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 method for steady-state analysis of gradient-based algorithms for a complex coefficient adaptive infinite impulse response (IIR) notch filter is presented. Accurate expressions of estimation mean square error (MSE) for variable coefficients are derived by approximating the power spectral densities (PSD) of estimation errors. Suppression of a complex sinusoidal interference in a direct-sequence quadrature phase shift keying (DS QPSK) system using the algorithms is also presented. Computer simulations are presented to support the validity of the analysis.

Gradient-based algorithms for a complex coefficient adaptive IIR notch filter: steady-state analysis and application

This paper presents a systematic study of pole and zero sensitivity minimization for state-space digital filters in several different yet related settings. First, a new weighted measure for pole and zero sensitivity for state-space digital filters is proposed and the problem of minimizing this measure is investigated. To this end, two efficient iterative techniques for minimizing this measure are developed by employing a quasi-Newton algorithm and relying on a recursive matrix equation, respectively. Furthermore, minimization of the proposed sensitivity measure subject to $l_{2}$ -scaling constraints is examined by extending the two aforementioned solution methods—one converts the constrained optimization problem at hand into an unconstrained problem and solves it using a quasi-Newton algorithm, while the other relaxes the constraints into a single constraint on matrix trace and solves the relaxed problem with an effective matrix iteration scheme. In addition, a simple yet novel method for the minimization of a zero sensitivity measure subject to minimal pole sensitivity is explored by pursuing an optimal coordinate transformation matrix. Simulation studies are presented to demonstrate the validity and effectiveness of the proposed techniques.

Minimization of Weighted Pole and Zero Sensitivity for State-Space Digital Filters

A novel technique for edge-preserving smoothing is developed by using median mean neural hybrid filters. This filter structure is represented by the cascade connection of a median filter, a mean filter, and a neural network. This filter can adapt itself to the various noise environment through the learning of a training image. In the case where a priori data such a training image is unavailable, this filter can be efficiently applied to edge-preserving smoothing for the images degraded by the Gaussian and impulsive noises. Moreover, the structure of the proposed filter is very simple. Finally, the simulation examples show the effectiveness of the proposed filters.

Edge-preserving smoothing using median mean neural hybrid filters

A technique for the restoration of an image degraded by a shift invariant point spread function (SIPSF), and corrupted with white Gaussian noise is developed. For taking account of the effect of edges in an image, two kinds of Hopfield networks are used alternately in the proposed technique. One is for the edge detection in images and another is for the restoration and smoothing of blurred images. For exact edge detection, a Hopfield network considering the line process is proposed. To achieve the fast convergence of optimizing process of restoration and smoothing network, the regularization parameter is chosen to be a sigmoid function in which its iteration number is considered as a variable. An example is shown to illustrate the utility of the proposed technique.

Image restoration by Hopfield networks considering the line process

PROBLEM TO BE SOLVED: To ensure more precise control characteristics depending on a system. SOLUTION: An adjustment means 4 is provided for adjusting PID control parameters of a PID controller 3 according to data in a database 6 storing vectors including features of a control system as information vectors and storing characteristic parameters of the control system. Every time a request point is given as a new information vector, the adjustment means 4 selects information vectors near to the request point from the database 6, generates and stores in the database 6 a PID control parameter corresponding to the request point according to the selected information vectors, and corrects the PID control parameter so as to reduce a control error before setting it in the PID controller 3. COPYRIGHT: (C)2006,JPO&NCIPI

Takao Hinamoto

Papers

Gradient-based algorithms for a complex coefficient adaptive IIR notch filter: steady-state analysis and application

Minimization of Weighted Pole and Zero Sensitivity for State-Space Digital Filters

Edge-preserving smoothing using median mean neural hybrid filters

Image restoration by Hopfield networks considering the line process

Control device, device for adjustment of control parameter, method for adjustment of control parameter, program, and recording medium