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Record W2125623048 · doi:10.5539/mas.v8n5p19

Efficiency Analysis of Low Power Class-E Power Amplifier

2014· article· en· W2125623048 on OpenAlex
Mousa Yousefi, Ziaadin Daie Koozehkanani, Jafar Sobhi, Hamid Jangi, Nasser Nasirezadeh

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueModern Applied Science · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsnot available
Fundersnot available
KeywordsSchematicAmplifierPower-added efficiencyInductorPower (physics)MATLABElectrical efficiencyElectronic engineeringCMOSElectrical engineeringPower bandwidthRF power amplifierComputer sciencePhysicsEngineeringVoltage

Abstract

fetched live from OpenAlex

This paper presents an analysis of effect of inductor and switch losses on output power and efficiency of low power class-E power amplifier. This structure is suitable for integrated circuit implementation. Since on chip inductors have large losses than the other elements, the effect of their losses on efficiency has been investigated. Equations for the efficiency have been derived and plotted versus the value of inductors and switch losses. Derived equations are evaluated using MATLAB. Also, Cadence Spectre has been used for schematic simulation. Results show a fair matching between simulated power loss and efficiency and MATLAB evaluations. Considering the analysis, the proposed power amplifier shows about 13 % improvement in power effiency at 400 MHz and -2 dBm output power. It is simulated in 0.18 ?m CMOS technology.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.904
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
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 head0.006
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