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Record W4293863205 · doi:10.1109/siu55565.2022.9864694

PLL Based Synchronous Read-Out for Resonant Biosensors

2022· article· en· W4293863205 on OpenAlex
Zeynep Duygu Sütgöl, Günhan Dündar, Faík Başkaya

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2022 30th Signal Processing and Communications Applications Conference (SIU) · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsPhase-locked loopComputer scienceElectronic engineeringMicroelectromechanical systemsAdaptabilityBiosensorEngineeringPhase noiseOptoelectronicsNanotechnologyPhysicsMaterials science

Abstract

fetched live from OpenAlex

Resonant Biosensors are being preferred for biomedical measurement and diagnosis applications, thanks to advances in micro-electro-mechanical (MEMS) technologies. In this paper, Verilog implementation and simulation results of a PLL-Based synchronous read-out, designed to measure the frequency change that is detected by resonant biosensors, is presented. The digital design of the sensor read-out offers low complexity and adaptability to different technologies. The first-order PLL based design shows high accuracy in 398-402 MHz range according to simulations.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.939
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 studies0.0020.000
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 head0.031
GPT teacher head0.271
Teacher spread0.240 · 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