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Record W2027827032 · doi:10.1049/iet-map.2010.0515

Novel-integrated patch antennas with multi-band characteristics

2011· article· en· W2027827032 on OpenAlexaff
Kashish Jhamb, Lei Li, Karumudi Rambabu

Bibliographic record

VenueIET Microwaves Antennas & Propagation · 2011
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGround planeWiMAXMicrostripWi-FiFEKORadiation patternReturn lossHFSSReflector (photography)MicrowaveMicrostrip antennaWirelessSlot antennaElectrical engineeringAntenna (radio)PhysicsElectronic engineeringEngineeringAcousticsOpticsTelecommunicationsWireless network

Abstract

fetched live from OpenAlex

In this study, two integrated multi-band microstrip patch antennas have been proposed for the next-generation wireless systems. The first design consists of a right isosceles triangular patch integrated with a pair of monopoles. The patch resonates at 3.5 GHz (WiMAX, worldwide interoperability for microwave access) and the monopoles at 2.45 GHz (WLAN, wireless local area network) and 1.96 GHz (PCS frequency), respectively. Furthermore, to improve the gain profile and to achieve directive radiation characteristics towards zenith, a reflector ground plane has been placed below the patch. The detailed analysis of the pattern synthesis technique has been explained. Similarly, the second proposed design consists of a semi-circular patch resonating at 3.5 GHz integrated with a slot and a monopole. The slot and monopole resonate at 5.5 GHz (WiMAX) and 2.45 GHz, respectively. Both the designs are realised on a 1.58 mm-thick FR4 substrate (dielectric constant=4.4) and have a return loss better than 10 dB with desired radiation characteristics at the specified resonant frequencies. Prototypes of the proposed designs have been fabricated and their radiation characteristics have been measured. A close agreement has been found between simulation and measurements.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.654
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
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.201
Teacher spread0.178 · 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

Citations33
Published2011
Admission routes1
Has abstractyes

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