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Record W2107082650 · doi:10.1109/tap.2011.2109686

Reconfigurable Axial-Mode Helix Antennas Using Shape Memory Alloys

2011· article· en· W2107082650 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsHelical antennaHelix (gastropod)Antenna (radio)ActuatorConductorReconfigurable antennaShape-memory alloyAcousticsComputer scienceMaterials sciencePhysicsGeometryDipole antennaElectrical engineeringTelecommunicationsEngineeringMathematicsAntenna factorAntenna efficiencyGeology

Abstract

fetched live from OpenAlex

Reconfigurable structures based on smart materials offer a potential solution to realize adaptive antennas for emerging communication devices. In this paper, a reconfigurable axial mode helix antenna is studied. A shape memory alloy spring actuator is used to adjust the height of a helix antenna. With the total length of the helix wire fixed, the pitch spacing and pitch angle are varied as the height is varied. This in turn can alter the antenna pattern in order to adjust to altered operating conditions. In order to undertake the design, the Kraus equations for the axial mode helix are compared with simulation results, and their range of applicability is clarified. It is shown that based on these equations, antenna gain variation is possible by varying the height of the antenna, while keeping its conductor length fixed. We then show that a pattern can be reconfigured using a two-helix structure. Finally, a proof-of-concept helix antenna is implemented using a shape memory alloy actuator. Measurement results confirm that the pattern can reconfigure while maintaining a reasonable impedance match.

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.

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 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 score0.692
Threshold uncertainty score0.723

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 head0.034
GPT teacher head0.236
Teacher spread0.202 · 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