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Record W2108461130 · doi:10.2528/pier12091008

EXPERIMENTAL DEMONSTRATION OF PULSE SHAPING FOR TIME-DOMAIN MICROWAVE BREAST IMAGING

2013· article· en· W2108461130 on OpenAlexafffund
Adam Santorelli, M. Chudzik, Evgeny Kirshin, Emily Porter, Aintzane Lujambio, Israel Arnedo, Milica Popović, Joshua D. Schwartz

Bibliographic record

VenueElectromagnetic waves · 2013
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsMcGill University
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaMinisterio de Ciencia e InnovaciónMcGill University
KeywordsMicrowave imagingComputer scienceTime domainMicrowavePulse (music)BroadbandImpulse (physics)Breast imagingElectronic engineeringAcousticsComputer visionPhysicsMammographyDetectorTelecommunicationsBreast cancerEngineeringMedicine

Abstract

fetched live from OpenAlex

Abstract—We experimentally demonstrate a low-cost hardware technique for synthesizing a specific electromagnetic pulse shape to improve a time-domain microwave breast imaging system. A synthesized broadband reflector (SBR) filter structure is used to reshape a generic impulse to create an ad-hoc pulse with a specifically chosen frequency spectrum that improves the detection and imaging capabilities of our experimental system. The tailored pulse shape benefits the system by improving the level of signal detection after transmission through the breast and thus permits higher-resolution images. We report on our ability to use this technique to detect the presence of tumours in realistic breast phantoms composed of varying quantities of glandular tissue. Additionally, we provide a set of images based on this experimental data that demonstrates the increased effectiveness of the system using the SBR-shaped pulse in the localisation and identification of the embedded tumour. 1.

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: 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
Metaresearch0.0000.001
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 integrity0.0010.000
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 head0.005
GPT teacher head0.199
Teacher spread0.194 · 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

Citations35
Published2013
Admission routes2
Has abstractyes

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