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Record W2163398991 · doi:10.1109/biocas.2009.5372092

Resonance-based wireless power delivery for implantable devices

2009· article· en· W2163398991 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsElectromagnetic coilMaximum power transfer theoremCoupling (piping)Wireless power transferInductive couplingMaterials sciencePower (physics)Resonance (particle physics)Q factorElectrical engineeringNuclear magnetic resonanceOptoelectronicsEngineeringPhysicsResonator

Abstract

fetched live from OpenAlex

Due to the limited life time of batteries, biomedical implants typically use inductive coupling to transfer power to the implantable device. Inductive coupling of source and load coils suffers from low efficiency due to the low coupling between the coils. The low coupling limits the maximum transferable power and operating range of the system. Using a resonance-based coupling technique, the adverse effect of low coupling between source and load coils is in part compensated by the high quality factor of the coils. Unlike its two-coil counterpart, in the presented four-coil energy transfer system the efficiency profile of the power transfer is not a monotonically decreasing function of the distance between the coils and can be optimized to provide a maxima at a relatively large operating distance. Furthermore, as compared to conventional systems, resonance-based system show more than 2× efficiency improvement over an increased operating range.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.009
GPT teacher head0.205
Teacher spread0.196 · 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

Quick stats

Citations57
Published2009
Admission routes1
Has abstractyes

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