We present an hp-adaptive virtual element method (VEM) based on the hypercircle of Prager and Synge for approximation solutions to diffusion problems. introduce a reliable efficient posteriori error estimator, which is computed by solving auxiliary global mixed problem. show that VEM satisfies discrete inf-sup condition with constant independent discretization parameters. Furthermore, we constr...

A novel adaptive spectral method has been recently developed to numerically solve partial differential equations (PDEs) in unbounded domains. To achieve accuracy and improve efficiency, the relies on dynamic adjustment of three key tunable parameters: scaling factor, a displacement basis functions, expansion order. In this paper, we perform first numerical analysis using generalized Hermite fun...

This paper presents new adaptive filtering techniques used in speech enhancement system. Adaptive filtering schemes are subjected to different trade-offs regarding their steady-state misadjustment, speed of convergence, and tracking performance. Fractional Least-Mean-Square (FLMS) is a new adaptive algorithm which has better performance than the conventional LMS algorithm. Normalization of LMS ...

In dealing with model predictive controllers (MPC), controller tuning is a key design step. Various tuning methods are proposed in the literature which can be categorized as heuristic, numerical and analytical methods. Among the available tuning methods, analytical approaches are more interesting and useful. This paper is based on a proposed analytical MPC tuning approach for plants can be appr...

numerical analyses have shown that successful flow simulations and the accuracy of solution noticeably depend on the number of nodes used in computational meshing. a suitable meshing should have the capability of adapting with main flow parameters. because the number of total nodes that can be used in numerical simulation is limited, making such grid for complex flows is almost difficult, if it...

Many applications in machine learning or signal processing involve nonsmooth optimization problems. This nonsmoothness brings a low-dimensional structure to the optimal solutions. In this paper, we propose randomized proximal gradient method harnessing underlying structure. We introduce two key components: (i) random subspace algorithm; and (ii) an identification-based sampling of subspaces. Th...