MétaCan
Menu
Back to cohort
Record W1514930950 · doi:10.1109/ipps.1996.508137

Parallel synthetic aperture radar processing on workstation networks

2002· article· en· W1514930950 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.

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

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced SAR Imaging Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSynthetic aperture radarComputer scienceWorkstationInverse synthetic aperture radarRadarRadar signal processingRadar imagingParallel processingSide looking airborne radarComputer graphics (images)Computer hardwareSignal processingArtificial intelligencePulse-Doppler radarTelecommunicationsOperating system

Abstract

fetched live from OpenAlex

Synthetic aperture radar (SAR) signal processing poses a significant challenge due to its very large computation and data storage requirements. This paper presents the computational requirements of a typical high resolution satellite SAR data processing scenario. A classification of approaches to partitioning the SAR problem for parallel processing is given. The suitability of networks of workstations (NOW) for SAR processing is analyzed for a number of partitioning approaches. The network throughputs required to support SAR processing on NOW are derived. SAR processing is found to demand extremely high network throughput that is difficult to achieve with today's technology.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.843
Threshold uncertainty score0.469

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 communication0.0000.000
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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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

Quick stats

Citations23
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced SAR Imaging TechniquesFrench-language works237,207