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Record W2147394558 · doi:10.1109/lawp.2010.2049977

PSO/FDTD Optimization Technique for Designing UWB In-Phase Power Divider for Linear Array Antenna Application

2010· article· en· W2147394558 on OpenAlexaff
Abdolmehdi Dadgarpour, Gholamreza Dadashzadeh, Mohammad Naser‐Moghadasi, Farid Jolani, Bal S. Virdee

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2010
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPower dividers and directional couplersMicrostripGround planeWilkinson power dividerReturn lossParticle swarm optimizationInsertion lossFinite-difference time-domain methodAntenna (radio)Phase (matter)Computer sciencePlanarPower (physics)PhysicsTopology (electrical circuits)Electronic engineeringElectrical engineeringTelecommunicationsOpticsEngineeringAlgorithmFrequency divider

Abstract

fetched live from OpenAlex

This letter presents the design of a compact planar in-phase power divider with ultrawideband (UWB) performance. The device consists of three T-shaped microstrip lines arranged in parallel to each other on one side of the dielectric substrate that are electromagnetically coupled with an H-shaped slot etched on its ground plane. Particle swarm optimization (PSO) and the finite-difference time domain (FDTD) are combined to achieve the optimal power divider design for a given specification. The measured results show the transition of the signal between the T-shaped microstrip line and the ground-plane slot divides power equally, with relatively low insertion loss, good return loss, high stability of phase, and high isolation between the two output ports across the UWB frequency band defined between 3.1-10.6 GHz. The power divider is compact in size, occupying an area of 15 × 32 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . These features make it suitable for linear array antennas.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.808
Threshold uncertainty score0.841

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 head0.008
GPT teacher head0.236
Teacher spread0.227 · 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

Citations18
Published2010
Admission routes1
Has abstractyes

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