Infectious diseases both within human and animal populations often pose serious health and socioeconomic risks. From a statistical perspective, their prediction is complicated by the fact that no two epidemics are identical due to changing contact habits, mutations of infectious agents, and changing human and animal behaviour in response to the presence of an epidemic. Thus model parameters gov...

In this paper, a pixel-based background modeling method, which uses nonparametric kernel density estimation, is proposed. To reduce the burden of image storage, we modify the original KDE method by using the first frame to initialize it and update it subsequently at every frame by controlling the learning rate according to the situations. We apply an adaptive threshold method based on image cha...

Abstrad-A modular approach to adaptive control of nonlinear systems introduced previously is presented in a general framework. This approach abandons the certainty equivalence principle ubiquitous in the estimation-based approach. The adaptive stabilization problem is studied in the setting of control Lyapunov functions, and is reduced to the problem of input-to-state stabilization with respect...

We study a general class of statistical detection problems where the underlying objective is to detect items belonging to a rare class from a very large database. We propose a computationally efficient method to achieve this goal. Our method consists of two steps. In the first step we estimate the density function of the rare class alone with an adaptive bandwidth kernel density estimator. The ...

We consider the possibility of optimal choice of observation window in the problem of parameter estimation by the observations of an inhomogeneous Poisson process. A minimax lower bound is proposed for the risk of estimation under an arbitrary choice of observation window. Then the adaptive procedure is proposed which is asymptotically e cient in the sense of this bound. Let X be a separable me...

Department of Mathematical Sciences, University of Alberta, Edmonton, Alberta, T6G 2G1 Canada Weapons Systems Division, Defence Science Technology Organisation, P.O. Box 1500, Salisbury, SA, 5108, Australia Department of Systems Engineering & Cooperative Research Centre for Robust and Adaptive Systems, Research School of Information Sciences and Engineering, Australian National University, Canb...

We present two new hyperbolic source probability models to effectively represent sub-gaussian and super-gaussian families of sources for dynamic and convolutive Blind Source Recovery (BSR). Both models share a common boundary for the gaussian density function. The proposed hyperbolic probability model for the sub-gaussian densities is an extension of the Pearson density model. The model can rep...

against the range-dependent accuracy of the tracking radar measurements including range, elevation and bearing angles, a new hybrid adaptive kalman filter is proposed to enhance the performance of the radar aided strapdown inertial navigation system (ins/radar). this filter involves the concept of residual-based adaptive estimation and adaptive fading kalman filter and tunes dynamically the fil...

In this paper, we study adaptive finite element discretisation schemes for a class of parameter estimation problem. We propose to use adaptive multi-meshes in developing efficient algorithms for the estimation problem. We derive equivalent a posteriori error estimators for both the state and the control approximation, which particularly suit an adaptive multi-mesh finite element scheme. The err...

We investigate the potential of adaptive equalization techniques to mitigate inter-channel nonlinear interference noise (NLIN). We derive a lower bound on the mutual information of a system using adaptive equalization, showing that the channel estimation error determines the equalizer’s performance. We develop an adaptive equalization scheme which uses the statistics of the NLIN to obtain optim...