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Record W2896686925 · doi:10.32508/stdj.v18i3.840

Design and simulation of pulse generator for UWB based on LC-tank differential oscillators topology

2015· article· en· W2896686925 on OpenAlexaff
Nhan Chi Nguyen, Nghĩa Hoai Duong, Anh Dinh

Bibliographic record

VenueScience and Technology Development Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPulse generatorPulse (music)TransmitterUltra-widebandTopology (electrical circuits)NMOS logicPulse-width modulationPower (physics)Generator (circuit theory)Electrical engineeringCMOSLC circuitPhysicsElectronic engineeringMaterials scienceEngineeringChannel (broadcasting)CapacitorTransistorVoltage

Abstract

fetched live from OpenAlex

This paper presents a detailed analysis, design and simulation of pulse generator for Ultra-Wideband (UWB) based on LC-tank differential oscillators topology. The differential oscillators with a cross-coupled NMOS pair and a tail current source are used to achieve more positive gain and generate negative resistance to the LC-tank. Besides, this oscillator is suitable for UWB high frequency and low power applications. The UWB pulse generator is composed of a simple on-off keying (OOK) modulated and LC-tank differential oscillators. The circuit of UWB pulse generator designed and simulated in 0.13 um CMOS technology. The UWB pulse generator generates a pulse for the 6 - 10 GHz UWB transmitter. Simulation results show a pulse width of 586 ps, a peak to peak amplitude pulse of 88.6 mV from the 1.2 V power supply and the die area of 0.22 mm2. The average power consumption of approximately 0.55 mW and an energy consumption of 1.1 pJ/pulse at 500 MHz pulse repetition rate (PRR) are observed.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.012

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)0.0030.000

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 head0.027
GPT teacher head0.248
Teacher spread0.222 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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