MétaCan
Menu
Back to cohort

Unified Integration of Self-Oscillating Mixer-Antenna for Compact Receiver Frontend

2021· article· en· W3128668162 on OpenAlexaff
Srinaga Nikhil Nallandhigal, Ke Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMicrostripAntenna (radio)Radio frequencyImpedance matchingOscillation (cell signaling)Electrical impedancePhysicsSIGNAL (programming language)Patch antennaElectrical engineeringElectronic engineeringAcousticsComputer scienceEngineeringOptics

Abstract

fetched live from OpenAlex

A method of integration for unifying active device and receiving antenna to realize a low-loss and compact single-ended self-oscillating mixer-antenna (SOMA) frontend is proposed in this work, through an experimental prototype demonstrated over X-band. Stability analysis of the active device is performed first, and a shorted microstrip line is then used as a source feedback to destabilize it. Subsequently, rectangular patch antenna impedance behavior is studied, and eventually designed to satisfy the oscillation requirement at LO frequency of 10.8 GHz, and also to exhibit stability and good radiation performance at RF frequency of 10 GHz. Dual band microstrip matching network is designed for simultaneously satisfying the oscillation condition at LO and the impedance matching at IF for measurements. It is observed experimentally that the oscillation occurs at LO frequency of 10.86 GHz. Subsequently, a 10 GHz RF input signal is fed, which results in a self-oscillating mixer configuration with an IF signal of 0.86 GHz at the output. From measurements, the proposed SOMA exhibits a conversion gain of 10 dB, and input power 1-dB compression point of -10 dBm.

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 score0.001
Threshold uncertainty score0.003

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.024
GPT teacher head0.236
Teacher spread0.212 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicAntenna Design and AnalysisFrench-language works237,207