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Record W2108016697 · doi:10.1109/tap.2004.838802

Design and analysis of a combination antenna with rectangular dielectric resonator and inverted L-plate

2005· article· en· W2108016697 on OpenAlexaff
K. Lan, S.K. Chaudhuri, S. Safavi‐Naeini

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2005
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDielectric resonator antennaDielectricOpticsAntenna (radio)Radiation patternDielectric resonatorMaterials scienceResonatorPhysicsTelecommunicationsOptoelectronicsComputer science

Abstract

fetched live from OpenAlex

A wide-band compact antenna combining a rectangular dielectric resonator (DR) with a grounded and inverted L-plate is proposed. Two different impedance matching circuits, a vertical T-shaped strip attached to one side of the DR and a microstrip stub terminated into a T-branch, are introduced to enhance the impedance matching and to economize the space utilization by the matching circuits. A two-step procedure is applied to design the radiation element and the feed structure of the antenna. Approximate formulas are used to obtain initial estimation of the antenna dimensions. Then finite-difference time-domain method (FDTD) with the help of fast Fourier transform/Pade/spl acute/ method is used for fine-tuning these dimensions to obtain satisfactory resonant frequency and quality factor. Next, the feed structure is designed and adjusted to obtain the optimal impedance matching. Since the radiation element and the feed structure are designed separately, the design process is simplified and both their performances are optimized within the frame of design restrictions. Two antennas, for 2.4 and 5.8 GHz Bluetooth and wireless local-area network applications, are designed as examples. The -10 dB bandwidths between 210 and 240 MHz are obtained. The antenna gains are 1.0 and 1.5 dBi, 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.010
GPT teacher head0.200
Teacher spread0.190 · 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
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

Citations16
Published2005
Admission routes1
Has abstractyes

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