Detecting the Stable, Observable and Controllable States of the Human Brain Dynamics

Kamrani, Ehsan; Foroushani, Armin N.; Vaziripour, Mohsen

doi:10.4236/ojmi.2012.24024

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…1 is tested. Second, the combined reduction is applied to a linearized system for the inversion of fMRI data to deduce connectivity between brain regions [29,2]. Lastly, an extreme-scale problem is tested as well as an evaluation of the effectivity of the reduction method for different configurations.…”

Section: Numerical Resultsmentioning

confidence: 99%

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Data-driven combined state and parameter reduction for inverse problems

Himpe

Ohlberger

2015

Adv Comput Math

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In this contribution we present an accelerated optimization-based approach for combined state and parameter reduction of a parametrized forward model, which is used to construct a surrogate model in a Bayesian inverse problem setting. Following the ideas presented in Lieberman et al. (SIAM J. Sci. Comput. 32(5), 2523-2542, 2010), our approach is based on a generalized data-driven optimization functional in the construction process of the reduced order model and the usage of a MonteCarlo basis enrichment strategy that results in an additional speed-up of the overall method. In principal, the model reduction procedure is based on the offline construction of appropriate low-dimensional state and parameter spaces and an online inversion step using the resulting surrogate model that is obtained through projection of the underlying forward model onto the reduced spaces. The generalizations and enhancements presented in this work are shown to decrease overall computational time and thus allow an application to large-scale problems. Numerical experiments for a generic model and a fMRI connectivity model are presented in order to compare the computational efficiency of our improved method with the original approach.

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Section: Numerical Resultsmentioning

confidence: 99%

“…In the scope of this work a linearized fMRI forward sub-model from [29] is utilized to be applicable in a fully linear setting:…”

Section: Fmri Connectivity Modelmentioning

confidence: 99%

Data-driven combined state and parameter reduction for inverse problems

Himpe

Ohlberger

2015

Adv Comput Math

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“…The combined state and parameter reduction using the gramian-based (using [3]) and optimization-based methods is demonstrated on two models. First, a model for the connectivity of different brain region based generating fMRI signals [6,7]; and second, a connectivity model generating EEG signals [8]. In figure 1, the relative L 2 output error of the combined reduction in state and parameter space is shown for varying sizes in states and parameters for the reduced order model.…”

Section: Numerical Resultsmentioning

confidence: 99%

Combined State and Parameter Reduction

Himpe

Ohlberger

2014

Proc Appl Math and Mech

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“…where n is the total number of GPS devices, idealValue is the IDEAL weight value from CnW-S1, h fused is the fused EC and GPS heading value, h allGPS is the fused calculated heading for all the "n" number of GPS, w allGPS is the weight given to calculated combined GPS heading, h EC is the measured EC heading, w EC is the weight given to EC heading, w i is the weight given to "i th " GPS, h i is the calculated FAz heading of "i th " GPS, and * h fused is labeled as "GPSEC_yaw" in the later parts of algorithm and it will also be used in lieu of GPSn_FAz during the EKF stage when GPS data is invalid. The controllability and observability of the extended Kalman filter or Kalman filter have been extensively researched and proven in several works such as those by Elizabeth and Jothilakshmi [45], Kamrani et al [46], and Southall et al [47]. The dynamic and measurement models which are nonlinear in nature are as follows:…”

Section: Classification and Weighing-stage 1 (Cnw-s1mentioning

confidence: 99%

Design and Real-Time Implementation of a 3-Stage CnW Heading System on an Ubuntu Linux-Embedded Board

Vista

Chong

2019

Journal of Sensors

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This paper describes the design and real-time implementation of a proposed algorithm for deriving an accurate heading system by fusing data from various inexpensive sensor devices that is comparable to more expensive maritime navigation systems. The proposed algorithm is a 3-Stage Classification N’ Weighing (CnW) Heading System with forward azimuth (FAz) and extended Kalman filter (EKF). Data from three Global Positioning System devices, an inertial measurement unit, and an electronic compass were fed into the algorithm that can be generally described as Classification N’ Weighing-Stage 1 → forward azimuth → Classification N’ Weighing-Stage 2 → extended Kalman filter → Classification N’ Weighing-Stage 3. The proposed algorithm is shown to be comparably accurate as an expensive marine navigation system, and it has less processing time compared to our previous work. The Qt-anywhere-based system developed on a Linux desktop was successfully downloaded onto an Ubuntu Linux-embedded board for real-time implementation. Important notes related to device naming problems when deploying the system on a Linux-embedded board are also given as reference for those interested to address it.

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Detecting the Stable, Observable and Controllable States of the Human Brain Dynamics

Cited by 6 publications

References 15 publications

Data-driven combined state and parameter reduction for inverse problems

Data-driven combined state and parameter reduction for inverse problems

Combined State and Parameter Reduction

Design and Real-Time Implementation of a 3-Stage CnW Heading System on an Ubuntu Linux-Embedded Board

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