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Record W2006267409 · doi:10.1088/0266-5611/26/5/055011

Two-dimensional near-field microwave holography

2010· article· en· W2006267409 on OpenAlexaff
Maryam Ravan, Reza K. Amineh, Natalia K. Nikolova

Bibliographic record

VenueInverse Problems · 2010
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHolographyMicrowave imagingSampling (signal processing)Range (aeronautics)MathematicsMicrowaveFourier transformNoise (video)Iterative reconstructionOpticsResolution (logic)Image (mathematics)Computer visionArtificial intelligenceComputer sciencePhysicsMathematical analysisTelecommunicationsDetector

Abstract

fetched live from OpenAlex

A new two-dimensional (2D) holographic microwave imaging technique is proposed to reconstruct the 2D image of a target. It is based on the Fourier analysis of the data recorded by two antennas scanning together two separate rectangular parallel apertures on both sides of a target. The complex back-scattered signals of the two antennas are first processed to localize the target in the range direction. Then, the 2D image of the target is reconstructed. No assumptions are made about the incident fields, which can be derived by either simulation or measurement. Both the back-scattered and forward-scattered signals can be used to reconstruct the image of the target. This makes the proposed technique applicable with near-field measurements. To evaluate the proposed technique, the range localization and the 2D image reconstruction of a predetermined simulated target are examined. Associated resolution limits, sampling constraints and the impact of noise are also discussed.

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)0.000
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0010.001
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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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

Citations53
Published2010
Admission routes1
Has abstractyes

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