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Record W2112533667 · doi:10.1109/aps.2010.5561012

Quadrifilar Helix antenna for UHF RFID

2010· article· en· W2112533667 on OpenAlexaff
Garret McKerricher, Jim Wight

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsUltra high frequencyRadio-frequency identificationAntenna (radio)Computer scienceMicrostrip antennaElectrical engineeringHelical antennaRadiation patternMicrostripTelecommunicationsAntenna efficiencyElectronic engineeringEngineering

Abstract

fetched live from OpenAlex

Radio Frequency Identification (RFID) technology is widely used for automatically identifying and tracking objects. Specifically Ultra High Frequency (UHF) (900 MHz) RFID technology is rapidly expanding because it offers the best tradeoffs between read range, cost, and size. UHF RFID has been focused on supply chain management, serving the needs of manufacturing, distribution and shipping. The low cost of the RFID tags has made the systems very attractive. While UHF RFID tags cost tens of cents, RFID reader systems cost hundreds to thousands of dollars. Much effort has been concentrated on increasing tag read distance so that a reader can cover a larger area. Increasing the coverage area reduces the number of readers, and antennas necessary. Commercial UHF RFID reader antennas are based upon the microstrip patch antenna. The radiation pattern of a patch antenna has peak gain at boresight (directly in front of the antenna) as seen in Fig. 1. A radiation pattern with peak gain at boresight is not always ideal. There are many applications where an RFID reader antenna is centered above the coverage area and the path loss at boresight is lowest. This paper describes the design of a Quadrifilar Helix Antenna (QFHA) with a uniquely shaped radiation pattern to efficiently address this situation and increase the coverage area.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.982
Threshold uncertainty score0.548

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)0.0010.000

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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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 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

Citations7
Published2010
Admission routes1
Has abstractyes

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