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Record W3016153035 · doi:10.1109/access.2020.2985711

A Three-Port Zero-Power RFID Sensor Architecture for IoT Applications

2020· article· en· W3016153035 on OpenAlexafffund
Nabil Khalid, Rashid Mirzavand, Hossein Saghlatoon, Mohammad Mahdi Honari, Pedram Mousavi

Bibliographic record

VenueIEEE Access · 2020
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology Futures
KeywordsWireless sensor networkComputer scienceCapacitive sensingKey distribution in wireless sensor networksWirelessElectro-optical sensorDemodulationUltra high frequencyElectrical engineeringEmbedded systemElectronic engineeringTelecommunicationsEngineeringComputer networkWireless network

Abstract

fetched live from OpenAlex

A novel zero-power wireless sensor architecture is proposed and demonstrated in this paper. The proposed wireless sensor, which is a passive sensor, combines UHF RFID and a capacitive sensor to enable reading of physical and chemical parameters wirelessly, without compromising much on the read-range, and complexity of conventional RFID tags. The sensor alters the phase of backscattered RFID signal, which is detected at the receiver using a non-coherent IQ demodulator. Due to the universal nature of this architecture, any type of sensor, such as temperature, humidity, water level sensor, can be realized. For the sake of simplicity, a varactor diode was initially used to get a thorough understanding of the system. Following that, a flood sensor was used to evaluate the performance of zero-power wireless sensor in a real-world application.

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: Empirical · Consensus signal: none
Teacher disagreement score0.938
Threshold uncertainty score0.803

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 head0.026
GPT teacher head0.260
Teacher spread0.234 · 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

Citations36
Published2020
Admission routes2
Has abstractyes

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