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In this research it is applied a computer vision system by using artificial neural network for the identification and defect detection on several samples of the available textile's woven fabrics. Several textural features that extracted by the Neigboring Greylevel Dependence Matrix (NGDM), and GreylevelRun Length Matrix (GRLM), are used as input data for the network, which was trained by the backpropagation method. The results of experiment indicated that the system can identi& three product-types i.e. plain, twill, and sateen weaves, and detect several defects :pick's broken, pick's inhomogeinity, reed-mark, and dirties within more than 80% degree of correctness.

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It is widely known that scale-free networks are robust against random node removal, which is one of major interesting findings in network science. This suggests that, for instance, communication networks such as the Internet is robust against random node failures caused by breakdowns and/or malicious attacks if their network topologies are scale-free networks. Generally, the ratio of failed devices (e.g., routers) to operational devices is not extremely high. In this paper, we revisit the robustness of complex networks against random node removal. Through simulations, we compare the robustness of scale-free and non-scale-free networks against random node removal as well as random edge removal. Our findings include that, contrary to common understanding, non-scale-free networks are more robust than scale-free networks except under extremely high node removal ratio. We also show that the robustness of non-scale-free networks can be further improved by bounding the minimum node degree of those networks.

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