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Record W2043029696 · doi:10.1109/map.2014.6931659

Systematic Approach to Estimating Monocycle Pulse for Time-Domain Studies of UWB Antennas Using Numerical Computations and Simulation Tools

2014· article· en· W2043029696 on OpenAlexaff
Debarati Ganguly, Debatosh Guha, Sanghamitro Das, Ashish Rojatkar

Bibliographic record

VenueIEEE Antennas and Propagation Magazine · 2014
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTime domainAntenna (radio)Pulse (music)Computer scienceElectronic engineeringUltra-widebandDipole antennaPulse durationDuration (music)ComputationDomain (mathematical analysis)AcousticsAlgorithmEngineeringTelecommunicationsPhysicsOpticsMathematics

Abstract

fetched live from OpenAlex

This article describes a new technique for determining an appropriate input pulse and its duration, which are important for time-domain studies of ultra-wideband (UWB) antennas. For such studies, the researchers normally prefer EM simulation tools, and use different types of input pulses. The basis of the choice of input pulse is a bit obscure in most of the reports appearing in the open literature. Antenna researchers indeed face a problem in having suitable guidelines for choosing the input pulse, a mathematical approach to generate it, and in estimating the pulse's duration. The width or duration of the input pulse cannot be arbitrary. This plays a significant role in determining the reliable time-domain response of the antenna in both transmitting and receiving modes. This is successfully addressed in this paper, taking a UWB monopole as the antenna under test. The proposed technique should accurately guide and help a researcher to perform the time-domain characterization of a UWB monopole/dipole antenna using mathematical tools and EM simulators commercially available.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.765
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.041
GPT teacher head0.289
Teacher spread0.247 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations11
Published2014
Admission routes1
Has abstractyes

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