Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

The work is giving estimations of the discrepancy between solutions of the initial and the homogenized problems for a one{dimensional second order elliptic operators with random coeecients satisfying strong or uniform mixing conditions. We obtain several sharp estimates in terms of the corresponding mixing coeecient. Abstract. In the theory of homogenisation it is of particular interest to determine the classes of problems which are stable on taking the homogenisation limits. A notable situation where the limit enlarges the class of original problems is known as memory (nonlocal) eeects. A number of results in that direction has been obtained for linear problems. Tartar (1990) innitiated the study of the eeective equation corresponding to nonlinear equation: @ t u n + a n u 2 n = f: Signiicant progress has been hampered by the complexity of required computations needed in order to obtain the terms in power{series expansion. We propose a method which overcomes that diiculty by introducing graphs representing the domain of integration of the integrals in each term. The graphs are relatively simple, it is easy to calculate with them and they give us a clear image of the form of each term. The method allows us to discuss the form of the eeective equation and the convergence of power{series expansions. The feasibility of our method for other types of nonlinearities will be discussed as well.

Applied Mathematics and Computation

Identification methods for Hammerstein nonlinear systems

Reconstruction of continuous-time systems from their non-uniformly sampled discrete-time systems

Parameter estimation with scarce measurements

Iterative solutions of the generalized Sylvester matrix equations by using the hierarchical identification principle

This technical note studies identification problems for dual-rate sampled-data linear systems with noises. A hierarchical least squares (HLS) identification algorithm is presented to estimate the parameters of the dual-rate ARMAX models. The basic idea is to decompose the identification model of a dual-rate system into several sub-identification models with smaller dimensions and fewer parameters. The proposed algorithm is more computationally efficient than the recursive least squares (RLS) algorithm since the RLS algorithm requires computing the covariance matrix of large sizes, while the HLS algorithm deals with the covariance matrix of small sizes. Compared with our previous work, a detailed study of the HLS algorithm is conducted in this technical note. The performance analysis and simulation results confirm that the estimation accuracy of the proposed algorithm are close to that of the RLS algorithm, but the proposed algorithm retains much less computational burden.

Hierarchical Least Squares Identification for Linear SISO Systems With Dual-Rate Sampled-Data

In this paper, we present a gradient based iterative algorithm for solving general linear matrix equations by extending the Jacobi iteration and by applying the hierarchical identification principle. Convergence analysis indicates that the iterative solutions always converge fast to the exact solutions for any initial values and small condition numbers of the associated matrices. Two numerical examples are provided to show that the proposed algorithm is effective.

Gradient based iterative solutions for general linear matrix equations

This paper considers the problems of parameter identification and output estimation with possibly irregularly missing output data, using output error models. By means of an auxiliary model (or reference model) approach, we present a recursive least-squares algorithm to estimate the parameters of missing data systems, and establish convergence properties for the parameter and missing output estimation in the stochastic framework. The basic idea is to replace the unmeasurable inner variables with the output of an auxiliary model. Finally, we test the effectiveness of the algorithm with an example system. Copyright © 2009 John Wiley & Sons, Ltd.

Least-squares parameter estimation for systems with irregularly missing data

This paper is concerned with the numerical solutions to the linear matrix equations A"1XB"1=F"1 and A"2XB"2=F"2; two iterative algorithms are presented to obtain the solutions. For any initial value, it is proved that the iterative solutions obtained by the proposed algorithms converge to their true values. Finally, simulation examples are given to verify the proposed convergence theorems.

Jie Ding

Papers

Iterative solutions of the generalized Sylvester matrix equations by using the hierarchical identification principle

Hierarchical Least Squares Identification for Linear SISO Systems With Dual-Rate Sampled-Data

Gradient based iterative solutions for general linear matrix equations

Least-squares parameter estimation for systems with irregularly missing data

Iterative solutions to matrix equations of the form AiXBi=Fi