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Record W2911050299 · doi:10.1017/s1759078718001630

Investigation on the effects of resistive loading on wrapped bow-tie antennas

2019· article· en· W2911050299 on OpenAlexaff
Doojin Lee, George Shaker, William Melek

Bibliographic record

VenueInternational Journal of Microwave and Wireless Technologies · 2019
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBalunReflection coefficientResistive touchscreenAntenna (radio)AcousticsBlumlein PairWaveformDirectional antennaMaterials scienceOpticsHorn antennaDipole antennaElectrical impedanceSlot antennaPhysicsElectrical engineeringVoltagePulse generatorEngineering

Abstract

fetched live from OpenAlex

Abstract The pulse radiating characteristic of a wrapped bow-tie antenna (WBA) and wrapped resistively loaded bow-tie antenna (WRLBA) is presented for impulse radar applications in this paper. The numerical analysis of the WRLBA is performed by comparing that of the WBA. The wrapped antennas are realized on a flexible substrate. The antennas are fed by an impedance tapered balun, which has an overall transmission loss of −1.4 dB over the balun length. The characteristics of the resistive loading to the wrapped antenna, such as reflection coefficient, reflected pulse in the time domain, voltage standing wave ratio, and input impedance, are experimentally investigated and compared with simulated results. The fidelity factor of the radiated electric field on the boresight direction for the WBA and WRLBA is calculated as 0.82 and 0.96, respectively. The wireless communication ability is evaluated by the transmission coefficient, group delay, boresight gain, and received waveform. The calculated fidelity factor of the received waveform for the WBA and WRLBA is 0.79 and 0.85, respectively. The average and variations of the group delay of both wrapped antennas are observed to be around 2.5 ns and less than 1.5 ns, respectively.

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 score0.008
Threshold uncertainty score0.284

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 head0.011
GPT teacher head0.205
Teacher spread0.194 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

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