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Record W2128161983 · doi:10.1049/iet-cds.2011.0279

Low-power oscillator for passive radio frequency identification transponders

2012· article· en· W2128161983 on OpenAlexafffund
Mahzad Azarmehr, Rashid Rashidzadeh, Majid Ahmadi

Bibliographic record

VenueIET Circuits Devices & Systems · 2012
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Windsor
FundersCMC Microsystems
KeywordsPower (physics)Electronic engineeringComputer scienceElectrical engineeringRadio-frequency identificationCMOSFrequency bandDynamic demandRing oscillatorPower budgetMicrowaveTelecommunicationsVoltageEngineeringSwitched-mode power supplyPhysicsBandwidth (computing)

Abstract

fetched live from OpenAlex

Passive radio frequency identification tags extract energy from incoming electromagnetic waves to power up their internal circuitry. Such a limited source of power demands efficient circuits to minimise the power consumption. In this work a new technique is proposed to design a low-power ring oscillator in which the voltage swing of internal nodes are constrained to lower the dynamic power consumption. The proposed power reduction technique can be employed for RFID tags operating over different frequency bands from low frequency (LF) to microwave. A low-power oscillator operating in the medium-frequency range (6–16 MHz) for applications such as electronic article surveillance and item management has been implemented in this work. Post-layout simulation results using STMicroelectronics CMOS 65 nm technology indicate that the proposed method can reduce the power consumption by more than 25%.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.224
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2012
Admission routes2
Has abstractyes

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