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Batteryless UHF-RFID Memory-Enabled Sensor for the Detection of Entry Point Openings

2025· article· W7124899966 on OpenAlexaff
Mahdi Barati, Mohammadesmaeil Akbarpour, RASHID MIRZAVAND

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicRFID technology advancements
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRectifier (neural networks)VoltagePoint (geometry)Power (physics)Energy (signal processing)Energy harvestingPhase (matter)

Abstract

fetched live from OpenAlex

This paper presents a novel batteryless RFID sensor tag for detecting the opening of entry points. To address the common issue of temporary loss of reader-tag communication-especially in crowded environments with multiple tags-a custom-designed 1-bit memory circuit is proposed that retains sensor data for a user-defined duration until communication is re-established. The tag employs a three-port structure to embed sensor data into the phase of the reader's reflected signal. An energy harvesting block, including a matched doublediode rectifier and a boost converter with an internal bulk converter, provides a regulated voltage to power the memory circuit. A prototype was implemented and tested successfully, demonstrating an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S_{11}$</tex> below -20 dB and approximately 60° phase variation at the reader side between logic states 0 and 1, confirming reliable operation and easy detection of the sensor state.

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 categoriesMeta-epidemiology (narrow)
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 score0.893
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.233
Teacher spread0.226 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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