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Record W1906770071 · doi:10.1109/tcsi.2015.2477577

A Low-Power Dual-Injection-Locked RF Receiver With FSK-to-OOK Conversion for Biomedical Implants

2015· article· en· W1906770071 on OpenAlexaff
Mohamed Zgaren, Mohamad Sawan

Bibliographic record

VenueIEEE Transactions on Circuits and Systems I Regular Papers · 2015
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsFrequency-shift keyingTransceiverElectrical engineeringCMOSRadio frequencyElectronic engineeringEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

We present in this paper an ultra low power ISM band RF receiver intended for implantable biomedical devices. The proposed circuit, which represents the reception part of a new energy-efficient RF FSK transceiver, consist of an FSK receiver (Rx) with OOK fully passive wake-up device (WuRx). This WuRx is batteryless with energy harvesting technique which plays an important role in making the RF transceiver energy-efficient. This proposed receiver is achieved with a reduced hardware architecture which does not use an accurate local oscillator, high Q external inductor and I/Q signal path. The circuit is base on a dual injection locked FSK-to-ASK conversion technique. The circuit is implemented in IBM 0.13 μm CMOS technology with 1.2 V supply voltage. This WuRx achieves a data rate of 100 kbps for 0.2 μW power dissipation at -53 dBm input signal. The Rx shows -78 dBm sensitivity for 8 Mbps data rate while consuming 639 μW power.

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 communication0.0000.001
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 head0.017
GPT teacher head0.211
Teacher spread0.194 · 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

Citations46
Published2015
Admission routes1
Has abstractyes

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