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Microwave Characterization and Probe Sensing: Parametric Study with Skin Phantom Thickness

2022· article· en· W4282842229 on OpenAlexafffund
Jasmine Boparai, Yanis Jallouli, Oliver Miller, Rachel Tchinov, Milica Popović

Bibliographic record

Venue2022 IEEE MTT-S International Microwave Biomedical Conference (IMBioC) · 2022
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDielectricImaging phantomMicrowaveMaterials scienceCharacterization (materials science)Biomedical engineeringMicrowave imagingParametric statisticsHuman skinSkin effectMillimeterOptoelectronicsOpticsComputer scienceNanotechnologyElectrical engineeringPhysicsMedicineEngineering

Abstract

fetched live from OpenAlex

Novel microwave and millimeter-wave devices have been recently investigated as promising diagnostic aids for skin anomaly detection. These devices rely on knowledge of skin dielectric parameters. Skin thickness varies with body location, and, hence, the goal of our work is to characterize the dielectric skin properties in the microwave range as a function of skin thickness. To do so systematically, and in a controlled laboratory environment, we developed skin phantoms models ranging from 0.5 mm to 5 mm in thickness, with 0.5 mm increments. These phantoms are placed on a fat-mimicking material and then characterized with the Keysight slim form probe over the 0.5–2.6 GHz range. Our results indicate that skin thickness, usually known from extensive anatomical resources, should be taken into account for proper interpretation of eventual dielectric characterization in vivo, as the probe's sensing volume is likely to include a complex, multi-tissue dielectric distribution.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.228
Teacher spread0.215 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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