We investigate a problem of approximate non-linear sampling recovery of functions on the interval I:=[0,1] expressing the adaptive choice of n sampled values of a function to be recovered, and of n terms from a given family of functions Φ. More precisely, for each function f on I, we choose a sequence ξ = {ξs} s =1 n of n points in I, a sequence a = {as}s=1} n of n functions defined on n and a ...

Sampling rate conversion (SRC) is one of important issues in modern sampling theory. It can be realized by up-sampling, filtering, and down-sampling operations, which need large complexity. Although some efficient algorithms have been presented to do the sampling rate conversion, they all need to compute the N-point original signal to obtain the up-sampling or the down-sampling signal in the tim...

The linear sampling method (LSM) is a qualitative inverse scattering technique that can create good-fidelity imagery at low computational expense. However, it challenging to use in many practical scenarios due its need for wide-angle multistatic-multiview data with dense spatial sampling. We present new LSM formulation imaging conducting targets from more limited sensor distribution. mitigates ...

The linear sampling method is a qualitative procedure for the visualization of both impenetrable and inhomogeneous scatterers, which requires the regularized solution of a linear illposed integral equation of the first kind. An open issue in this technique is the one of determining the optimal scatterer profile from the visualization maps in an automatic manner. In the present paper this proble...

To encode the speech quality with reduce the redundancy within samples that resulted from uniform sampling method like PCM, uniform sampling or non-linear sampling coding methods can be considered. However, it is well known that when conventional sampling methods are applied directly to speech signal, the required amount of data is comparable to or more than that of uniform sampling method in n...

A linear programming formulation of the optimal stopping problem for Markov decision processes is approximated using linear function approximation. Using this formulation, a reinforcement learning scheme based on a primal-dual method and incorporating a sampling device called ‘split sampling’ is proposed and analyzed. An illustrative example from option pricing is also included.

This paper presents the application of both local and global sensitivity analysis techniques to an estimation of the uncertainty of the output of a 3D reaction-diffusion ecological model, which describes the seasonal dynamics of dissolved Nitrogen and Phosphorous, and that of the phytoplanktonic and zooplanktonic communities in the lagoon of Venice. Two sources of uncertainty were taken into ac...

We propose a numerical procedure to design a linear output-feedback controller for a remote linear plant in which the loop is closed through a network. The controller stabilizes the plant in the presence of delays, sampling, and packet dropouts in the (sensor) measurement and actuation channels. We consider two types of control units: anticipative and non-anticipative. In both cases the closedl...