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Record W2547381650 · doi:10.1109/newcas.2016.7604801

Quad-Level Carrier Width Modulation demodulator for micro-implants

2016· article· en· W2547381650 on OpenAlexaff
Aref Trigui, Mohamed R. Ali, Ahmed Chiheb Ammari, Yvon Savaria, Mohamad Sawan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsDemodulationModulation (music)Pulse-width modulationElectronic engineeringComputer sciencePulse-amplitude modulationData transmissionCMOSTransmission (telecommunications)Pulse-position modulationIBMPower consumptionElectrical engineeringWirelessVoltagePower (physics)EngineeringTelecommunicationsComputer hardwarePulse (music)Materials scienceChannel (broadcasting)Physics

Abstract

fetched live from OpenAlex

Due to their growing potential, Implantable Medical Devices (IMDs) are getting more complex and thus require more sophisticated wireless powering and communication systems. This paper introduces a new modulation technique based on pulse width modulation for data transmission across inductive links. The technique is called Quad-Level Carrier Width Modulation (QCWM). It allows high data-rate transmission and ultra low power consumption, while maintaining a simple implementation. A very simple fully integrated QCWM demodulator circuit is implemented using 130 nm IBM CMOS technology. Post-layout simulation results validate the feasibility of the proposed technique and show ultra-low power consumption of 35.5 μW drawn from a supply voltage of 1.2V when transmitting data at a rate of 10.85 Mbps.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.222
Teacher spread0.198 · 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
GenreMethods

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
Published2016
Admission routes1
Has abstractyes

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