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Record W2137146836 · doi:10.1109/mwsym.2005.1517141

A subharmonic self-oscillating mixer using substrate integrated waveguide cavity for millimeter-wave application

2005· article· en· W2137146836 on OpenAlexaff
Jijun Xu, Ke Wu

Bibliographic record

VenueIEEE MTT-S International Microwave Symposium Digest, 2005. · 2005
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsHarmonic mixerResonatorMonolithic microwave integrated circuitAmplifierExtremely high frequencyPhase noiseLocal oscillatorFrequency mixerElectrical engineeringPlanarGain compressionOptoelectronicsRadio frequencyAcousticsElectronic engineeringMaterials sciencePhysicsEngineeringOpticsComputer scienceCMOS

Abstract

fetched live from OpenAlex

A low-cost, compact, subharmonic self-oscillating mixer integrated with antenna is presented and demonstrated at 30GHz. This novel configuration makes use of substrate integrated waveguide (SIW) cavity as a resonator in the feedback loop to stabilize the fundamental oscillating frequency. This allows the possibility of building a complete planar receiver, with improved phase noise, as an integrated front-end for millimeter wave systems such as radar and wireless sensor network. It is also an attractive structure for MMIC design. A convenient method free from FET amplifier design is proposed in the work for this kind of self-oscillating mixer, which can also be applied in SIW oscillator design. The circuit, implemented as a down-converter, exhibits an average conversion loss of 8.6dB, and an IF phase noise of -86dBc/Hz at 100-kHz offset. The effects of DC variation on the oscillating frequency and the output P/sub 1dB/ gain compression are measured and demonstrated.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.237
Teacher spread0.219 · 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

Citations31
Published2005
Admission routes1
Has abstractyes

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