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Record W3009135861 · doi:10.1109/tie.2020.2977550

Fixed-Frequency Low-Loss Dielectric Material Sensing Transmitter

2020· article· en· W3009135861 on OpenAlexafffund
Hossein Saghlatoon, Rashid Mirzavand, Pedram Mousavi

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsTransmitterAntenna (radio)Radio frequencyFrequency bandElectronic engineeringFrequency modulationRadio transmitter designRadio-frequency identificationAntenna tunerSIGNAL (programming language)Electrical engineeringComputer scienceAcousticsEngineeringDipole antennaPhysicsAntenna efficiency

Abstract

fetched live from OpenAlex

This article presents an all-in-one system for the detection of the relative permittivity of samples in direct contact with a sensor antenna, based on the frequency variation detection and using a cost- and energy-effective manner. In a specific application, in which the antenna should be in contact with the sensing material, characteristics of the antenna change with respect to the frequency spectrum for different materials. Conventionally, a frequency spectrum monitoring device is required to monitor these changes, and the sensing data should be obtained by postprocessing the observation. The proposed system converts the sensing information in the frequency response of the device to a voltage, which can be utilized further for transmission as well as compensating and frequency retuning the system. The sensor antenna loads the radio frequency oscillator at the transmitter resulting in a change at the operating frequency of the system. A small portion of the signal is sampled and used for recovery in a phase/frequency comparator (PFC). The output of the PFC is a voltage corresponding to the difference between the operating frequency and the reference signal. The proposed sensor system is fabricated at the 915-MHz ultrahigh-frequency radio frequency identification band using on-off keying modulation as an evaluation, and the measured results with some known samples are presented. Since the proposed technique is implemented by utilizing the building blocks of a conventional transmitter, the power consumption and cost of the system are kept intact.

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 score0.823
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 science0.0000.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.208
Teacher spread0.181 · 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

Citations28
Published2020
Admission routes2
Has abstractyes

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