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Record W4241200026 · doi:10.1115/imece2012-87968

MEMS Demodulator

2012· article· en· W4241200026 on OpenAlexaff
So-Ra Chung, Sangtak Park, Eihab Abdel‐Rahman, John T. W. Yeow, Mahmoud Khater

Bibliographic record

VenueVolume 9: Micro- and Nano-Systems Engineering and Packaging, Parts A and B · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDemodulationBasebandAmplifierMicroelectromechanical systemsElectronic engineeringRadio frequencyIntermediate frequencyElectrical engineeringSoftware-defined radioSIGNAL (programming language)Heterodyne (poetry)Superheterodyne receiverComputer scienceEngineeringPhysicsAcousticsChannel (broadcasting)OptoelectronicsCMOS

Abstract

fetched live from OpenAlex

This paper presents research focusing on developing and simulating a new way of digital demodulation for the front end Radio frequency (RF) mechanically using MEMS electrostatic actuator by sensing the displacement of a parallel-plates. The operating principle based on the coupling multi-physics of the proposed demodulation device is explained. The analytical modeling and simulation results with experimental data are presented. Recent developments in the Micro Electro Mechanical Systems (MEMS) technology have shown the benefits of reliable mechanical strength that merges with electrical properties. Interest has increased and thus on to improve their performance by applying MEMS technology as to replace existing industrial parts and tools. A typical RF receiver consists of a front end, a band-pass filter, low noise amplifier (LNA), a local oscillator, and a mixer that recovers a baseband signal from a modulated RF signal. In a heterodyne receiver there is more than one intermediate stage. ASK and FSK digital demodulation using electrostatic actuator indicates better feasibility at lower frequency lower than 100 Hz for digital demodulation while indicating wide range of potential baseband range up to 1 kHz.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.937
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.006
GPT teacher head0.180
Teacher spread0.174 · 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 designNot applicable
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
Published2012
Admission routes1
Has abstractyes

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Same venueVolume 9: Micro- and Nano-Systems Engineering and Packaging, Parts A and BSame topicAdvanced MEMS and NEMS TechnologiesFrench-language works237,207