Parameter estimation is considered to be one of the greatest challenges in computational systems biology. Biological experiments can measure only a fraction of the kinetic parameters and the rest has to be estimated in silico. Recently parameter estimation problems have been addressed in the framework of control theory. One of the most successful and widely used methods in control theory for es...

Neuroblastoma is the most common cancer in infants and the fourth most common cancer in children. Aggressive cell growth and chemoresistance are notorious obstacles in neuroblastoma therapy. Exposure to the anticancer drug ellipticine inhibits efficiently growth of neuroblastoma cells and induces apoptosis in these cells. However, ellipticine induced resistance in these cells. The upregulation ...

This paper presents sensor data fusion using Unscented Kalman Filter (UKF) to implement high performance vestibulo-ocular reflex (VOR) based vision tracking system for mobile robots. Information from various sensors is required to be integrated using an efficient sensor fusion algorithm to achieve a continuous and robust vision tracking system. We use data from low cost accelerometer, gyroscope...

a r t i c l e i n f o a b s t r a c t Keywords: Adaptation Unscented Kalman filter Masreliez–Martin filter Relative navigation Robot arm tracking Unscented Kalman filter (UKF) has been extensively used for state estimation of nonlinear stochastic systems, which suffers from performance degradation and even divergence when the noise distribution used in the UKF and the truth in a real system are...

Abstract Aiming to improve the position and velocity precision of INS/GNSS system during GNSS outages, a novel that combines unscented Kalman filter (UKF) nonlinear autoregressive neural networks with external inputs (NARX) is proposed. The NARX-based module utilized predict measurement updates UKF outages. A new offline approach for selecting optimal NARX suggested tested. This based ...

In a series of recent studies a new approach for applying the Kalman filter to nonlinear system, referred to as Unscented Kalman filter (UKF), was proposed. In this contribution we apply the UKF to several speech processing problems, in which a model with unknown parameters is given to the measured signals. We show that the nonlinearity arises naturally in these problems. Preliminary simulation...

In this paper three different filtering methods, the Extended Kalman Filter (EKF), the Gauss-Hermite Filter (GHF), and the Unscented Kalman Filter (UKF), are compared for state-only and coupled state and parameter estimation when used with log state variables of a model of the immunologic response to the human immunodeficiency virus (HIV) in individuals. The filters are implemented to estimate ...

Kalman filters provide an important technique for estimating the states of engineering systems. With several variations of nonlinear Kalman filters, there is a lack of guidelines for filter selection with respect to a specific research or engineering application. This creates a need for an in-depth discussion of the intricacies of different nonlinear Kalman filters. Particularly of interest for...

Existing polar transfer alignment (TA) algorithms are designed based on linear Kalman filters (KF) to estimate misalignment angles. In the case of a large misalignment angle, these algorithms cannot be applied in order to achieve accurate TA. In this paper, a TA algorithm based on an unscented Kalman filter (UKF) is proposed to solve the problem of the large misalignment angle in the polar regi...

This paper proposes an optimized algorithm for 3-D Point Based Rigid Registration. This algorithm uses an Unscented Kalman filter (UKF) for estimating the state vector of transformation, which can be interpreted as a nonlinear function of translation and rotation. In the previous work, we showed that the drawback of the UKF algorithm in estimating high range rotations is due to its sensitivity ...