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Record W2731887528 · doi:10.1109/imbioc.2017.7965779

Examination of the phase and log magnitude measurement projections and the implications for tomographic image reconstruction behavior

2017· article· en· W2731887528 on OpenAlexfundno aff
Paul M. Meaney, Samar Hosseinzadegan, Andreas Fhager, Mikael Persson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsnot available
FundersNational Institutes of HealthFondation Chalmers
KeywordsMagnitude (astronomy)Convergence (economics)Tomographic reconstructionIterative reconstructionTomographyPhase (matter)InverseInverse problemInverse scattering problemMicrowave imagingAntenna (radio)ScatteringPerspective (graphical)AlgorithmOpticsPhysicsComputer scienceMathematicsArtificial intelligenceMathematical analysisMicrowaveGeometryAstrophysicsTelecommunications

Abstract

fetched live from OpenAlex

We examine the log magnitude and phase projections of simple circular objects in a 2D microwave tomography setting. Because of the inherent insight visible with these log transformed quantities, we can explore the forward and associated inverse scattering characteristics. For these situations, it is clear that the reconstructions for the cases where the target properties are higher than that of the background take more iterations and the convergence behavior is more uneven. Interestingly this corresponds to the situation where the composite pattern from the antenna/target combination produces a crudely focused beam with corresponding side nulls and lobes. We speculate that these features are somewhat confounding and ultimately prove more challenging for the reconstruction algorithm than for when the target properties are less than the background.

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)0.011
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.030
GPT teacher head0.271
Teacher spread0.241 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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