2020
Poisson Kalman filter for disease surveillance
Ebeigbe D, Berry T, Schiff S, Sauer T. Poisson Kalman filter for disease surveillance. Physical Review Research 2020, 2: 043028. PMID: 39211287, PMCID: PMC11360429, DOI: 10.1103/physrevresearch.2.043028.Peer-Reviewed Original Research
2019
Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks
Gigante S, van Dijk D, Moon K, Strzalkowski A, Wolf G, Krishnaswamy S. Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks. 2019, 00: 1-4. DOI: 10.1109/sampta45681.2019.9030978.Peer-Reviewed Original ResearchStochastic dynamic systemsDeep generative neural networksProbability distributionDynamic systemsMarkov modelGlobal dynamicsLocal snapshotGenerative neural networksKalman filterSnapshot dataNeural networkNeural network frameworkRecurrent neural networkSuch systemsNext stateModeling networkNetwork frameworkDynamicsShallow modelsLocal transitionsHypothetical trajectoryModelBiological systemsNatural sciencesLongitudinal measurements
2016
Towards patient-specific modeling of mitral valve repair: 3D transesophageal echocardiography-derived parameter estimation
Zhang F, Kanik J, Mansi T, Voigt I, Sharma P, Ionasec RI, Subrahmanyan L, Lin BA, Sugeng L, Yuh D, Comaniciu D, Duncan J. Towards patient-specific modeling of mitral valve repair: 3D transesophageal echocardiography-derived parameter estimation. Medical Image Analysis 2016, 35: 599-609. PMID: 27718462, DOI: 10.1016/j.media.2016.09.006.Peer-Reviewed Original ResearchConceptsMitral valve modelingTemporal informationPatient-specific modelingImage acquisitionEuclidean distanceValve modelingComputational frameworkExtended Kalman filterImage analysisModeling frameworkKalman filterFrameworkAverage errorMitral valve geometryTEE imagesInformationMachineParameter estimationClosed mitral valveLeaflet material propertiesSubjective predictionModelingImagesRepresentationOptimization
2012
Reconstructing Mammalian Sleep Dynamics with Data Assimilation
Sedigh-Sarvestani M, Schiff S, Gluckman B. Reconstructing Mammalian Sleep Dynamics with Data Assimilation. PLOS Computational Biology 2012, 8: e1002788. PMID: 23209396, PMCID: PMC3510073, DOI: 10.1371/journal.pcbi.1002788.Peer-Reviewed Original ResearchConceptsUnscented Kalman filterData assimilationData assimilation frameworkParameter estimation methodNonlinear computational modelSleep-wake regulatory networkAssimilation frameworkUnknown parametersHidden variablesCovariance inflationNoisy variablesSlow dynamicsSparse measurementsComputational modelModel parametersKalman filterModel statesEstimation methodSimulation studyComplex systemsOptimal variablesFilter modelUKF frameworkModel variablesPartial observability
2010
Towards model-based control of Parkinson's disease
Schiff S. Towards model-based control of Parkinson's disease. Philosophical Transactions Of The Royal Society A Mathematical Physical And Engineering Sciences 2010, 368: 2269-2308. PMID: 20368246, PMCID: PMC2944387, DOI: 10.1098/rsta.2010.0050.Peer-Reviewed Original Research
2005
A skewed Kalman filter
Naveau P, Genton M, Shen X. A skewed Kalman filter. Journal Of Multivariate Analysis 2005, 94: 382-400. DOI: 10.1016/j.jmva.2004.06.002.Peer-Reviewed Original ResearchState-space modelKalman filterKalman filtering operationSkew-normal distributionMultivariate normal distributionKalman filter operationAssumption of normalityMultivariate caseComputational advantagesNormal distributionFiltering operationFilter operationSkewnessFilterExtensionLarge varietyModelApplicabilityDistributionNormalityWide rangeAssumption
1996
Tracking myocardial deformation using phase contrast MR velocity fields: a stochastic approach
Meyer FG, Constable RT, Sinusas AJ, Duncan JS. Tracking myocardial deformation using phase contrast MR velocity fields: a stochastic approach. IEEE Transactions On Medical Imaging 1996, 15: 453-465. PMID: 18215927, DOI: 10.1109/42.511749.Peer-Reviewed Original ResearchVelocity fieldVelocity dataKalman filterAverage errorField velocityVelocity vectorMR velocity dataDeformationEntire cardiac cycleNew approachBasic dynamical modelImage framesMotion problemCardiac motionLV motionFilterMotionContour dataStochastic approachThree-dimensional datasetsDynamical modelNoisePath lengthKinematicsPhantom data
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