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Record W1724973444

High Power Two- Stage Class-AB/J Power Amplifier with High Gain and Efficiency

2014· article· en· W1724973444 on OpenAlexvenueno aff
Fatemeh Rahmani, Farhad Razaghian, Alireza Kashaninia

Bibliographic record

VenueJournal of academic and applied studies · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsnot available
Fundersnot available
KeywordsAmplifierPower bandwidthPower-added efficiencyPower gainElectronic engineeringLinear amplifierDirect-coupled amplifierRF power amplifierComputer scienceBroadbandHarmonicElectrical engineeringBandwidth (computing)Control theory (sociology)Topology (electrical circuits)Operational amplifierEngineeringPhysicsTelecommunicationsAcoustics
DOInot available

Abstract

fetched live from OpenAlex

This paper presents a hybrid Broadband power amplifier which provides high drain efficiency. AB and J, Two Classes of power amplifier are described using GaN HEMT with matching networks together with input and output compact elements. Using Load Pull method, the best input and output network in the central frequency of 3GHz for output power of 40dBm, 10dB high gain and high efficiency of 80%, has been designed. After describing the design of each of the amplifiers and comparing their performance, the proposed circuit, two-class AB/J are discussed to be the target of the circuit design, reducing the input power to achieve high efficiency output power and gain. Input and output matching proposed circuit elements in terms of theory and simulation are compared, and the results of both investigations were similar. Also, the fundamental harmonic and the second harmonic in the 0.5GHz bandwidth have the desirable amplitude of the output signal.

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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.251
Teacher spread0.239 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

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