Newest processes of CMOS manufacturing allow integrating billions of transistors in a single chip. This huge integration enables to perform complex circuits, which require an energy efficient communication architecture with high scalability and parallelism degree, such as a Network-on-Chip (NoC). However, these technologies are very close to physical limitations implying the susceptibility increase of faults on manufacture and at runtime. Therefore, it is essential to provide a method for efficient fault recovery, enabling the NoC operation even in the presence of faults on routers or links, and still ensure deadlock-free routing even for irregular topologies. A preprocessing approach of the most probable fault scenarios enables to anticipate the computation of deadlock-free routings, reducing the time necessary to interrupt the system operation in a fault event. This work describes a preprocessing technique of fault scenarios based on forecasting fault tendency, which employs a fault threshold circuit and a high-level software that identifies the most relevant fault scenarios. We propose methods for dissimilarity analysis of scenarios based on measurements of cross-correlation of link fault matrices. At runtime, the preprocessing technique employs analytic metrics of average distance routing and links load for fast search of sound fault scenarios. Finally, we use RTL simulation with synthetic traffic to prove the quality of our approach.

Color textures are among the most important visual attributes in image analysis. This paper presents a novel method to analyze color textures by modeling a color image as a graph in two different and complementary manners (each color channel separately and the three color channels altogether) and by obtaining statistical moments from the shortest paths between specific vertices of this graph. Such an approach allows to create a set of feature vectors, which were extracted from VisTex, USPTex, and TC00013 color texture databases. The best classification results were 99.07%, 96.85%, and 91.54% (LDA with leave-one-out), 87.62%, 66.71%, and 88.06% (1NN with holdout), and 98.62%, 96.16%, and 91.34% (LDA with holdout) of success rate (percentage of samples correctly classified) for these three databases, respectively. These results prove that the proposed approach is a powerful tool for color texture analysis to be explored.