Nikos E. Mastorakis

Design of two-dimensional recursive filters using genetic algorithms

In this paper, we examine a new design method for two-dimensional (2-D) recursive digital filters using genetic algorithms (GAs). The design of the 2-D filter is reduced to a constrained minimization problem the solution of which is achieved by the convergence of an appropriate GA. Theoretical results are illustrated by a numerical example. Also, comparison with the results of some previous design methods is attempted.

Design of two-dimensional recursive filters by using neural networks

A new design method for two-dimensional (2-D) recursive digital filters is investigated. The design of the 2-D filter is reduced to a constrained minimization problem the solution of which is achieved by the convergence of an appropriate neural network. The method is tested on a numerical example and compared with previously published methods when applied to the same example. Advantages of the proposed method over the existing ones are discussed as well.

New fast normalized neural networks for pattern detection

Neural networks have shown good results for detecting a certain pattern in a given image. In this paper, fast neural networks for pattern detection are presented. Such processors are designed based on cross correlation in the frequency domain between the input image and the input weights of neural networks. This approach is developed to reduce the computation steps required by these fast neural networks for the searching process. The principle of divide and conquer strategy is applied through image decomposition. Each image is divided into small in size sub-images and then each one is tested separately by using a single fast neural processor. Furthermore, faster pattern detection is obtained by using parallel processing techniques to test the resulting sub-images at the same time using the same number of fast neural networks. In contrast to fast neural networks, the speed up ratio is increased with the size of the input image when using fast neural networks and image decomposition. Moreover, the problem of local sub-image normalization in the frequency domain is solved. The effect of image normalization on the speed up ratio of pattern detection is discussed. Simulation results show that local sub-image normalization through weight normalization is faster than sub-image normalization in the spatial domain. The overall speed up ratio of the detection process is increased as the normalization of weights is done offline.

Stability of multidimensional systems using genetic algorithms

The study of the stability of m-dimensional systems is a difficult one especially when m /spl ges/ 3. There exist only a few results in the literature and unfortunately, there does not exist any practical criterion. In this work, the stability of an m-dimensional system is dealt as a minimization problem of the absolute value of its characteristic polynomial over the boundaries of its variables (i.e., on the m unit circles). This minimization problem is solved by using genetic algorithms (GAs). Using GAs we obtain, in general, better results than other methods of minimization (numerical techniques, neural networks, etc.). Numerical examples are presented.

Fast Code Detection Using High Speed Time Delay Neural Networks

This paper presents a new approach to speed up the operation of time delay neural networks for fast code detection. The entire data are collected together in a long vector and then tested as a one input pattern. The proposed fast time delay neural networks (FTDNNs) use cross correlation in the frequency domain between the tested data and the input weights of neural networks. It is proved mathematically and practically that the number of computation steps required for the presented time delay neural networks is less than that needed by conventional time delay neural networks (CTDNNs). Simulation results using MATLAB confirm the theoretical computations.

Image Segmentation with Improved Artificial Fish Swarm Algorithm

Some improved adaptive methods about step length are proposed in the Artificial Fish Swarm Algorithm (AFSA), which is a new heuristic intelligent optimization algorithm. The experimental results show that proposed methods have better performances such as good and fast global convergence, strong robustness, insensitivity to initial values, and simplicity of implementation. We apply the method in the image processing for the multi-threshold image segmentation compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The properties are discussed and analysed at the end.

A genetic algorithm approach to the problem of factorization of general multidimensional polynomials

In this paper, a solution to the problem of the multidimensional (m-D) polynomial factorization is attempted by using genetic algorithms (GAs). The proposed method is based on an appropriate minimization of the norm of the difference between the original polynomial and its desirable factorized form. Using GAs, we can obtain better results than with other methods of minimization (numerical techniques, neural networks, etc.). The present methodology, which can also be used for every type of m-D factorization, is illustrated by means of a numerical example.

A method for computing the 2-D stability margin

In this brief, the margin of stability of two-dimensional (2D) discrete systems is considered. A new method to compute the stability margin of 2-D continuous systems is provided. Illustrative examples are also included.

A new method for computing the stability margin of two-dimensional continuous systems

This paper presents a new method for computing the stability margin of two-dimensional (2-D) continuous systems. The method is based on the computation of the Hermite matrix in 2-D continuous systems, one of its partial derivatives and their resultant. The theoretical result is illustrated by examples.

New necessary stability conditions for 2-D systems

In this paper, some interesting necessary conditions for the stability of two-dimensional (2-D) systems are presented. The inversion of these conditions gives sufficient conditions for the instability of the same systems. The proof of these conditions is given. A comparison with some other known criteria is given.