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Record W2081603286 · doi:10.1109/iscas.2013.6572333

An ultra-low-power monitoring system for inductively coupled biomedical implants

2013· article· en· W2081603286 on OpenAlexaff
Kamyar Keikhosravy, Pouya Kamalinejad, Shahriar Mirabbasi, Kenichi Takahata, Victor C. M. Leung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRectifier (neural networks)Context (archaeology)Power (physics)CMOSComputer scienceVoltageElectrical engineeringElectronic engineeringEngineeringEmbedded systemPhysics

Abstract

fetched live from OpenAlex

In this paper, an ultra-low-power system for wireless monitoring of inductively coupled biomedical implants is presented. The system is fully integrated and composed of custom rectifier, alignment and monitoring circuits with enhanced performance. The proposed system is described in the context of a smart-stent system that monitors the re-narrowing of blood vessels at the smart-stent site. The building blocks of the system are designed and simulated in a 0.13-μm CMOS technology. Simulation results for the monitoring system show that the proposed rectifier provides 53% power conversion efficiency (PCE) for -10.36 dBm input power (in the alignment mode) and 62% PCE for -4.06 dBm input power (in the monitoring mode). The alignment unit is capable of operating by drawing a 12 μA from a supply voltage as low as 0.6 V and the monitoring circuit consumes as low as 176 μW.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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

Citations8
Published2013
Admission routes1
Has abstractyes

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