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.

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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

In this paper, a novel design technique for adaptive stack filters is proposed. The Boolean function of the proposed filters is selected by the controller based on pattern classification. This controller associates an input vector with some cluster according to its pattern and selects the Boolean function corresponding to the cluster. The Boolean functions used in this technique are adjusted by using the adaptive least mean absolute (ALMA) algorithm. As inputs of this controller, both binary and multilevel signals are considered. Finally, a simulation example is given to illustrate the utility of the proposed technique. © 1999 Scripta Technica, Electron Comm Jpn Pt 3, 82(4): 38–47, 1999

Design of adaptive stack filters using pattern classification

The minimization of roundoff noise subject to l/sub 2/-norm dynamic-range scaling constraints in two-dimensional (2-D) state-space digital filters is considered by using joint error feedback and coordinate transformation optimization. An iterative approach for minimizing the roundoff noise under l/sub 2/-norm dynamic-range scaling constraints is developed by jointly optimizing a scalar error-feedback matrix and a coordinate transformation matrix. A numerical example is presented to illustrate the utility of the proposed technique.

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Jointly optimized error feedback and realization for roundoff noise minimization in two-dimensional state-space digital filters

This paper presents a new fuzzy filter structure for edge-preserving smoothing of an image corrupted by impulsive and white Gaussian noise. This filter structure is expressed as an adaptive weighted mean filter that uses fuzzy control. The coefficients of the proposed filter can be varied adaptively by fuzzy control laws based on differences between pixels in the window. The parameter of this filter can be adjusted by learning. Finally, simulation results demonstrate the effectiveness of the proposed technique.

A tunable fuzzy filter based on differences between pixels

Error spectrum shaping in 2‐d recursive digital filters using error feedback with quadrant‐symmetric or antisymmetric coefficients

A procedure is developed for the design of a minimal-order observer capable of estimating the states of a two-dimensional system in the presence of unknown inputs. This is done using singular-value decomposition. The assignment of eigenvalues to the observer is facilitated by choosing the observer structure.

Takao Hinamoto

Papers

Design of adaptive stack filters using pattern classification

Jointly optimized error feedback and realization for roundoff noise minimization in two-dimensional state-space digital filters

A tunable fuzzy filter based on differences between pixels

Error spectrum shaping in 2‐d recursive digital filters using error feedback with quadrant‐symmetric or antisymmetric coefficients

Two-dimensional disturbance decoupled observers†