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Record W2199482109

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

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

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.

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 score0.003
Threshold uncertainty score0.011

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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

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

Citations4
Published2010
Admission routes1
Has abstractyes

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