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Record W2199482109

Introducing Real-Time On-Board SAR Image Generation using an Optronic SAR Processor

2010· article· en· W2199482109 on OpenAlex
Linda Marchese, Michel Doucet, Bernd Harnisch, Martin Süess, Pascal Bourqui, Nichola Desnoyers, Ludovic Guillot, François Châteauneuf, Alain Bergeron

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSynthetic Aperture Radar (EUSAR), 2010 8th European Conference on · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced SAR Imaging Techniques
Canadian institutionsInstitut National d'Optique
Fundersnot available
KeywordsComputer scienceSynthetic aperture radarImage processingComputer visionArtificial intelligenceImage processorReal-time computingComputer hardwareImage (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

This paper introduces a compact optronic processor prototype that has the capability to instantaneously generate SAR images. This real-time processor is light weight, low power consuming and small in size, the design being specifically targeted for on-board SAR image processing. SAR images are typically processed electronically applying dedicated Fourier transformations. This may be performed optically at the speed of light, however. The optronic processor architecture provides inherent parallel computing capabilities for fast SAR processing. Indeed, SAR images have been generated from ENVISAT / ASAR raw data. A review of the design of the optronic processor prototype and an analysis of the SAR images it produces are presented.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.020
GPT teacher head0.255
Teacher spread0.236 · 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