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Low-Cost Methodology for Estimation of Breast Shape using Microwave Signals

2025· article· en· W4414165107 on OpenAlexfundno aff
Daniela M. Godinho, Afonso Simões, Inês A. Correia, Bruno Mendes, Gonçalo Canastra, Joana Rajão‐Saraiva, Rodrigo Dias, Raquel C. Conceição

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaResearch Executive AgencyMinisterio de Economía y CompetitividadEuropean CommissionMcGill University
KeywordsMicrowave imagingAntenna (radio)MicrowaveSimple (philosophy)Estimation theoryEstimation

Abstract

fetched live from OpenAlex

The retrieval of the body shape can be crucial ahead of medical Microwave Imaging (MWI) reconstruction.Current state-of-the-art methods provide accurate estimations of the body shape but they require additional hardware and may be computationally intensive and time-consuming.In this paper, we propose a simple method using the original measurements of an MWI acquisition, and we assess the performance of the method under different conditions.The results show the estimation accuracy varies with the complexity of the shape under examination, antenna configuration and data source.The estimated median errors for some antenna positions were as low as 0.4 mm.An optimised estimation was obtained by fine-tuning parameters for each simulated or experimental scenario.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.045
GPT teacher head0.307
Teacher spread0.262 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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