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Record W1978273850 · doi:10.1109/tie.2013.2242656

Energy Harvesting for In-Ear Devices Using Ear Canal Dynamic Motion

2013· article· en· W1978273850 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

VenueIEEE Transactions on Industrial Electronics · 2013
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMicropowerEnergy harvestingEar canalEnergy (signal processing)AcousticsClosing (real estate)Generator (circuit theory)Mechanical energyComputer sciencePower (physics)EngineeringPhysics

Abstract

fetched live from OpenAlex

In this paper, we study the possibility of using energy harvesting from ear canal dynamic motion as a source of power to replace the use of batteries for in-ear devices. Two hand-made micropower generators capable of scavenging energy from ear canal deformation are presented in this paper: 1) a hydroelectromagnetic energy harvester and 2) a flexible piezoelectric generator. The experimental results show that 3.3 mJ of energy per mouth opening and closing cycle is available from ear canal dynamic motion. If we consider that possibly thousands of such cycles occur daily, ear canal dynamic motion could prove to be a likely source of energy for in-ear applications.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.567
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.240
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