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Resolution Enhancement of Sub-Wavelength Imaging Using Magnetic-Dominated Fields

2025· article· W4417132018 on OpenAlexaff
Ghazaleh Tashtarian, Ahad Tavakoli, Abdolali Abdipour, Hamid Akbari–Chelaresi, Omar M. Ramahi

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsLoop antennaAntenna (radio)Dipole antennaDipoleMagnetic dipoleAmplitudeMagnetic fieldElectric fieldMonopole antenna

Abstract

fetched live from OpenAlex

In this study, we designed and evaluated two electrically small antennas: a dipole antenna and a loop antenna for detecting breast tumors in a realistic numerical phantom. This investigation is crucial as these antennas can excite electric and magnetic dipole moments within the tissues. The antennas operated in the low-frequency regime with a matching network. We measured the amplitude of both the electric and magnetic fields generated by these antennas. Our analysis focused on their effectiveness in identifying tumor locations. Our findings reveal the superiority of the electrically small loop antenna and its measured magnetic field over the dipole antenna and electric field in enhancing tumor detection accuracy, as evidenced by the analysis of the positioning error (PE) factor. Additionally, we achieved a resolution of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\lambda / 300$</tex>.

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: none
Teacher disagreement score0.612
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.232
Teacher spread0.224 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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