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Record W2958005240 · doi:10.1109/tap.2019.2927824

Improvement of Multi-Frequency Microwave Breast Imaging through Frequency Cycling and Tissue-Dependent Mapping

2019· article· en· W2958005240 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaDepartment of Biotechnology, Ministry of Science and Technology, IndiaUniversity of Calgary
KeywordsMicrowaveMicrowave imagingRadio frequencyAcousticsMaterials scienceOpticsComputer scienceTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

Two modifications of the Contrast Source Inversion (CSI) algorithm have been employed to improve resulting 2-D microwave images of the dielectric properties of synthetic breast models produced using a well-established frequency-hopping technique. First, while reconstructions from low-frequency data are often used as initial guesses for otherwise unstable higher-frequency inversions, an improvement in overall image quality can be observed when the imaginary part of the reconstructed low-frequency image is altered to reflect the identical tissue geometry as its real part when used as an initial guess during subsequent reconstructions. Second, in such frequency-hopping scenarios, rather than terminating reconstructions following inversions of the highest frequency data, “frequency cycling” (i.e., returning to the lowest frequency in the sequence) can be used to further improve reconstructions without loss of resolution or anatomical detail.

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.

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 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: none
Teacher disagreement score0.565
Threshold uncertainty score0.749

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 head0.010
GPT teacher head0.217
Teacher spread0.207 · 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