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Record W3197897069 · doi:10.1109/lsens.2021.3109101

PCA-Assisted Blood Glucose Monitoring Using Metamaterial-Inspired Sensor

2021· article· en· W3197897069 on OpenAlexaff
Ala Eldin Omer, George Shaker, Safieddin Safavi‐Naeini

Bibliographic record

VenueIEEE Sensors Letters · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsImaging phantomSensitivity (control systems)Substrate (aquarium)Principal component analysisMaterials scienceCoupling (piping)DielectricMetamaterialBiomedical engineeringBiological systemPhysicsComputer scienceOptoelectronicsOpticsArtificial intelligenceElectronic engineeringEngineering

Abstract

fetched live from OpenAlex

A metamaterial-inspired sensor is developed for noninvasive blood glucose monitoring. The sensor operating between 3–4 GHz integrates three resonant cells of single split rings with a microstrip line on a 66 × 20 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> dielectric substrate.The proposed design exploits the inter-resonator coupling between adjacent cells to enlarge the sensing zone for more intensive interaction with the glucose tissue. The sensitivity performance for glucose detection is numerically analyzed at different geometrical parameters using a single-pole Debye model to approximate the dispersing behavior of the varying glucose on top of a skin layer. The resulting scattering responses to glucose variations are projected into a low-dimensional space using the principal component analysis algorithm to epitomize the data variances near resonance in fewer variables with a higher spatial resolution.The desired performance of the prototyped sensor is practically validated by measuring synthetic types of blood of 100–300 mg/dL inside a 3-D printed ear phantom using a vector network analyzer with higher sensitivity (∼0.0125 dB/[mg/dL]) than that of a single-cell double split-ring type.

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 score0.010
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 head0.035
GPT teacher head0.236
Teacher spread0.201 · 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

Citations16
Published2021
Admission routes1
Has abstractyes

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