Publication:
Machine learning and Kalman filtering for nanomechanical mass spectrometry

dc.contributor.departmentDepartment of Computer Engineering
dc.contributor.departmentDepartment of Electrical and Electronics Engineering
dc.contributor.kuauthorBaytekin, Nuri Berke
dc.contributor.kuauthorÇoban, Serhat Emre
dc.contributor.kuauthorDemir, Alper
dc.contributor.kuauthorErdoğan, Mete
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.date.accessioned2024-12-29T09:37:46Z
dc.date.issued2024
dc.description.abstractNanomechanical resonant sensors are used in mass spectrometry via detection of resonance frequency shifts. There is a fundamental tradeoff between detection speed and accuracy. Temporal and size resolution are limited by the resonator characteristics and noise. A Kalman filtering (KF) technique, augmented with maximum-likelihood estimation, was recently proposed as a Pareto optimal solution. We present enhancements and robust realizations for this technique, including a confidence boosted thresholding approach and machine learning (ML) for event detection. We describe learning techniques that are based on neural networks and boosted decision trees for temporal location and event size estimation. In the pure learning-based approach that discards the Kalman filter, the raw data from the sensor are used in training a model for both location and size prediction. In the alternative approach that augments a Kalman filter, the event likelihood history is used in a binary classifier for event occurrence. Locations and sizes are predicted using maximum-likelihood, followed by a Kalman filter that continually improves the size estimate. We present detailed comparisons of the learning-based schemes and the confidence boosted thresholding approach and demonstrate robust performance for a practical realization. Our results indicate that KF combined with the thresholding approach performs at least as well and at a lower computational cost when compared with ML techniques. © 2001-2012 IEEE.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.issue5
dc.description.openaccessAll Open Access
dc.description.openaccessGreen Open Access
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.volume24
dc.identifier.doi10.1109/JSEN.2024.3350730
dc.identifier.eissn1558-1748
dc.identifier.issn1530-437X
dc.identifier.quartileQ1
dc.identifier.scopus2-s2.0-85182930516
dc.identifier.urihttps://doi.org/10.1109/JSEN.2024.3350730
dc.identifier.urihttps://hdl.handle.net/20.500.14288/22481
dc.identifier.wos1280063700001
dc.keywordsKalman filtering (KF)
dc.keywordsMachine learning (ML)
dc.keywordsNanomechanical mass spectrometry
dc.language.isoeng
dc.publisherIEEE-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIEEE Sensors Journal
dc.rightsCC BY-NC-ND (Attribution-NonCommercial-NoDerivs)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectElectrical engineering
dc.subjectElectronic engineering
dc.subjectTelecommunications
dc.titleMachine learning and Kalman filtering for nanomechanical mass spectrometry
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorErdoğan, Mete
local.contributor.kuauthorBaytekin, Nuri Berke
local.contributor.kuauthorÇoban, Serhat Emre
local.contributor.kuauthorDemir, Alper
local.publication.orgunit1College of Engineering
local.publication.orgunit2Department of Computer Engineering;Department of Electrical and Electronics Engineering
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relation.isOrgUnitOfPublication21598063-a7c5-420d-91ba-0cc9b2db0ea0
relation.isOrgUnitOfPublication.latestForDiscovery89352e43-bf09-4ef4-82f6-6f9d0174ebae
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relation.isParentOrgUnitOfPublication.latestForDiscovery8e756b23-2d4a-4ce8-b1b3-62c794a8c164

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