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Record W2790387962 · doi:10.1049/iet-cds.2017.0507

Low‐power low data rate FM‐UWB receiver front end

2018· article· en· W2790387962 on OpenAlexafffund
Sean E. Whitehall, Carlos E. Saavedra

Bibliographic record

VenueIET Circuits Devices & Systems · 2018
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRF front endAmplifierEnvelope detectorCMOSElectrical engineeringSIGNAL (programming language)Front and back endsRadio receiver designIntermediate frequencyWidebandEngineeringElectronic engineeringRadio frequencyChannel (broadcasting)Computer scienceTransmitter

Abstract

fetched live from OpenAlex

This study introduces a frequency modulated ultra‐wideband (FM‐UWB) receiver optimised for low power and fast start‐up. The receiver consists of a front end amplifier converting a frequency modulated signal to an amplitude modulated signal which is applied to an envelope detector. The receiver front end is for 500 MHz channels centred at 3450 and 3950 MHz. The amplifier uses passive gain and four cascaded gain stages to achieve high radio‐frequency gain without the need for super‐regeneration. By simplifying the architecture this way, the front end has a 5 μs wake‐up time to enable efficient duty‐cycling. The measured front end receives a signal at −68 dBm while consuming 600 μW of power (excluding a test buffer) from a 1 V supply. Fabrication was done using the IBM 130‐nm CMOS technology on a 1 mm × 1 mm loose die.

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)0.001
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.005

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 head0.026
GPT teacher head0.237
Teacher spread0.211 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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