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Record W2131528784 · doi:10.1109/tmtt.2008.927409

Ultra-High-Speed Multichannel Data Transmission Using Hybrid Substrate Integrated Waveguides

2008· article· en· W2131528784 on OpenAlexaff
Asanee Suntives, Ramesh Abhari

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2008
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsMcGill University
Fundersnot available
KeywordsData transmissionElectronic engineeringBandwidth (computing)WaveguideTransmission (telecommunications)Cutoff frequencyChannel (broadcasting)InterconnectionComputer scienceMaterials scienceOptoelectronicsEngineeringTelecommunicationsComputer hardware

Abstract

fetched live from OpenAlex

Hybrid substrate integrated waveguides (SIWs) are proposed as an alternative low-cost interconnect solution to enable ultra-high-speed signal transmission. The SIW serves as a bandpass data transmission channel providing 15-GHz bandwidth. The hybrid structure utilizes the available low-pass frequency region below the cutoff frequency by adding striplines inside the waveguide. This technique of substrate reuse allows several independent data channels to be transmitted simultaneously through the hybrid structure. Hence, the channel capacity is increased without changing the overall waveguide footprint. Transition structures for routing striplines in the waveguide are designed and optimized through full-wave simulations. A number of test structures are fabricated to investigate data transmission using single and hybrid SIW interconnects. Experiments, as well as system simulations, demonstrate the capability of these interconnects for achieving ultra-high-speed data transmission. The hybrid waveguide system yields 15-Gb/s aggregate data transmission with excellent signal integrity characteristics. The proposed hybrid structure suggests an alternative solution for implementation of parallel data transmission systems with significant increase in the total channel throughput.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.632
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.028
GPT teacher head0.240
Teacher spread0.213 · 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

Citations33
Published2008
Admission routes1
Has abstractyes

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