Ensemble Kalman filter for state estimation of brain activity by considering a large scale nonlinear dynamical model

2017 ◽  
pp. 445-448
Author(s):  
P. A. Munõz-Gutiérrez ◽  
E. Giraldo
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Ensemble Kalman Filter ◽  
Large Scale ◽  
Brain Activity ◽  
Dynamical Model ◽  
Nonlinear Dynamical ◽  
Nonlinear Dynamical Model

ASIC Implementation of a Nonlinear Dynamical Model for Hippocampal Prosthesis

2018 ◽  
Vol 30 (9) ◽  
pp. 2472-2499 ◽  
Author(s):  
Zhitong Qiao ◽  
Yan Han ◽  
Xiaoxia Han ◽  
Han Xu ◽  
Will X. Y. Li ◽  
...  
Keyword(s):  
Low Power ◽  
Integrated Circuit ◽  
Large Scale ◽  
Random Number Generator ◽  
Core Area ◽  
Dynamical Model ◽  
Volterra Model ◽  
Nonlinear Dynamical ◽  
The Core ◽  
Nonlinear Dynamical Model

A hippocampal prosthesis is a very large scale integration (VLSI) biochip that needs to be implanted in the biological brain to solve a cognitive dysfunction. In this letter, we propose a novel low-complexity, small-area, and low-power programmable hippocampal neural network application-specific integrated circuit (ASIC) for a hippocampal prosthesis. It is based on the nonlinear dynamical model of the hippocampus: namely multi-input, multi-output (MIMO)–generalized Laguerre-Volterra model (GLVM). It can realize the real-time prediction of hippocampal neural activity. New hardware architecture, a storage space configuration scheme, low-power convolution, and gaussian random number generator modules are proposed. The ASIC is fabricated in 40 nm technology with a core area of 0.122 mm[Formula: see text] and test power of 84.4 [Formula: see text]W. Compared with the design based on the traditional architecture, experimental results show that the core area of the chip is reduced by 84.94% and the core power is reduced by 24.30%.

STATE ESTIMATION OF A LARGE-SCALE SYSTEM IN THE PETROLEUM INDUSTRY: THE ENSEMBLE KALMAN FILTER FOR UPDATING RESERVOIR MODELS

2007 ◽  
Vol 40 (5) ◽  
pp. 147-152
Author(s):  
Geir Næivdal ◽  
Alberto Bianco ◽  
Alberto Cominelli ◽  
Laura Dovera ◽  
Rolf Johan Lorentzen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Ensemble Kalman Filter ◽  
Large Scale ◽  
Petroleum Industry ◽  
Large Scale System ◽  
Reservoir Models

Multiscale-finite-element-based ensemble Kalman filter for large-scale groundwater flow

2012 ◽  
Vol 468-469 ◽  
pp. 22-34 ◽  
Author(s):  
Liangsheng Shi ◽  
Lingzao Zeng ◽  
Dongxiao Zhang ◽  
Jinzhong Yang
Keyword(s):  
Finite Element ◽  
Kalman Filter ◽  
Groundwater Flow ◽  
Ensemble Kalman Filter ◽  
Large Scale ◽  
Multiscale Finite Element

scholarly journals Scale-Dependent Representation of the Information Content of Observations in the Global Ensemble Kalman Filter Data Assimilation

2016 ◽  
Vol 144 (8) ◽  
pp. 2927-2945
Author(s):  
Nedjeljka Žagar ◽  
Jeffrey Anderson ◽  
Nancy Collins ◽  
Timothy Hoar ◽  
Kevin Raeder ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Information Content ◽  
Ensemble Kalman Filter ◽  
Large Scale ◽  
Variance Reduction ◽  
Weather Prediction ◽  
Current Data ◽  
Model Framework ◽  
The Tropics

Abstract Global data assimilation systems for numerical weather prediction (NWP) are characterized by significant uncertainties in tropical analysis fields. Furthermore, the largest spread of global ensemble forecasts in the short range on all scales is in the tropics. The presented results suggest that these properties hold even in the perfect-model framework and the ensemble Kalman filter data assimilation with a globally homogeneous network of wind and temperature profiles. The reasons for this are discussed by using the modal analysis, which provides information about the scale dependency of analysis and forecast uncertainties and information about the efficiency of data assimilation to reduce the prior uncertainties in the balanced and inertio-gravity dynamics. The scale-dependent representation of variance reduction of the prior ensemble by the data assimilation shows that the peak efficiency of data assimilation is on the synoptic scales in the midlatitudes that are associated with quasigeostrophic dynamics. In contrast, the variance associated with the inertia–gravity modes is less successfully reduced on all scales. A smaller information content of observations on planetary scales with respect to the synoptic scales is discussed in relation to the large-scale tropical uncertainties that current data assimilation methodologies do not address successfully. In addition, it is shown that a smaller reduction of the large-scale uncertainties in the prior state for NWP in the tropics than in the midlatitudes is influenced by the applied radius for the covariance localization.

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