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Record W1821125193 · doi:10.1109/aps.2001.959770

Enhanced performance of an aperture-coupled rectangular microstrip antenna on a simplified unipolar compact photonic band gap (UC-PBG) structure

2002· article· en· W1821125193 on OpenAlexaff
Sanjeev Sharma, L. Shafai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
Fundersnot available
KeywordsMaterials sciencePhotonic crystalOptoelectronicsMicrostripMicrostrip antennaDielectricOpticsAperture (computer memory)Patch antennaAntenna (radio)Electrical engineeringPhysicsAcousticsEngineering

Abstract

fetched live from OpenAlex

Recently, the concepts of photonic band gap (PBG) material and unipolar compact photonic band gap (UC-PBG) structure have been proposed by several researchers for improved performance of microstrip antennas and microwave circuits. When etched on high dielectric constant substrate materials they suppress surface waves. The photonic band gap (PBG) substrate normally has periodic air columns micro-machined in the substrate material. However, in the UC-PBG case, a periodic metallic pattern is etched on a grounded dielectric substrate. The metallic pattern consists of square pads separated by capacitive gaps and narrow lines connecting adjacent cells, thereby, creating strong LC coupling. The effect is manifested by a reduced guided wavelength of the propagating modes, and, therefore, reduced lattice period of the UC-PBG pattern. However, the unit cell for creating such a UC-PBG structure seems complex in design and uses more metal or copper area which may increase the cost of production. The authors propose a simpler UC-PBG unit cell design. Its effect on the impedance bandwidth, gain and cross-polarization level performance on an aperture-coupled rectangular microstrip antenna is discussed. The simulation results are reported.

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 score0.476
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)0.0010.000

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 head0.199
Teacher spread0.188 · 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

Citations34
Published2002
Admission routes1
Has abstractyes

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