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Record W2100998208 · doi:10.1109/icassp.2007.367072

Through the Wall Imaging using Chaotic Modulated Ultra Wideband Synthetic Aperture Radar

2007· article· en· W2100998208 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
TopicGeophysical Methods and Applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSynthetic aperture radarRadarComputer scienceRadar imagingAutocorrelationUltra-widebandChaoticInverse synthetic aperture radarReflection (computer programming)Continuous-wave radarElectronic engineeringEnergy (signal processing)WidebandRemote sensingArtificial intelligenceTelecommunicationsEngineeringGeologyPhysicsMathematics

Abstract

fetched live from OpenAlex

A novel chaotic modulated ultra wideband (UWB) synthetic aperture radar (SAR) imaging scheme and its post data processing technique are presented for through the wall surveillance applications. It is illustrated that the proposed radar has an excellent resolution performance due to the autocorrelation properties of chaotic signals. Through modeling room reverberation and target reflection, and deriving energy distribution at the receiver, detection performance is carried out to show the advantages of proposed radar. Electromagnetic (EM) simulations are performed in a through the wall scenario. Compared with the conventional UWB radar, the result verifies the effectiveness and superiority of the proposed technique.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.866
Threshold uncertainty score0.340

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 head0.015
GPT teacher head0.257
Teacher spread0.242 · 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

Citations5
Published2007
Admission routes1
Has abstractyes

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