In this paper, a new method is presented for the detection of defects in random textures. In the training stage, the feature vectors of the normal textures’ images are extracted by using the optimal response of Gabor wavelet filters, and their probability density is estimated by means of the Gaussian Mixture Model (GMM). In the testing stage, similar to the previous stage,at first, the feature vectors corresponding to local neighborhoods of each pixel of the image under inspection are extracted. Then, by computing the likelihood of the test image’s feature vectors’ belonging to the parameters of the GMM, they are compared with a threshold value. Finally, the defective regions are localized in a defect map. The proposed algorithm was evaluated on a set of grayscale ceramic tile images with random textures. The simulations indicate that in comparison with the previous methods, the proposed algorithm enjoys an acceptable computational volume and accuracy in the detection of texture defects. تفاصيل المقالة

This paper presents an accurate Differential Global Positioning System (DGPS) using multi-layered Neural Networks (NNs) based on the Back Propagation (BP) and Imperialistic Competition Algorithm (ICA) in order to predict the DGPS corrections for accurate positioning. Simulation results allowed us to optimize the NN performance in term of residual mean square error. We compare results obtained by the NN technique with BP and ICA. Results show a good improvement obtained by the application of the NN trained by the ICA. The experimental results on measurement data demonstrate that the prediction total RMS error using NN trained by the ICA learning algorithm are 0.8273 and 0.7143 m, before and after selective availability, respectively. تفاصيل المقالة

Due to the complex physical properties of the detected targets using sonar systems, identification and classification of the actual targets is among the most difficult and complex issues of this field. Considering the characteristics of the detected targets and unique capabilities of the intelligent methods in classification of their dataset, these methods seem to be the proper choice for the task. In recent years, neural networks and support vector machines are widely used in this field. Linear methods cannot be applied on sonar datasets because of the existence of higher dimensions in input area, therefore, this paper aims to classify such datasets by a method called Online Multi Kernel Classification (OMKC). This method uses a pool of predetermined kernels in which the selected kernels through a defined algorithm are combined with predetermined weights which are also updated simultaneously using another algorithm. Since the sonar data is associated with higher dimensions and network complexity, this method has presented maximum classification accuracy of 97.05 percent. By reducing the size of input data using genetic algorithm (feature selection) and statistical moments (feature extraction), eliminating the existing redundancy is crucial; so that the classification accuracy of the algorithm is increased on average by 2% and execution time of the algorithm is declined by 0.1014 second at best. تفاصيل المقالة