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

This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

Digital filters can be broadly classified into two groups: recursive (infinite impulse response (IIR)) and non-recursive (finite impulse response (FIR)). An IIR filter can provide a much better performance than the FIR filter having the same number of coefficients. However, IIR filters might have a multi-modal error surface. Therefore, a reliable design method proposed for IIR filters must be based on a global search procedure. Artificial bee colony (ABC) algorithm has been recently introduced for global optimization. The ABC algorithm simulating the intelligent foraging behaviour of honey bee swarm is a simple, robust, and very flexible algorithm. In this work, a new method based on ABC algorithm for designing digital IIR filters is described and its performance is compared with that of a conventional optimization algorithm (LSQ-nonlin) and particle swarm optimization (PSO) algorithm.

/pdf/a-new-design-method-based-on-artificial-bee-colony-algorithm-4h83c1feyi.pdf

A new design method based on artificial bee colony algorithm for digital IIR filters

Brief paper: H∞ filtering for 2D Markovian jump systems

digital signal processing a computer based approach is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the digital signal processing a computer based approach is universally compatible with any devices to read.

Digital Signal Processing A Computer Based Approach

A two-dimensional (2-D) Lyapunov equation with constant coefficients is considered for the Fornasini-Marchesini second local state-space (LSS) model. First, a novel criterion relating to the Lyapunov equation is presented that sufficiently guarantees the asymptotic stability. A sufficiency condition that ensures the absence of limit cycles is also given. Next, the above stability condition is incorporated into the 2-D filter structure to design 2-D state-space digital filters with guaranteed asymptotic stability. An efficient method is then developed for computing the characteristic polynomial and the inverse of the system matrix. Finally, two numerical examples are given to design 2-D stable state-space digital filters. >

2-D Lyapunov equation and filter design based on the Fornasini-Marchesini second model

Based on the Fornasini-Marchesini second local state-space (LSS) model, criteria that sufficiently guarantee the asymptotic stability of 2-D discrete systems are given. A sufficient condition for a 2-D nonlinear discrete system to be free of overflow oscillations is then shown in the case when a 2-D discrete system is employed by saturation arithmetic. Finally, an upper bound on parameter variations which guarantees the asymptotic stability of a perturbed 2-D discrete system is considered. It is shown that the upper bound stated here is less conservative than the existing ones.

Stability of 2-D discrete systems described by the Fornasini-Marchesini second model

In this paper, minimax design of infinite-impulse-response (IIR) filters with prescribed stability margin is formulated as a conic quadratic programming (CQP) problem. CQP is known as a class of well-structured convex programming problems for which efficient interior-point solvers are available. By considering factorized denominators, the proposed formulation incorporates a set of linear constraints that are sufficient and near necessary for the IIR filter to have a prescribed stability margin. A second-order cone condition on the magnitude of each update that ensures the validity of a key linear approximation used in the design is also included in the formulation and eliminates a line-search step. Collectively, these features lead to improved designs relative to several established methods. The paper then moves on to extend the proposed design methodology to quadrantally symmetric two-dimensional (2-D) digital filters. Simulation results for both one-dimensional (1-D) and 2-D cases are presented to illustrate the new design algorithms and demonstrate their performance in comparison with several existing methods.

/pdf/optimal-design-of-iir-digital-filters-with-robust-stability-2hpqfh8bay.pdf

Optimal design of IIR digital filters with robust stability using conic-quadratic-programming updates

Since Lim's paper (see IEEE Trans. Circuits Syst., vol.33, p. 357-364, Apr. 1986) on the frequency-response-masking (FRM) technique for the design of finite-impulse response digital filters with very small transition widths, the analysis and design of FRM filters has been a subject of study. In this paper, a new optimization technique for the design of various FRM filters is proposed. Central to the new design method is a sequence of linear updates for the design variables, with each update carried out by semidefinite programming. Algorithmic details for the design of basic and multistage FRM filters are presented to show that the proposed method offers a unified design framework for a variety of FRM filters. Design simulations are included to illustrate the proposed algorithms and to evaluate the design performance in comparison with that of several existing methods.

/pdf/optimal-design-of-frequency-response-masking-filters-using-30aub0wohw.pdf

Optimal design of frequency-response-masking filters using semidefinite programming

A novel expression for the evaluation of L/sub 2/-sensitivity is developed for the cases of linear discrete-time systems, linear continuous-time systems, and two-dimensional (2-D) state-space digital filters. This is accomplished by introducing the concept of general controllability and observability Gramians in each case. Moreover, the L/sub 2/-sensitivity measures obtained here contain the conventional L/sub 1//L/sub 2/-sensitivity measures as a special case. An iterative procedure for constructing the optimal coordinate transformation matrix that minimizes the L/sub 2/-sensitivity measure is then presented in each case. This procedure is advantageous since the initial estimate and the estimate at each iteration can be calculated analytically. Finally, three numerical examples are given to illustrate the utility of the proposed techniques.

http://www.ece.uvic.ca/~wslu/Publications/Lu-Journal/J7.pdf

Analysis and minimization of L/sub 2/-sensitivity for linear systems and two-dimensional state-space filters using general controllability and observability Gramians

Takao Hinamoto

Papers

2-D Lyapunov equation and filter design based on the Fornasini-Marchesini second model

Stability of 2-D discrete systems described by the Fornasini-Marchesini second model

Optimal design of IIR digital filters with robust stability using conic-quadratic-programming updates

Optimal design of frequency-response-masking filters using semidefinite programming

Analysis and minimization of L/sub 2/-sensitivity for linear systems and two-dimensional state-space filters using general controllability and observability Gramians