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Record W2165644089 · doi:10.1109/ultsym.2006.164

5H-2 A Piezoelectric Membrane Sensor for Biomedical Monitoring

2006· article· en· W2165644089 on OpenAlexafffund
Yuu Ono, Q. Liu, Makiko Kobayashi, C. K. Jen, Alain Blouin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceLead zirconate titanatePiezoelectricityUltrasonic sensorFOIL methodElectrodeOptoelectronicsSubstrate (aquarium)UnimorphComposite materialPiezoelectric sensorAcousticsDielectric

Abstract

fetched live from OpenAlex

A piezoelectric membrane sensor, consisting of a metal foil, a piezoelectric ceramic film and a top electrode, has been developed. Thick lead zirconate titanate composite film was coated onto a stainless steel (SS) foil and a top electrode was formed using a silver paste. The SS foil served as the bottom electrode as well as the substrate. Due to the porosity in the piezoelectric film and the thin metallic membrane substrate, the high flexibility was realized. The porosity was observed by a scanning electron microscopy. This membrane sensor has been worked as a unimorph-type bending sensor as well as an ultrasonic sensor. Biomedical applications using this sensor were demonstrated. The sensor was directly attached onto a wrist and signals corresponding to arterial pulse waves have been successfully obtained. The signals had a signal-to-noise ratio of better than 20 dB. Breathing curves were also measured on a human belly. In addition, ultrasonic signal reflected from a finger bone was observed with a pulse-echo technique. Thus, this sensor could be used as a wearable sensor, which does not disturb daily life activities including sleeping, for real-time and continuous monitoring of personal health conditions

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)0.000
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.012
GPT teacher head0.226
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

Citations18
Published2006
Admission routes2
Has abstractyes

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