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Record W4409311144 · doi:10.1109/jerm.2025.3555198

Employing Surface Waves for Characterizing Skin: Experimental Validation

2025· article· en· W4409311144 on OpenAlexafffund
Shangyang Shang, Milad Mokhtari, Milica Popović

Bibliographic record

VenueIEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology · 2025
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSurface (topology)Materials scienceEnvironmental scienceComputer scienceGeologyMathematicsGeometry

Abstract

fetched live from OpenAlex

Microwave technology has recently been explored as a non-invasive method for skin cancer diagnosis. While most research has focused on reflection signal analysis, transmission-based approaches remain under-explored. In this work, surface wave transmissions generated by two antennas are employed to characterize skin cancer in a non-invasive way. We refined the theoretical model proposed in our previous work, showing that the transmission coefficient ($S_{21}$) amplitude and phase shift can effectively indicate both the presence and the size of skin tumors. Controlled experiments were conducted to validate the theory, using oil-gelatin-based phantoms to mimic both healthy skin and malignant tissue with varying tumor sizes. The experimental results strongly support the theoretical predictions and align with the simulation outcomes from our previous study.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.359
Teacher spread0.316 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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