The fundamental problem of identifying a linear time-invariant system, from measured samples of its output response to a known input, is one that impacts on many important fields of interest. Naturally, it has a long and continuing history, but our purpose is to break fresh ground by utilizing a new and simple deterministic theory founded squarely on wellestablished passive network concepts. Sp...

The hyperplane approximation technique is a recent popular approach to motion estimation applied to a range of computer vision techniques such as active appearance models and template matching. Simply, the method learns a linear relationship between artificially applied parametric motion perturbations and the consequent induced pixel greylevel differences. Once trained, solving for any new moti...

The combined effects of nanoparticle and magnetic field on the nonlinear Jeffery-Hamel flow are analyzed in the present study. The basic governing equations are solved analytically to nonlinear ordinary differential equation using perturbation method together with a semi-numerical analytical technique called Hermite- Padé approximation. The obtained results are well agreed with that of the Adom...

in this article, by using chebyshev’s polynomials and chebyshev’s expansion, we obtain the best uniform polynomial approximation out of p2n to a class of rational functions of the form (ax2+c)-1 on any non symmetric interval [d,e]. using the obtained approximation, we provide the best uniform polynomial approximation to a class of rational functions of the form (ax2+bx+c)-1 for both cases b2-4a...

fractional calculus has been used to model the physical and engineering processes that have found to be best described by fractional differential equations. for that reason, we need a reliable and efficient technique for the solution of fractional differential equations. the aim of this paper is to present an analytical approximation solution for linear and nonlinear multi-order fractional diff...

Interactive dynamic influence diagrams (I-DIDs) are graphical models for sequential decision making in uncertain settings shared by other agents. Algorithms for solving I-DIDs face the challenge of an exponentially growing space of candidate models ascribed to other agents, over time. We formalize the concept of a minimal model set, which facilitates qualitative comparisons between different ap...

In [10, 11] we proposed a technique to approximate Hybrid Automata (HA) with Polynomial HA. The idea was to replace functions appearing in formulae with their Taylor Polynomials. Here we extend this technique to HA with formulae admitting integral functions. We prove that we get over-approximations of the original HA. We study the conditions ensuring that: 1. the “distance” between the formulae...

We show that the Laplace approximation of a supremum by L-norms has interesting consequences in optimization. For instance, the logarithmic barrier functions (LBF) of a primal convex problem P and its dual P∗ appear naturally when using this simple approximation technique for the value function g of P or its Legendre-Fenchel conjugate g∗. In addition, minimizing the LBF of the dual P∗ is just e...

We present a technique to evaluate the approximation ratio on random instances of the Minimum Energy Broadcast Problem in Ad-Hoc Radio Networks which is known to be NP-hard and approximable within 12. Our technique relies on polynomial-time computable lower bound on the optimal cost of any instance. The main result of this paper is that the approximation ratio has never achieved a value greater...