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Record W2359159804

An Image-Rejection Complex Filter with Low Power Based on Transconductance-Resistance-Capacitance Cells

2013· article· en· W2359159804 on OpenAlexaff
Cheng Jun

Bibliographic record

VenueXi'an Jiaotong Daxue xuebao · 2013
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsQueen's University
Fundersnot available
KeywordsTransconductanceCapacitanceFilter (signal processing)CMOSLow-pass filterElectronic engineeringElectrical engineeringImage responseBandwidth (computing)High-pass filterOperational transconductance amplifierLinearityComputer scienceVoltageRadio frequencyPhysicsAmplifierEngineeringTelecommunicationsOperational amplifierTransistorIntermediate frequency
DOInot available

Abstract

fetched live from OpenAlex

A 1st-order transconductance-capacitance-resistance(Gm-R-C) complex filter for image rejection in low-IF wireless sensor network(WSN) receiver is proposed to reduce the power consumption of radio frequency receiver in WSN.The proposed cell adopts two fully differential Gm-cells with low power and R-C loads to perform low-pass filtering for the input I/Q signals,while the frequency translation is realized by two additional differential Gm cells through directly cross-adding relevant currents.Resistive source degeneration is adopted in the Gm cells to improve linearity.Based on the proposed 1st-order cell,a 4th-order Butterworth complex filter with center frequency of 300 kHz and bandwidth of 160 kHz is designed using the 0.35 μm CMOS technology.Simulation results under 1% I/Q mismatch show that the filter achieves an image rejection ratio of 50 dB,an in-band 3rd-order input intercept power of 25.1 mW,a pass-band gain of 18 dB,and an RMS voltage of input referred noise of 35 μV with current consumption of only 760 μA from a 2.7 V supply.All the simulated parameters achieve the system requirements of WSN with low power consumption.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.525
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.011
GPT teacher head0.197
Teacher spread0.186 · 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.

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

Citations0
Published2013
Admission routes1
Has abstractyes

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