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An Ultralow-Power Capacitive Array-Based IR-UWB Transmitter Using Cross-Coupled Oscillator

2023· article· en· W4384947562 on OpenAlexaff
Hadi Hayati, Gabriel Gagnon-Turcotte, Mousa Karimi, Benoit Gosselin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsTransmitterCapacitive sensingCMOSElectrical engineeringComputer scienceElectronic engineeringImpulse (physics)Bit error rateChannel (broadcasting)TelecommunicationsEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents an ultra-wideband (UWB) transmitter based on capacitive array that decreases dependency of data rate to pulse repetition frequency, as well as power consumption and complexity. The entire system includes several delay stages, a capacitive array circuit, a Schmitt trigger, an impulse generator, a cross-coupled oscillator, and an antenna driver. A sequence of 5-bit parallel data is applied to the capacitive array, providing ramp signals with 32 equally separated slopes. This returns a variable pulsewidth at the output of the Schmitt trigger circuit, which corresponds to a specific sequence of input data. Post-layout simulation results show that the proposed circuit provides a linear time change in the pulsewidth with an accuracy of 176 ps in average for every input data LSB. Furthermore, the entire circuit consumes only 190 µW from a 0.6-V supply. The proposed transmitter achieves a significantly low energy consumption of 950 fJ/bit at 200 Mbps within the Federal Communications Commission spectral mask which addresses the design challenges of ultralow-power internet-of-things devices. The circuit is designed in TSMC 65-nm standard CMOS technology and occupies 0.0525 mm<sup>2</sup>of die area.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.268
Teacher spread0.245 · 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 teacher head, not a consensus.

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
Published2023
Admission routes1
Has abstractyes

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