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Record W2054754702 · doi:10.1109/saconet.2014.6867779

Exploiting dual-antenna diversity for phase cancellation in augmented RFID system

2014· article· en· W2054754702 on OpenAlexaff
Jing Wang, Miodrag Bolić

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIndoor and Outdoor Localization Technologies
Canadian institutionsUniversity of Ottawa
FundersChina Scholarship Council
KeywordsUltra high frequencyRadio-frequency identificationComputer scienceAntenna (radio)Antenna diversityDual (grammatical number)SIGNAL (programming language)Electronic engineeringPhase (matter)Radio frequencyTelecommunicationsEngineeringPhysics

Abstract

fetched live from OpenAlex

In this paper, we propose a dual antenna technique to lessen the phase cancellation effect in an augmented ultra high frequency (UHF) radio frequency identification (RFID) system. In the augmented RFID system, a recently developed semi-passive RFID component (sensatag) has the functionality to sense the backscattering communication between a reader and a passive tag being within its proximity. This unique functionality of sensatag allows for the development of proximity-based indoor localization solutions with high localization accuracy. However, due to the phase difference between RFID reader's continuous carrier wave (CW) and tag's backscattering signal, the backscattered signal from the tag received by the sensatag could be significantly attenuated. We call this destructive effect as phase cancellation. This problem exists in passive RFID system because the RFID reader transmits CW during tag's communication. Taking advantage of the spatial diversity of dual antenna technique, the probability of phase cancellation effect could be reduced. In addition, we present an UHF RFID system simulation framework, that includes the model of the sensatag. Then we demonstrate the performance of the dual antenna technique with data obtained from computer simulation.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.704
Threshold uncertainty score0.299

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 head0.015
GPT teacher head0.222
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 teacher head, 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".

Quick stats

Citations11
Published2014
Admission routes1
Has abstractyes

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