MétaCan
Menu
Back to cohort
Record W2164872505 · doi:10.1109/chinasip.2013.6625395

The spatial shift operations on image reconstruction from 2D-FRFT information with application to SAR moving target detection

2013· article· en· W2164872505 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMathematics
TopicMathematical Analysis and Transform Methods
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaCanada Research Chairs
KeywordsSynthetic aperture radarFractional Fourier transformComputer scienceComputer visionArtificial intelligenceInvariant (physics)Fourier transformIterative reconstructionInverse synthetic aperture radarImage (mathematics)Frequency domainRadar imagingAlgorithmPattern recognition (psychology)RadarMathematicsFourier analysisTelecommunications

Abstract

fetched live from OpenAlex

In this paper, the property of spatial shift operation on image reconstruction from amplitude and phase information in two-dimensional Fractional Fourier Transform (2D-FRFT) is studied through mathematical analysis and computer simulations. From the analysis presented in this paper, the phase information is spatial shift-invariant for image reconstruction in the 2D-FRFT domain while the amplitude information is not. Based on the analysis, we proposed a novel method to detect moving targets in the synthetic aperture radar (SAR) images. The effectiveness of the proposed solution is demonstrated through experiments.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.819
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.264
Teacher spread0.254 · 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