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Record W2108964633 · doi:10.2528/pierb15012807

MODERN ANTENNA DESIGN USING MODE ANALYSIS TECHNIQUES

2015· article· en· W2108964633 on OpenAlexaff
George Shaker, Safieddin Safavi‐Naeini, Nagula Sangary

Bibliographic record

VenueProgress In Electromagnetics Research B · 2015
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsBlackberry (Canada)DSM (Canada)University of Waterloo
Fundersnot available
KeywordsComputer scienceMode (computer interface)Antenna (radio)TelecommunicationsHuman–computer interaction

Abstract

fetched live from OpenAlex

In this paper, the modal theory of antennas is re-visited, believing that it brings invaluable information towards facilitating the design of multi-feed multi-band antennas.First, some subtle changes are proposed to enhance the applicability of the theory.Next, using some efficient computational techniques, the proposed formulations are shown to predict, to a very high accuracy, the input impedance of any antenna under study.This greatly simplifies the antenna problem and focuses design efforts on finding the appropriate complex resonance frequency to cover a required band.Finding the appropriate feed location is then a matter of extracting the corresponding impedance map for this antenna through simple field manipulations. BACKGROUNDModal analysis among the circuit and filter communities is widely spread.A close look at the design steps of many of the available modern filters reveals the utilization of modal techniques as a key design component [1].Notably, modal analysis of antennas has long been known in the antenna community.Lo and Richards contributed significantly to the advancement of this theory in the late 1970s and early 1980s [2].However, their approach was limited in its accuracy due to the utilization of some theoretical assumptions of the complex resonant frequencies of the antennas they studied.These assumptions imposed significant limitations on the antennas' structures that could be analyzed, along with limitations on the achievable Q values.To date, modal analysis in antenna design has not evolved at the same rate it did in circuit/filter applications.There exists a plurality of work on antenna analysis using modal expansion techniques.The work of Harrington and Mautz is one example [3], and the work of Shen and MacPhie is another [4].Remarkably, the analysis and design of printed antennas is widely documented in numerous research papers and assembled in several books (see for example [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]).In its simplest form, a planar printed antenna is modeled as a transmission line model with its radiating edges treated as slots.A more rigorous analysis came in 1977 by Professor Lo [2] who treated the planar printed antenna as a cavity.His work was amended by multiple refinements to the modal approach for antenna design [14,15].However, the research following his approach essentially diminished since the early 1990s with rare subsequent occurrences in publications.This is primarily due to the limited accuracy of the approach when dealing with practical antenna designs.The limited accuracy was mainly attributed to the approximate calculation of the complex resonant frequency.This means that if such a frequency were calculated to a higher accuracy, then the technique would result in much better results.This will be the main focus of this chapter.Although the transmission-line model is easy to use, it suffers from numerous disadvantages [11].For instance, it is only useful for patches of rectangular shape, the fringe factor must be empirically determined, it ignores field variations along the radiating edge, and it is not adaptable to inclusion of

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.113
GPT teacher head0.383
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), 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".

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Citations6
Published2015
Admission routes1
Has abstractyes

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