This study employed the use of Cellular Neural Networks (CNN) for Edge Detection in images due to its high operational speed. The process of edge detection is unavoidable in many image processing tasks such as obstacle detection and satellite picture processing .The conventional edge detector models such as Sobel Operator, Robert Cross, among which Canny is the best, have high computational time. The CNN Model is a class of Differential Equation that has been known to have many application areas and high operational speed. The work investigated four parameters: resolutions, processing time, false alarm rate, and usability for performance evaluation. The CNN Model was modified by using hyperbolic tangent (tanh x) and Von Neumann Neighborhood. The modified CNN Model and enhanced Canny Model were implemented using MATLAB 7.0 running on Pentium III and 128 MB RAM Personal Computer. A series of images served as input for both Canny and modified CNN Model. With several images tested, the overall results indicated that the two models have similar resolutions with average computational time of 1.1078 seconds and 2.293 seconds for CNN-based and Enhanced Canny Model respectively. The hyperbolic entry a22 of the cloning template A made our work fully controllable since max(tanh x) = +1 and min(tanh x) = -1. A consideration of the set of digital images showed that edge maps which result from Canny Model have adjacent boundaries that tend to merge. The CNN model obviously produced the optimum edge map with 2FP    edges that are one-pixel wide and unbroken. The false alarm rate of noise variance probability for which an edge detector can easily declare an edge pixel given that there is no edge, showed the value 0.8525 for CNN Model and 0.4595 for Canny Model. The CNN parameters can be adjusted and modeled to solve Partial Differential Equations and Maximum likelihood problems for edge detection while Canny Model cannot be easily adjusted for any other functionality. The CNN-based edge detector performed better than the popular canny operator in terms of the computational time required, usability, and false alarm rate.

This paper presents anti-synchronization of two different chaotic systems using optimal control method. The proposed technique is applied to achieve chaos anti-synchronization for the new four- dimensional and hyperchaotic Lü systems with fully unknown parameters. Numerical simulations results are presented to demonstrate the effectiveness of the method.

Statistical Learning Theory is commonly regarded as a sound framework within which we handle a variety of learning problems in presence of small size data samples. It has become a rapidly progressing research area in machine learning. The theory is based on real random samples and as such is not ready to deal with the statistical learning problems involving complex fuzzy random samples, which we may encounter in real world scenarios. This paper explores statistical learning theory based on complex fuzzy random samples. Firstly, the definition of complex fuzzy random variable is introduced. Next the concepts and some properties of the mathematical expectation and independence of complex fuzzy random variables are provided. Secondly, the concepts of annealed entropy, growth function and VC dimension of measurable complex fuzzy set valued functions are proposed, and the bounds on the rate of uniform convergence of learning process based on complex fuzzy random samples are constructed. Thirdly, on the basis of these bounds, the idea of the complex fuzzy structural risk minimization principle is presented. Finally, the consistency of this principle is proven and the bound on the asymptotic rate of convergence is derived.

Purpose of this paper is to solve a nonstandard optimal control problem governed by ordinary differential equation by finite element method. In the first part of the paper, we describe the method of discretisation of continuous optimal control problem and then linearize the problem and obtain a linear programming. By solving the linear programming, the control and state functions and the value of objective function are obtained.

It is well known that low bit rate block-based discrete cosine transform coded images exhibits visually annoying coding artifacts. It is of interest to be able to numerically assess the degree of blocking artifacts as it plays an important role in the design, optimization and assessment of image and video coding systems. A novel algorithm for image blocking artifact detection is presented in this paper. Experimental results illustrating the performance of proposed method are presented and evaluated. Our experiment results show that the proposed method of measuring blocking artifacts exhibits satisfactory performance as compared to other post-processing method’s/techniques and is very efficient and stable since the signal need not be compressed/decompressed.

Image restoration is an ill-posed inverse problem, which has been introduced the regularization method to suppress over-amplification. In this paper, we propose to apply the iterative regularization method to the image restoration problem and present a nested iterative method, called iterative conjugate gradient regularization method. Convergence properties are established in detail. Based on [6], we also simultaneously determine the regularization parameter based on the restored image at each step. Simulation results show that the proposed iterative regularization method is feasible and effective for image restoration.

Recently, various papers investigated the geometry features, synchronization and control of complex network provided with certain topology. While, sometimes the exact topology of a network is unknown or uncertain. Using Lyapunov theory, we propose an adaptive feedback controlling method to identify the exact topology of a rather general weighted complex dynamical network model. By receiving the network nodes evolution, the topology of such kind of network with identical or different nodes, or even with switching topology can be monitored. Experiments show that the methods presented in this paper are of high accuracy with good performance.

In general, textiles are perceived as soft materials and structures and textile fibres can be organised into fabrics of different complexities to form new materials and structures. Textiles are widely used in many applications, ranging from clothing to advanced technical applications such as textile composites for the aerospace and other industries. Accurate understanding the relationships between the construction and technical behaviour of textiles is the key in engineering textile materials for the intended applications. This keynote paper introduces the modelling techniques for different textile materials and structures and reports on the latest progress in modelling textiles for ballistic protection. In this area of work, it is shown that the modelling results agreed with the experimental work well and modelling tool can be used to guide the ballistic materials effectively.