In this paper, the design problem of state estimator for genetic regulatory networks with time delays and randomly occurring uncertainties has been addressed by a delay decomposition approach. The norm-bounded uncertainties enter into the genetic regulatory networks (GRNs) in random ways, and such randomly occurring uncertainties (ROUs) obey certain mutually uncorrelated Bernoulli distributed w...

This paper deals with the state estimation of dynamic systems. A recursive linear MMSE estimator is presented as an alternative to Kalman filtering . This estimator has the ability to cope with asynchronous measurements, and to process the data by sets of undefined sizes. It is particularly suitable for fault detection, because the decisions can be based on more data. This is an open door to ro...

We propose a novel kernel estimator of the baseline function in a general highdimensional Cox model, for which we derive non-asymptotic rates of convergence. To construct our estimator, we first estimate the regression parameter in the Cox model via a LASSO procedure. We then plug this estimator into the classical kernel estimator of the baseline function, obtained by smoothing the so-called Br...

Let be a random sample from a normal distribution with unknown mean and known variance The usual estimator of the mean, i.e., sample mean is the maximum likelihood estimator which under squared error loss function is minimax and admissible estimator. In many practical situations, is known in advance to lie in an interval, say for some In this case, the maximum likelihood estimator...

This paper treats data reduction in array processing for the spatially colored noise case. The purpose is to reduce the computational complexity of the applied signal processing algorithms by mapping the data into a space of lower dimension by means of a linear transformation. We discuss ways to implement the transformation and show that it suffices to estimate the array covariance matrix inste...

The measured rate of the tracking sensor becomes biased under some operational situation. For a highly maneuverable aircraft in 3D space, the target dynamics changes from time to time, and the Kalman filter using position measurement only can not be used effectively to reject the rate measurement bias error. To cope with this problem, we present a new algorithm which incorporate FIR-type filter...

Using the modern time-series analysis method in the time domain, based on the autoregressive moving average (ARMA) innovation model and white noise estimator, non-regular descriptor discrete-time stochastic linear systems are researched. Under assumption 1~3, an asymptotically stable reduced-order Wiener state estimator for descriptor systems is given by using projection and block matrix theori...

Nunierically stable and computationally efficient Power System Statc Estimation (PSSE) algorithms are designed using Orthogonalization (QR decomposition) approach. They u3e Givens rotations for orthogonalization which enables sparsity exploitation during factorization of large sparse auginentecl Ja,cobian. Apriori row and column ordering is usiially performed to reduce intermediate a.nd and ove...

Simultaneous Localization and Mapping (SLAM) is concerned with the development of filters to accurately and efficiently infer the state parameters (position, orientation, etc.) of an agent and aspects of its environment, commonly referred to as the map. A mapping system is necessary for the agent to achieve situatedness, which is a precondition for planning and reasoning. In this work, we consi...

Due to its distributed nature, a cyber-physical system is vulnerable to various faults, including sensory integrity attacks. Such faults need to be accounted for in the design of a state estimator. In this paper, we consider sparse sensor faults, in which a small unknown group of sensors can be compromised. We first show a necessary condition that allows the state to be estimated in the presenc...