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Record W4289535814 · doi:10.1109/tim.2022.3195265

Design and Performance Analysis of a Picosecond Pulse Generator

2022· article· en· W4289535814 on OpenAlexaff
Aaron D. Pitcher, Charl W. Baard, Natalia K. Nikolova

Bibliographic record

VenueIEEE Transactions on Instrumentation and Measurement · 2022
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsMcMaster University
FundersDefence and Security AcceleratorSaudi Pharmaceutical Society
KeywordsJitterPulse generatorWaveformBandwidth (computing)WidebandArbitrary waveform generatorElectronic engineeringUltra-widebandComputer scienceElectrical engineeringVoltageEngineeringTelecommunications

Abstract

fetched live from OpenAlex

A picosecond pulse generator is designed to generate a stable differentiated Gaussian (monocycle) waveform. The design approach to increasing the center frequency, bandwidth and peak-to-peak voltage, as compared to previously reported ultra-wideband (UWB) generators, is described. The 280 ps wide pulse achieves a 1:10 fractional bandwidth (FBW) ratio extending from 500 MHz to beyond 5 GHz at the -10 dB level. A measurement procedure is proposed for evaluating the jitter and noise performance of UWB pulse generators, and it is applied to the fabricated prototype. The procedure exploits jitter and noise definitions from high-speed digital electronics, which are adapted here for the jitter and noise evaluation of UWB pulse generators at microwave frequencies. The problems in obtaining the absolute and relative jitter of a UWB generator are discussed along with proposed solutions. The impact of the input trigger on the pulse stability is demonstrated through the dramatic improvement achieved by the integration of a jitter cleaner in the generator’s circuit.

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)0.001
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0040.001

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 head0.030
GPT teacher head0.216
Teacher spread0.187 · 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

Citations14
Published2022
Admission routes1
Has abstractyes

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