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Record W4310456504 · doi:10.1109/tbcas.2022.3225304

A 21.3%-Efficiency Clipped-Sinusoid UWB Impulse Radio Transmitter With Simultaneous Inductive Powering and Data Receiving

2022· article· en· W4310456504 on OpenAlexaff
Nima Soltani, Hamed Mazhab Jafari, Karim Abdelhalim, Hossein Kassiri, Xilin Liu, Roman Genov

Bibliographic record

VenueIEEE Transactions on Biomedical Circuits and Systems · 2022
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of TorontoYork University
Fundersnot available
KeywordsTransmitterElectrical engineeringCMOSFrequency-shift keyingVoltage-controlled oscillatorElectronic engineeringBit error rateWirelessData transmissionChipRadio frequencyEngineeringTelecommunicationsVoltageComputer scienceDemodulationChannel (broadcasting)

Abstract

fetched live from OpenAlex

An ultra-wide-band impulse-radio (UWB-IR) transmitter (TX) for low-energy biomedical microsystems is presented. High power efficiency is achieved by modulating an LC tank that always resonates in the steady state during transmission. A new clipped-sinusoid scheme is proposed for on-off keying (OOK)-modulation, which is implemented by a voltage clipper circuit with on-chip biasing generation. The TX is designed to provide a high data-rate wireless link within the 3-5 GHz band. The chip was fabricated in 130 nm CMOS technology and fully characterized. State-of-the-art power efficiency of 21.3% was achieved at a data-rate of 230 Mbps and energy consumption of 21pJ/b. A bit-error-rate (BER) of less than 10$^{-6}$was measured at a distance of 1 m without pulse averaging. In addition, simultaneous wireless powering and VCO-based data transmission are supported. A potential extension to a VCO-free all-wireless mode to further reduce the power consumption is also discussed.

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 score0.001
Threshold uncertainty score0.004

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.018
GPT teacher head0.225
Teacher spread0.208 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

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Same venueIEEE Transactions on Biomedical Circuits and SystemsSame topicEnergy Harvesting in Wireless NetworksFrench-language works237,207