A modified (G'/G)- expansion method is proposed to construct hyperbolic, trigonometric and rational function solutions of nonlinear evolution equations which can be thought of as the generalization of the (G'/G)- expansion method given recently by M.Wang et al . To illustrate the validity and advantages of this method, the (1+1)-dimensional Hirota-Ramani equation and the (2+1)-dimensional Breaking soliton equation are considered and more general traveling wave solutions are obtained. It is shown that the proposed method provides a more general powerful mathematical tool for solving nonlinear evolution equations in mathematical physics.

The process of removing noise from the original image is still a demanding problem for researchers. There have been several algorithms and each has its assumptions, merits, and demerits. The prime focus of this paper is related to the pre processing of an image before it can be used in applications. The pre processing is done by de-noising of images. In order to achieve these de-noising algorithms, filtering approach and wavelet based approach are used and performs their comparative study. Different noises such as Gaussian noise, salt and pepper noise, speckle noise are used. The wavelet based approach has been proved to be the best in de-noising images corrupted with Gaussian noise, salt and pepper noise and speckle noise A quantitative measure of comparison is provided by the parameters like Peak signal to noise ratio, Root mean square error, Entropy and Correlation of the image.

This paper proposed an improved Automatic License Plate Recognition (ALPR) system for all types of Vietnam license plates (LP), which consists of three modules: license plate location (LPL), characters segmentation and characters recognition. In the LPL module, we have combined edge detection, image subtraction, mathematical morphology, radon transform, interpolation and specific characteristics of Vietnam LP to locate exact LP region. In the characters segmentation module, we have used peak-to-valley method and statistical parameters of Vietnam LP to segment characters & numbers in one-row & two-row types of Vietnam LP. In the characters recognition module, we have used a Multilayer Perceptron (MLP) neural network and back-propagation (BP) algorithm to recognize characters & numbers of Vietnam LP, two MLP networks used independent for characters & numbers and MLP has trained with noises in the training task. Character & number images processed by the pre-processing task, which obtained high quality of character & number images for the using network task to improve accuracy of the system. We have implemented our ALPR system on 700 Vietnam vehicle images taken from actual system with different conditions such as lightening conditions (night & day), license angles, illumination, size and type, colors and reflected light. Our method is more effective than some existing methods and efficiency of computing time & accuracy is improved and very satisfied for all types of Vietnam LP and Vietnam environment.

This paper deals with an alternative approach for solving the PDE-constrained optimization problems. For this purpose, the problem has been discretized with the help of finite difference method. Then the reduced problem has been solved by advanced real coded genetic algorithm with ranking selection, whole-arithmetic crossover and non uniform mutation. The proposed approach has been illustrated with a numerical example. Finally, to test the performance of the algorithm, sensitivity analyses have been performed on objective function values with respect to different parameters of genetic algorithm.

Genetic algorithms (GA) are evolutionary techniques that used crossover and mutation operators to solve optimization problems using a survival of the fittest idea. They have been used successfully in a variety of different problems, including the traveling salesman problem. The main idea of Traveling Salesman Problem (TSP) is to find the minimum traveling cost for visiting cities; the salesman must visit each city exactly once and return to the starting point of origin. Genetic algorithms are search methods that employ processes found in natural biological evolution. These algorithms search on a given population of potential solutions to find those that pass some specifications or criteria. In this paper, we apply modified genetic algorithm methodology for finding near-optimal solutions for TSP problem using shared neighbours to insure that the closest cities to have the highest priorities to be carried out to the next generation.

There are different university offering different types of courses over several years, and the biggest issue with that is how to get information to make course more effective. In real life these types of database usually contain temporal coherences, which cannot be captured by means of standard association rule mining. Here temporal Association rule mining can be used to evaluate the course effectiveness and helps to look for in regards to changes in performance of the course from time to time. For Example there is a course offering different topics. We can say that the topics having full attendance are totally effective and carry no hesitation information. While there are some topics which are almost fully attendant carry some hesitation information. This hesitation information is valuable and can be used to make the course more effective and interesting. Thus there is need for developing temporal vague association rule algorithms that reveal such hesitation information and temporal coherences within this data.

Combining the advantage of spectral-gradient method, a spectral conjugate gradient method for the global optimization is presented. This method has the property that the generated search direction is sufficiently descent without utilizing the line search. The proof of the convergence of the proposed method is given. Numerical experiments show that the method is efficient and feasible.