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Record W2031992884 · doi:10.1088/0967-3334/33/5/831

Electrical impedance tomography system based on active electrodes

2012· article· en· W2031992884 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

VenuePhysiological Measurement · 2012
Typearticle
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsCarleton University
Fundersnot available
KeywordsElectrical impedance tomographyElectronic engineeringAmplifierElectrical impedanceElectrodeInstrumentation (computer programming)Electrical engineeringConvertersField-programmable gate arrayEngineeringVoltageComputer scienceComputer hardwareCMOSPhysics

Abstract

fetched live from OpenAlex

Electrical impedance tomography (EIT) can image the distribution of ventilated lung tissue, and is thus a promising technology to help monitor patient breathing to help selection of mechanical ventilation parameters. Two key difficulties in EIT instrumentation make such monitoring difficult: (1) EIT data quality depends on good electrode contact and is sensitive to changes in contact quality, and (2) EIT electrodes are difficult and time consuming to place on patients. This paper presents the design and initial tests of an active electrode-based system to address these difficulties. Our active electrode EIT system incorporates an active electrode belt, a central voltage-driven current source, central analog to digital converters and digital to analog converters, a central FPGA-based demodulator and controller. The electrode belt is designed incorporating 32 active electrodes, each of which contains the electronic amplifiers, switches and associated logic. Tests show stable device performance with a convenient ease of use and good imaging ability in volunteer tests.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score0.995

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 integrity0.0000.000
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 head0.029
GPT teacher head0.213
Teacher spread0.183 · 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