MétaCan
Menu
← Back to cohort

Multifunctional Antennas for Cognitive Radio Applications

2019· article· en· W2950605160 on OpenAlexaff
Chinmoy Saha, Jawad Y. Siddiqui, M.M. Yahia Antar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsCognitive radioWirelessSpurious relationshipSoftware-defined radioComputer scienceComponent (thermodynamics)Key (lock)Electronic engineeringAntenna (radio)EngineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

Evolution, design concept and practical realization of multifunctional antennas (MFA) are systematically reported in this paper. MFAs, traditionally evolved as an intelligent solution to mitigate lower spectrum-utilization using the opportunistic spectrum allocation-policy, are key component for modern wireless applications, such as multi-standard radios, cognitive radios (CR) of software defined radio (SDR) environment. Traditional realizations of MFAs use multiple radiating elements on a common substrate which enhances design constrains in terms of mitigating/reducing parasitic coupling and spurious impacts of the rotary arrangements required for actuating various radiating elements. In this paper, recent research contribution of designing MFAs employing a single UWB radiating element is reported. Improvised feed design with combinatorial loading of SRRs and PIN diodes on a printed monopole antenna, along with controlling their position in the feed region, results into multifunctional operation. Design strategy, techniques, and theoretical ideas for realizing MFAs along with simulated and experimental results are presented in this paper.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.220
Teacher spread0.208 · 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 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
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicAntenna Design and Analysis→French-language works237,207→