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Record W2090036820 · doi:10.1109/isspit.2013.6781908

A frequency domain MVDR beamformer for UWB microwave breast cancer imaging in dispersive mediums

2013· article· en· W2090036820 on OpenAlexaff
Forough Arabshahi, Sadaf Monajemi, Hamid Sheikhzadeh, Kaamran Raahemifar, Reza Faraji‐Dana

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMicrowave imagingClutterFrequency domainComputer scienceWidebandImage qualityMicrowaveRadio frequencyAcousticsElectronic engineeringOpticsImage (mathematics)PhysicsRadarTelecommunicationsComputer visionEngineering

Abstract

fetched live from OpenAlex

In this paper a new imaging technique for early stage ultra wideband (UWB) microwave breast cancer detection is proposed. A circular array of antennas illuminates the breast tissue with UWB pulses and the backscattered signals are then passed through a beamformer designed and applied in frequency domain. This design enables the beamformer to compensate for non-integer delays and frequency dependent dispersion and at the same time increases the accuracy of the beamformer. It is shown that the proposed imaging algorithm reduces the computational cost and memory of the imaging system by decreasing the sampling rate to the Nyquist rate and significantly reducing the number of required matrix inversions. Furthermore, the proposed algorithm significantly improves the quality of the obtained image and on average the signal-to-clutter ratio of the image is increased by 89.29% compared to other cases.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · 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
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.004
GPT teacher head0.203
Teacher spread0.199 · 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 designSimulation or modeling
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

Citations8
Published2013
Admission routes1
Has abstractyes

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