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An Estimation Algorithm for General Linear Single Particle Tracking Models with Time-Varying Parameters

Molecules (Basel, Switzerland), 2021-02, Vol.26 (4), p.886

2021

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A Two-Filter Approach for State Estimation Utilizing Quantized Output Data

Sensors (Basel, Switzerland), 2021-11, Vol.21 (22), p.7675

2021

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Finite Impulse Response Errors-in-Variables System Identification Utilizing Approximated Likelihood and Gaussian Mixture Models

IEEE access, 2023, Vol.11, p.24615-24630

2023

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Maximum Likelihood Estimation for an SAG Mill Model utilizing Physical Available Measurements

IEEE access, 2024-01, Vol.12, p.1-1

2024

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On Filtering and Smoothing Algorithms for Linear State-Space Models Having Quantized Output Data

Mathematics (Basel), 2023-03, Vol.11 (6), p.1327

2023

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On identification of FIR systems having quantized output data

Automatica (Oxford), 2011-09, Vol.47 (9), p.1905-1915

2011

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Identification of sparse FIR systems using a general quantisation scheme

International journal of control, 2014-04, Vol.87 (4), p.874-886

2014

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EM-Based Maximum-Likelihood Channel Estimation in Multicarrier Systems With Phase Distortion

IEEE transactions on vehicular technology, 2013-01, Vol.62 (1), p.152-160

2013

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Local likelihood estimation of time-variant Hawkes models

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, p.4199-4203

2016

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A time-varying approach to single particle tracking with a nonlinear observation model

2020 American Control Conference (ACC), 2020, Vol.2020, p.5151-5156

2020

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Asymptotic statistical analysis for model-based control design strategies

Automatica (Oxford), 2011-05, Vol.47 (5), p.1041-1046

2011

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Single Particle Tracking Model Segmentation using Change Detection Techniques

Biophysical journal, 2021-02, Vol.120 (3), p.177a-177a

2021

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Truncated Hawkes point process modeling: System theory and system identification

Automatica (Oxford), 2020-03, Vol.113, p.108733, Article 108733

2020

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Model Segmentation in Single Particle Tracking

IFAC-PapersOnLine, 2021-01, Vol.54 (20), p.340-345

2021

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Estimation of Time-Varying Single Particle Tracking Models using Local Likelihood

Biophysical journal, 2019-02, Vol.116 (3), p.569a-569a

2019

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Determination of ewe behaviour around lambing time and prediction of parturition 7 days prior to lambing by tri-axial accelerometer sensors in an extensive farming system

Animal production science, 2022-07, Vol.62 (17), p.1729-1738

2022

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Estimation of general time-varying single particle tracking linear models using local likelihood

2020 European Control Conference (ECC), 2020, p.527-533

2020

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On the end-performance metric estimator selection

Automatica (Oxford), 2015-08, Vol.58, p.22-27

2015

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Maximum likelihood identification of Hawkes-Pham models with a guaranteed stability condition

2015 54th IEEE Conference on Decision and Control (CDC), 2015, p.3373-3378

2015

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A map approach for ℓq-norm regularized sparse parameter estimation using the EM algorithm

2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP), 2015, p.1-6

2015

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Model-based control design for a district heating plant

2017 IEEE 15th International Conference on Industrial Informatics (INDIN), 2017, p.615-620

2017

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On experiment design for single carrier and multicarrier systems

2015 European Control Conference (ECC), 2015, p.1772-1777

2015

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EM-based ML channel estimation in OFDM systems with phase distortion using RB-EKF

2014 International Symposium on Wireless Personal Multimedia Communications (WPMC), 2014, p.232-237

2014

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Model Segmentation in Single Particle Tracking⁎⁎This work was supported in part by the NIH through 1R01GM117039-01A1

IFAC-PapersOnLine, 2021, Vol.54 (20), p.340-345

2021

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A scenario-based approach to parameter estimation in state-space models having quantized output data

49th IEEE Conference on Decision and Control (CDC), 2010, p.2011-2016

2010

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An Estimation Algorithm for General Linear Single Particle Tracking Models with Time-Varying Parameters

A Two-Filter Approach for State Estimation Utilizing Quantized Output Data

Finite Impulse Response Errors-in-Variables System Identification Utilizing Approximated Likelihood and Gaussian Mixture Models

Maximum Likelihood Estimation for an SAG Mill Model utilizing Physical Available Measurements

On Filtering and Smoothing Algorithms for Linear State-Space Models Having Quantized Output Data

On identification of FIR systems having quantized output data

Identification of sparse FIR systems using a general quantisation scheme

EM-Based Maximum-Likelihood Channel Estimation in Multicarrier Systems With Phase Distortion

Local likelihood estimation of time-variant Hawkes models

A time-varying approach to single particle tracking with a nonlinear observation model

Asymptotic statistical analysis for model-based control design strategies

Single Particle Tracking Model Segmentation using Change Detection Techniques

Truncated Hawkes point process modeling: System theory and system identification

Model Segmentation in Single Particle Tracking

Estimation of Time-Varying Single Particle Tracking Models using Local Likelihood

Determination of ewe behaviour around lambing time and prediction of parturition 7 days prior to lambing by tri-axial accelerometer sensors in an extensive farming system

Estimation of general time-varying single particle tracking linear models using local likelihood

On the end-performance metric estimator selection

Maximum likelihood identification of Hawkes-Pham models with a guaranteed stability condition

A map approach for ℓq-norm regularized sparse parameter estimation using the EM algorithm

Model-based control design for a district heating plant

On experiment design for single carrier and multicarrier systems

EM-based ML channel estimation in OFDM systems with phase distortion using RB-EKF

Model Segmentation in Single Particle Tracking⁎⁎This work was supported in part by the NIH through 1R01GM117039-01A1

A scenario-based approach to parameter estimation in state-space models having quantized output data

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