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Record W2162933179 · doi:10.1109/iscas.2005.1465981

High-Efficiency Power Amplifier for Wireless Sensor Networks

2005· article· en· W2162933179 on OpenAlexaff
Devrim Yılmaz Aksin, Stefano Gregori, Franco Maloberti

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAmplifierElectrical engineeringRF power amplifierTransmitterDuty cycleElectronic engineeringPower-added efficiencyCapacitorComputer scienceWireless sensor networkEngineeringLinear amplifierCMOSVoltageChannel (broadcasting)Computer network

Abstract

fetched live from OpenAlex

We designed a high-efficiency class-E switched-mode power amplifier for a wireless networked micro-sensor system. In this system, where each sensor operates using a micro-battery, has local processing capability, and contains on the same chip integrated sensing elements and an RF transmitter, most of the power is dissipated by the transmitter. The proposed amplifier achieves 92.4% maximum drain efficiency and can vary the transmitted power between -4.2 to -0.2 dBm with almost constant efficiency. This last feature is obtained by controlling the modulation duty cycle and by switching the capacitors' values in the parallel circuit. The possibility of choosing the transmitted power depending on the distance from a base station or other sensors and on the charge level of the battery, combined with power aware network protocols, improves network lifetime, reliability, and adaptability. The amplifier is designed in 0.18 /spl mu/m CMOS process and operates with a nominal 1.2 V supply.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.811
Threshold uncertainty score0.962

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 science0.0000.000
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 head0.007
GPT teacher head0.204
Teacher spread0.196 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations12
Published2005
Admission routes1
Has abstractyes

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