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Record W1973891858 · doi:10.1142/s021812660400191x

NEW SAMPLING METHOD TO IMPROVE THE SFDR OF WIDE BANDWIDTH ADC DEDICATED TO NEXT GENERATION WIRELESS TRANSCEIVER

2004· article· en· W1973891858 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Circuits Systems and Computers · 2004
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSpurious-free dynamic rangeElectronic engineeringComputer scienceChannel (broadcasting)Spurious relationshipBandwidth (computing)Successive approximation ADCDynamic rangeWirelessTransceiverConvertersSampling (signal processing)EngineeringTelecommunicationsElectrical engineeringCapacitor

Abstract

fetched live from OpenAlex

Modern wireless communication standards that support high rates of voice and video streaming need high-speed Analog-to-Digital Converters (ADCs) with wide Spurious-Free Dynamic Range (SFDR). Conventional time-interleaved ADCs suffer from spurious components that seriously affect the SFDR. In this paper, we present the mathematical background describing the effect of randomizing the samples among the interleaved ADCs and we propose a digitally oriented method based on this analysis to randomize the mismatches among the ADC channels. Analyses and simulations show the effectiveness of the proposed approach in multi-channel ADCs with arbitrary bit resolution, channel's number and sampling rate. For a 10-bit 500 MS/s ADC, the SFDR achieved using the proposed randomizing method can be as wide as 75 dB, which is an enhancement of more than 26 dB comparing to the conventional time interleaved ADC.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.905
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.242
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