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Record W1492348114 · doi:10.23919/eumc.2011.6101873

Low-complexity and frequency-scalable analog real-time FDM receiver based on a dispersive delay structure

2011· article· en· W1492348114 on OpenAlexaff
Babak Nikfal, Christophe Caloz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsFrequency-division multiplexingElectronic engineeringComputer scienceMultiplexingGroup delay and phase delayFrequency domainTime-division multiplexingAnalog signal processingSignal processingDigital signal processingOrthogonal frequency-division multiplexingComputer hardwareEngineeringTelecommunicationsBandwidth (computing)

Abstract

fetched live from OpenAlex

A real-time analog frequency division multiplexing (FDM) receiver is proposed based on a dispersive delay structure (DDS). The DDS used here is a C-section all-pass network with a linear group delay response. It demultiplexes the FDM signal in the time domain by mapping each of its frequency components to its corresponding group delay. Compared to conventional FDM receivers, which utilize many analog and digital components for demultiplexing, making the receiver architecture complex, the proposed FDM receiver employs a single DDS in a simpler architecture. Moreover, the digital circuits employed in the conventional FDM receivers suffer from certain limitations at high frequencies, such as low speed, high power consumption and cost. The proposed DDS-based FDM receiver is purely analog and can be frequency-scaled.

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.020
GPT teacher head0.219
Teacher spread0.198 · 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

Citations6
Published2011
Admission routes1
Has abstractyes

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