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Record W2957063678 · doi:10.1049/iet-map.2018.5665

Concurrent cardiopulmonary detection using 12/24 GHz harmonic multiport interferometer radar architecture

2019· article· en· W2957063678 on OpenAlexaff
Lydia Chioukh, Tarek Djerafi, Dominic Deslandes, Ke Wu

Bibliographic record

VenueIET Microwaves Antennas & Propagation · 2019
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsPolytechnique MontréalInstitut National de la Recherche ScientifiqueÉcole de Technologie Supérieure
Fundersnot available
KeywordsInterferometryRadarHarmonicElectronic engineeringArchitectureComputer scienceEngineeringElectrical engineeringPhysicsAcousticsOpticsTelecommunicationsGeography

Abstract

fetched live from OpenAlex

A harmonically driven bio‐radar based on the technique of a multiport interferometer (six‐port) technique is proposed and studied in order to improve the performance of vital signs monitoring. The feasibility of estimating cardiopulmonary parametric signatures by deploying a harmonic multiport interferometer radar is presented. This scheme is helpful to reduce the noise floor and the effects of undesirable parasitic harmonics of breathing and intermodulation. A theoretical analysis is developed to explain why a better heart‐beat detection using the proposed harmonic six‐port radar architecture can be achieved. The well‐known null point problem usually encountered in such radar detections of heart‐beating signs is alleviated by the insertion of a fixed 45° phase shift between the two different frequencies in the six‐port discriminator without an actual phase‐shifter. Simulations are carried out to investigate detection accuracy and sensitivity issues for monitoring the vital signs by cancelling the breathing harmonics and intermodulation products. The concept is then validated by the measured results of an experimental prototype using the proposed harmonic six‐port radar platform operating at 12 GHz (fundamental) and 24 GHz (harmonic).

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 categoriesMeta-epidemiology (narrow)
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 score0.091
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
Bibliometrics0.0000.000
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 head0.015
GPT teacher head0.215
Teacher spread0.200 · 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.

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

Citations4
Published2019
Admission routes1
Has abstractyes

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