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Record W2897449994 · doi:10.1109/map.2018.2859200

Cellular Wireless Energy Harvesting for Smart Contact Lens Applications [Education Corner]

2018· article· en· W2897449994 on OpenAlexaff
Luyao Chen, Ben Milligan, Tianming Qu, Luxsumi Jeevananthan, George Shaker, Safieddin Safavi‐Naeini

Bibliographic record

VenueIEEE Antennas and Propagation Magazine · 2018
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMicroelectronicsEnergy harvestingElectrical engineeringRectifier (neural networks)WirelessLens (geology)Contact lensRadio frequencyEnergy (signal processing)Power (physics)Printed circuit boardEngineeringMaterials scienceComputer scienceTelecommunicationsOpticsPhysics

Abstract

fetched live from OpenAlex

An energy harvester for a smart contact lens that monitors the glucose level of a user has been developed and demonstrated. The energy harvester captures a smartphone's second-generation (2G) cellular emission and rectifies it into dc power to operate on-lens microelectronics for glucose detection and wireless data transmission. The energy harvester can reach a maximum radio frequency (RF)-to-dc power conversion efficiency of 47%. An electrically realistic human eye model was designed and fabricated using three-dimensional (3-D) printing technologies to assist in various measurements of the proposed energy harvester. For accessibility and ease of measurements, the proof-of-concept rectifier for the harvester has been designed on a 10 mm ? 30 mm ? 0.8 mm two-layer FR4 printed circuit board (PCB). The end design demonstrates that an energy harvester on a smart contact lens is able to produce 1 mW of dc power at 2.1 V via cellular emission from a smartphone placed 18 cm away.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.876
Threshold uncertainty score0.894

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 head0.014
GPT teacher head0.223
Teacher spread0.208 · 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.

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

Citations19
Published2018
Admission routes1
Has abstractyes

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