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Record W2358276195

Software-defined frequency synthesizer for multi-frequency HF surface wave radar

2009· article· en· W2358276195 on OpenAlexaff
Yang Zi-jie

Bibliographic record

VenueSystems engineering and electronics · 2009
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Computational Techniques and Applications
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsWaveformFrequency synthesizerRadarPhase noiseDirect digital synthesizerElectronic engineeringAmplitudeAcousticsFrequency offsetContinuous-wave radarComputer scienceOffset (computer science)Frequency modulationContinuous waveEngineeringPhysicsTelecommunicationsPhase-locked loopOpticsRadio frequencyRadar imagingOrthogonal frequency-division multiplexing
DOInot available

Abstract

fetched live from OpenAlex

A frequency synthesizer based on the waveform database for software defined multi-frequency surface wave sea-state remote sensing radar is presented.It can generate multiple pulsed frequency modulated continuous wave simultaneously or time-dividually,and the waveform,carrier frequency,initial phase and amplitude of which can be controlled independently.The performance of radar systems is improved greatly in the waveform types,amplitude and phase control,and frequency hopping time etc.The hardware and software structure is presented.Experimental results show that the phase noise of the frequency synthesizer is about-112 dBc/Hz at 1 kHz offset,which meets the stability demands of an HF surface wave radar for long coherent integration time.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.243
Teacher spread0.226 · 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 designSimulation or modeling
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

Citations1
Published2009
Admission routes1
Has abstractyes

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