MétaCan
Menu
Back to cohort
Record W2167604939 · doi:10.1109/tmtt.2007.914363

24-GHz Frequency-Modulation Continuous-Wave Radar Front-End System-on-Substrate

2008· article· en· W2167604939 on OpenAlexafffund
Zhaolong Li, Ke Wu

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2008
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsPolytechnique Montréal
FundersNanjing University of Science and TechnologySoutheast UniversityCanada Research ChairsRoyal Society of Canada
KeywordsMicrostripPlanarFront and back endsRadarElectronic engineeringMicrowaveElectronic circuitWaveguideModulation (music)Antenna (radio)EngineeringIntegrated circuitRF front endElectrical engineeringAcousticsComputer scienceTelecommunicationsMaterials scienceOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Design and implementation of a 24-GHz frequency-modulation continuous-wave radar front-end system is presented and discussed, and its hybrid planar and waveguide building blocks are fully integrated on one single substrate. A flexible and compact integration methodology on the basis of the substrate-integrated-circuits concept is deployed to design such a microwave front-end system-on-substrate. In this study, it is found that this surface-volume hybrid integration scheme not only enables the complete system integration of planar and nonplanar microwave circuits, but also combines respective advantages of such structures in connection with microstrip lines (planar) and waveguides (nonplanar). Design strategies of the system building blocks including mixers, power dividers, and antenna arrays are discussed together along with the measured results. To verify the developed radar prototype, laboratory-based target-range measurements are conducted.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.198
Teacher spread0.186 · 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

Citations80
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Microwave Theory and TechniquesSame topicMicrowave Engineering and WaveguidesFrench-language works237,207