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Record W2783855097 · doi:10.1364/oe.26.000990

Time-bandwidth compression of microwave signals

2018· article· en· W2783855097 on OpenAlexaff
Ming Li, Shuqian Sun, Bo Li, Hossein Asghari, Ye Deng, Wei Li, Ninghua Zhu

Bibliographic record

VenueOptics Express · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNational Natural Science Foundation of China
KeywordsMicrowavePulse compressionBandwidth (computing)Computer scienceSignal processingOpticsNanosecondElectronic engineeringDigital signal processingMaterials scienceRadarComputer hardwareTelecommunicationsPhysicsEngineeringLaser

Abstract

fetched live from OpenAlex

We report and demonstrate a reconfigurable photonic anamorphic stretch transform to realize time-bandwidth product (TBP) compression for microwave signals. A time-spectrum convolution system is employed to provide an ultra-high nonlinear dispersion up to several nanoseconds per gigahertz, which is required for processing nanosecond-long microwave signals. The group delay of the system can be engineered easily by programming a WaveShaper. Based on the proposed scheme, the TBP of a double pulse microwave signal is compressed by 1.9 times. Our proposal can provide a more efficient way to sample, digitize and store high-speed microwave signals, opening up entirely new perspectives for generation of many critical microwave signal processing modules.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.435
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.253
Teacher spread0.237 · 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 teacher head, 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

Citations0
Published2018
Admission routes1
Has abstractyes

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