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Record W1982382171 · doi:10.1007/s11265-012-0656-8

Differential Time Signaling Data-Link Architecture

2012· article· en· W1982382171 on OpenAlexafffund
Mostafa Rashdan, Abdel Yousif, J.W. Haslett, Brent Maundy

Bibliographic record

VenueJournal of Signal Processing Systems · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvancements in PLL and VCO Technologies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Calgary
KeywordsSignal edgeJitterComputer scienceDigital clock managerSIGNAL (programming language)CMOSClock domain crossingField-programmable gate arrayBandwidth (computing)Electronic engineeringComputer hardwareClock skewClock signalAnalog signalEngineeringTelecommunicationsDigital signal processingSynchronous circuit

Abstract

fetched live from OpenAlex

A new time-based high-speed data-link architecture, which we call Differential time Signaling (DTS) is presented. A clock pulse is embedded in the transmitted signal and is used as a time reference against which the rising and falling data pulse edge timings are compared. Using the DTS approach, data encoding is achieved by spacing the time between the embedded clock edges and the data pulse edges using a hierarchical time-delay resolution assignment to each bit in the data sequence. The proposed link is shown to concentrate the signal energy in a low bandwidth while reducing clock jitter effect. A simulated 3 Gb/s 90 nm CMOS DTS link using a 500 MHz clock signal is also described to provide a flavor for a monolithic realization. As a proof of concept, 700 Mb/s and 1.6 Gb/s DTS-based links have been designed using a commercial FPGA board. The measured eye diagrams for the transmitted and received signals over a 40-inch FR4 channel are presented.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.025
GPT teacher head0.255
Teacher spread0.230 · 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 designNot applicable
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

Citations15
Published2012
Admission routes2
Has abstractyes

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Same venueJournal of Signal Processing SystemsSame topicAdvancements in PLL and VCO TechnologiesFrench-language works237,207