MétaCan
Menu
Back to cohort
Record W1962171353

Development and Test of a Millimetre-Wave ISAR Images ATR System

2005· article· en· W1962171353 on OpenAlexaboutno aff

Bibliographic record

VenueDefense Technical Information Center (DTIC) · 2005
Typearticle
Languageen
FieldEngineering
TopicAdvanced SAR Imaging Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsArtificial neural networkConfusion matrixComputer scienceMATLABArtificial intelligenceConfusionTest dataInverse synthetic aperture radarMatrix (chemical analysis)Pattern recognition (psychology)Radar imagingRadarTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

The Defense R &D Canada - Valcartier is developing an efficient ATR algorithm capable of classifying millimetre-wave images using a back-propagation neural network. This neural network was developed and tested using the MATRIX 2005 ISAR data bank featuring the signatures of T-72, BMP-2, T-62 and the ZSU-23-4. The neural network was developed with MATLAB Toolbox Neural Network 3.02 and the results are compared with the HNet3 ATR system. Specialized M files were developed to evaluate the performance of the neural network providing the confusion matrix and the ROC curves.

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 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: Empirical · Consensus signal: none
Teacher disagreement score0.798
Threshold uncertainty score0.733

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 head0.013
GPT teacher head0.220
Teacher spread0.207 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations0
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueDefense Technical Information Center (DTIC)Same topicAdvanced SAR Imaging TechniquesFrench-language works237,207