In this paper a system capable of obtaining the 3D pose of a mobile robot using a ring of calibrated cameras attached to the environment is proposed. The system robustly tracks point fiducials in the image plane of the set of cameras generated by the robot’s rigid shape in motion. Each fiducial is identified with a point belonging to a sparse 3D geometrical model of robot’s structure. Such mode...

This article describes the use of the cell broadband engine as a high-performance platform for simulating the discharge of a lead acid battery to determine its state of health in an embedded application. The discharging of the battery is modelled as a twodimensional electrochemical process. The model equations are a set of coupled highly non-linear partial differential equations that evolve wit...

A central problem in time series analysis is prediction of a future observation. The theory of optimal linear prediction has been well understood since the seminal work of A. Kolmogorov and N. Wiener during World War II. A simplifying assumption is to assume that one-step-ahead prediction is carried out based on observing the infinite past of the time series. In practice, however, only a finite...

In this paper, we propose a new iteration process to approximate minimizers of proper convex and lower semi-continuous functions and fixed points of λ-hybrid multivalued mappings in Hilbert spaces. We also provide an example to illustrate the convergence behavior of the proposed iteration process and numerically compare the convergence of the proposed iteration scheme with the existing schemes.

In this section we provide a brief summary of the ExpectationMaximization (EM) algorithm. For details and theoretical understanding of the EM algorithm we recommend readers to refer to the following references (Dempster et al. (1977); McLachlan and Krishnan (1997)). The EM algorithm is an elegant and powerful method for finding the maximum likelihood of models with hidden variables. The key con...

Abstract This note considers the momentum method by Polyak and accelerated gradient Nesterov, both without line search but with fixed step length applied to strongly convex quadratic functions assuming that exact gradients are used appropriate upper lower bounds for extreme eigenvalues of Hessian matrix known. Simple 2-d-examples show Euclidean distance iterates optimal solution is non-monotone...

The augmented Lagrangian method (ALM) is a benchmark for solving a convex minimization model with linear constraints. We consider the special case where the objective is the sum of m functions without coupled variables. For solving this separable convex minimization model, it is usually required to decompose the ALM subproblem at each iteration into m smaller subproblems, each of which only inv...