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Record W2594659568

Radiation pattern enhancement of microstrip patch antennas using a 2D photonic band gap structure

2002· article· en· W2594659568 on OpenAlexaff
M. Fallah‐Rad, L. Shafai

Bibliographic record

VenueInternational Symposium on Antenna Technology and Applied Electromagnetics · 2002
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPhotonic crystalGround planeMicrostrip antennaMicrostripRadiation patternPatch antennaOpticsMaterials scienceAntenna (radio)DielectricMethod of moments (probability theory)RadiationRadiation propertiesOptoelectronicsPhysicsAcousticsTelecommunicationsComputer scienceMathematics
DOInot available

Abstract

fetched live from OpenAlex

Recently, there has been extensive research on the photonic band gap structures (PBG's) and their applications in the microstrip antennas and transmission lines. The PBG structures have the unique property of preventing the propagation of EM waves in certain directions for certain frequencies. The properties of these structures are defined by the shape, size, symmetry and, the constituent material. These periodic structures come in many shapes and forms. Some of these include drilled holes in the dielectric, patterns etched in the ground plane, and metallic patches placed around microstrip structures. The PBG structure studied in this paper consists of metallic patches with center vias connecting the patches to the ground plane. The properties of this 2D structure are studied using the Ansoft Ensemble simulator which is based on the method of moments (MOM). Two microstrip patch antenna using the PBG structure are designed and the effects of the PBG on the gain and radiation pattern are shown. It is observed that the radiation pattern of the patch antennas can be controlled by changing the parameters of the surrounding PBG structure.

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 categoriesMeta-epidemiology (narrow)
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 score0.036
Threshold uncertainty score1.000

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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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.

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

Citations0
Published2002
Admission routes1
Has abstractyes

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