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

The problem of reducing the deviation from a desired transfer function caused by the coefficient quantization errors is investigated for a three-dimensional (3-D) separable in denominator digital filter. To begin with, a 3-D transfer function with separable denominator is represented with the cascade connection of three one-dimensional (1-D) transfer functions by applying a minimal decomposition technique, and the multi-input multi-output (MIMO) 1-D transfer function located in the middle of the cascade connection is realized by a minimal state-space model. Next, the l 2 -sensitivity of the state-space model is analyzed, and the minimization problem of the l 2 -sensitivity subject to l 2 -scaling constraints is formulated. This problem is then converted into an unconstrained optimization problem by using linear-algebraic techniques, and an efficient quasi-Newton algorithm is applied to solve it. A numerical example is presented to illustrate the validity and effectiveness of the proposed technique.

/pdf/reduction-of-l-2-sensitivity-for-three-dimensional-separable-1y9mn4xaql.pdf

Reduction of l 2 -sensitivity for three-dimensional separable-denominator digital filters

The joint optimization problem of high-order error feedback and realization for minimizing roundoff noise at the filter output subject to-scaling constraints for two-dimensional (2-D) separable-denominator digital filters is investigated Linear algebraic techniques that convert the problem at hand into an unconstrained optimization problem are explored, and an efficient quasi-Newton algorithm is then applied to solve the unconstrained optimization problem iteratively Closed-form formulas for fast and accurate gradient evaluation are derived A numerical example is presented to demonstrate the validity and effectiveness of the proposed technique

Roundoff noise minimization for 2-D separable-denominator digital filters using jointly optimal high-order error feedback and realization

An L/sub 2/-sensitivity measure for the closed-loop system containing a state-estimate feedback analog controller is presented in closed form to evaluate the sensitivity of a closed-loop transfer function with respect to the state-estimate feedback controller's coefficients. This is accomplished by using a pure L/sub 2/-norm. An iterative procedure is also developed for constructing state-estimate feedback controller's structures with minimum L/sub 2/-sensitivity. Finally, a numerical example is given to illustrate the utility of the proposed technique.

http://ieeexplore.ieee.org/iel5/8452/26621/01186983.pdf

Optimal synthesis of state-estimate feedback analog controllers with minimum L/sub 2/-sensitivity

When multivalued images are transformed to binary images, the error diffusion method is often used because it is excellent in gray scale representation. When the error diffusion method is applied by means of a raster scan, equal-color regions with all black pixels or all white pixels occur along the edges. This phenomenon has the effect of emphasizing the edges, and the edges may be visually emphasized excessively. In this paper, in order to freely control the equal-color regions, a method that adaptively adjusts the filter coefficients in the error diffusion method based only on the difference from the points to be processed is presented. The filter coefficients are computed by using fuzzy reasoning. According to the proposed method, the width of the equal-color region near a step edge can be controlled adequately by providing appropriate values. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(2): 82–90, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.1135

Adaptive error diffusion using fuzzy reasoning for step edges

Based on the Fornasini-Marchesini second model, a technique is developed for implementing two-dimensional (2D) adaptive state-space filters. First, the relationship between the coefficient sensitivities and the intermediate transfer functions is investigated for the Fornasini-Marchesini second model. A least mean square (LMS) adaptive algorithm is then presented by using new systems that generate the gradient vectors. Finally, a 2-D adaptive line enhancer is constructed by using the 2-D adaptive state-space filter to illustrate the utility of the proposed technique.

http://ieeexplore.ieee.org/iel4/82/13459/00618042.pdf

Takao Hinamoto

Papers

Reduction of l 2 -sensitivity for three-dimensional separable-denominator digital filters

Roundoff noise minimization for 2-D separable-denominator digital filters using jointly optimal high-order error feedback and realization

Optimal synthesis of state-estimate feedback analog controllers with minimum L/sub 2/-sensitivity

Adaptive error diffusion using fuzzy reasoning for step edges

2-D adaptive state-space filters based on the Fornasini-Marchesini second model