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Record W2381192871

Target echo signal feature extraction and material recognition of ultra-wideband ground-penetrating radar

2009· article· en· W2381192871 on OpenAlexaff
Wei Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsComputer scienceGround-penetrating radarEcho (communications protocol)RadarSIGNAL (programming language)Feature extractionWidebandAcousticsArtificial intelligenceWaveletBroadbandFeature (linguistics)Remote sensingPattern recognition (psychology)GeologyTelecommunicationsElectronic engineeringPhysicsEngineering
DOInot available

Abstract

fetched live from OpenAlex

Based on the mechanism of radar signal transmission, the model of ultra -wideband ground -penetrating radar (UWB GPR) broadband echo was established, which revealed the effect of multi-frequency component on target echo signal feature extrac- tion and material recognition. Because of abundant information in the signal echo of UWB GPR, signature extraction become the key of automatic target recognition. In this paper, the wideband correlation processing which is realized by wavelet transform was employed to filter and extract the typical data. The shape of target was recognized by extraction the longitudinal and transverse typ- ical data, and the composition of target is classificated by the power spectrum of the echo.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.286

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 head0.012
GPT teacher head0.245
Teacher spread0.233 · 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 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

Citations0
Published2009
Admission routes1
Has abstractyes

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