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Record W2103809390 · doi:10.1109/aps.2006.1711005

Antenna optimization for RF mote communications

2006· article· en· W2103809390 on OpenAlexaff
R.H. Johnston, I.G. Finvers, J.W. Haslett

Bibliographic record

Venue2006 IEEE Antennas and Propagation Society International Symposium · 2006
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAntenna (radio)Impedance matchingTransceiverLimitingElectrical impedanceAntenna tunerRadio frequencyElectronic engineeringWirelessComputer scienceWireless sensor networkElectrical engineeringMatching (statistics)Antenna efficiencyEngineeringTelecommunicationsDipole antennaComputer networkMathematics

Abstract

fetched live from OpenAlex

Compact RF transceivers with integrated sensors (called "motes") are used in wireless sensor networks to monitor such things as patient vital signs, environmental parameters and manufacturing processes. One of the limiting factors for mote size and efficiency is the antenna. This paper examines, in both the transmitting and receiving modes, the impedance matching for the antenna used on a commercially available mote and shows that significant improvements in efficiency can be made through improved antenna impedance matching

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score0.721

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 head0.012
GPT teacher head0.232
Teacher spread0.220 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations2
Published2006
Admission routes1
Has abstractyes

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