation of the efficiency is used to conjugate-match the phase of TX elements. A high power transfer efficiency of 0.91% is achieved when a 2×2 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> implanted power receiver is located 5 cm away from the TX array.

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 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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.797
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.239
Teacher spread0.228 · 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 teacher head, not a consensus.

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

Citations7
Published2022
Admission routes1
Has abstractyes

Explore more

Same venue2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI)Same topicEnergy Harvesting in Wireless NetworksFrench-language works237,207