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Record W2319130819 · doi:10.1109/tcsii.2016.2551547

Improving Load Range of Dual-Band Impedance Matching Networks Using Load-Healing Concept

2016· article· en· W2319130819 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.

Bibliographic record

VenueIEEE Transactions on Circuits & Systems II Express Briefs · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsImpedance matchingElectrical impedanceMatching (statistics)Multi-band deviceCladding (metalworking)DielectricLoad balancing (electrical power)Computer scienceElectronic engineeringMaterials scienceTopology (electrical circuits)MathematicsElectrical engineeringTelecommunicationsEngineeringComposite materialOptoelectronicsAntenna (radio)GeometryStatistics

Abstract

fetched live from OpenAlex

A novel and very simple scheme to mend conventional dual-band impedance matching networks is presented. It involves the employment of a load-modifying element (load-healer) so as to extend the range of frequency-dependent complex load that could be matched. Two simple load-healers incorporated in the conventional T-network are used to illustrate the concept. The proposed scheme can be successfully applied in many situations where conventional matching networks are severely limited. Two prototypes operating concurrently at 1 and 2 GHz are designed corresponding to the two types of load-healer. They are implemented on FR4 substrate having a dielectric constant of 4.6, a substrate height of 1.5 mm, and a 35-μm copper cladding. The prototypes exhibit good agreement between their EM simulated and measured results.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.014
GPT teacher head0.214
Teacher spread0.200 · 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