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Record W1585335508 · doi:10.23919/eumc.2013.6686831

Low-cost E-band receiver front-end development for gigabyte point-to-point wireless communications

2013· article· en· W1585335508 on OpenAlexaff
Nasser Ghassemi, Jules Gauthier, Ke Wu

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2013
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversité de MontréalPolytechnique Montréal
Fundersnot available
KeywordsBandwidth (computing)Printed circuit boardElectrical engineeringRF front endElectronic engineeringFront and back endsElectronic componentIntegrated circuitWirelessMonolithic microwave integrated circuitEngineeringComputer scienceAntenna (radio)Telecommunications

Abstract

fetched live from OpenAlex

This paper presents an attractive low cost E-band (81-86 GHz) receiver front-end system with a broad bandwidth (up to 5 GHz) based on substrate integrated waveguide (SIW) technology. The proposed subsystem is designed for gigabyte point-to-point wireless communication system. Active components are surface-mounted on alumina substrate utilizing miniature hybrid microwave-integrated circuit (MHMIC) technology. To increase gain and bandwidth of the antenna, and also to reduce fabrication cost, antenna and passive components are designed and fabricated on a low cost Rogers 6002 substrate with low dielectric constant. To fabricate the passive components, low cost printed circuit board (PCB) process is used. Then, the two substrates are integrated together by simple wire bonding process. With this design and implementation technique, our E-band front-end receiver subsystem can demonstrate attractive advantages and features such as broad bandwidth, low cost, compact size, light weight, and repayable performance. The minimum detectable power density at the receiver point is 0.41 μW/cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and the dynamic range is 72 dB.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.706
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.212
Teacher spread0.201 · 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
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

Citations9
Published2013
Admission routes1
Has abstractyes

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