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Record W2056519443 · doi:10.1109/ccece.2008.4564544

Six-port FMCW collision avoidance radar sensor configurations

2008· article· en· W2056519443 on OpenAlexaff
Bouraima Boukari, E. Moldovan, Sofiène Affes, Ke Wu, R.G. Bosisio, Serioja Ovidiu Tatu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsPolytechnique MontréalInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsDiscriminatorRadarCollision avoidanceContinuous-wave radarOffset (computer science)Radar engineering detailsComputer scienceAmplitudePort (circuit theory)CollisionPulse-Doppler radarAcousticsElectronic engineeringEngineeringRadar imagingPhysicsTelecommunicationsOpticsDetector

Abstract

fetched live from OpenAlex

This paper presents two six-port 77 GHz FMCW collision avoidance radar sensor configurations. The first one uses a six-port double balanced mixer for direct frequency conversion and conventional frequency counting to determine the range and velocity of the target. The second one uses a six-port phase/frequency discriminator. Here velocity and range are determined in the same manner by evaluating the beat frequencies which are the slope of the instantaneous phase of the beat signals. Unlike other six-port collision avoidance sensors, measurement data processing techniques are developed to overcome the problems related to the DC current offset and amplitude imbalance, avoiding the need of six-port calibration. Computer simulation shows very good results for both configurations.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.024
GPT teacher head0.213
Teacher spread0.189 · 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

Citations11
Published2008
Admission routes1
Has abstractyes

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