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Record W2149016549 · doi:10.1109/glocom.2009.5425602

Parallel Singulation in RFID Systems

2009· article· en· W2149016549 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsQueen's University
Fundersnot available
KeywordsRadio-frequency identificationComputer scienceReading (process)Set (abstract data type)ThroughputCollisionInterrogationScheme (mathematics)Identification (biology)Computer architectureEmbedded systemTelecommunicationsWirelessOperating system

Abstract

fetched live from OpenAlex

Tag collisions can impose a major delay in radio frequency identification (RFID) systems. Such collisions are hard to overcome with passive tags due to their limited capabilities. In this paper, we look into the problem of minimizing the time required to read a set of passive tags. We propose the novel concept of parallel singulation which, aided by the multiple antennas configuration, interrogates tags in sets. Multiple instances of the anti-collision scheme are executed for each set of tags in parallel and in an autonomous manner. Simulation results show that our approach makes significant improvements in reducing collisions and interrogation delay and thus increasing the reading rate and throughput of RFID systems.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.188

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 head0.007
GPT teacher head0.213
Teacher spread0.206 · 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

Quick stats

Citations7
Published2009
Admission routes1
Has abstractyes

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