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Record W4240949463 · doi:10.22215/etd/2016-11466

The Field Programmable Microwave Substrate

2016· dissertation· en· W4240949463 on OpenAlexafffund
Nathan Jess

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsCarleton University
FundersDivision of ChemistryBruce Power
KeywordsMicrowaveMetamaterialMaterials scienceWaveguideRealization (probability)OptoelectronicsDielectricSubstrate (aquarium)Electronic engineeringElectrical engineeringEngineeringTelecommunications

Abstract

fetched live from OpenAlex

This thesis describes a research project to create a programmable microwave circuit having a similar level of programmability as that of a field programmable gate array (FPGA).The result of the thesis is the realization of the first ever practical low-loss programmable microwave waveguides.Since waveguides are a key component in any microwave circuit, this allows for the realization of a broad range of microwave functions with a single circuit.The programmable waveguides are implemented in what is referred to as a field programmable microwave substrate.The programmable substrate is implemented using metamaterials between two parallel metal plates.Between the metal plates are electrically small unit cells that consist of metal structuring that connects with active components not contained between the metal plates (i.e.above or below the metal plates).Each of these unit cells can be programmed to have a range of positive dielectric constants or a negative dielectric constant.Programming a positive dielectric constant core and negative dielectric constant sidewall yields a structure that can be described using the slab waveguide equations.This allows for waveguides to be dynamically programmed within the field programmable microwave substrate.The theory required to design low-loss metamaterials is presented and used to realize two prototype circuits.The first implementation is on an FR4 substrate and is used to demonstrate low-loss waveguides, amplifiers and oscillators from 0.9 -

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.003

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 head0.005
GPT teacher head0.215
Teacher spread0.209 · 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

Citations1
Published2016
Admission routes2
Has abstractyes

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