Ameer Noaman Abed Onaizah

Texture analysis is gaining popularity among the scientific community. A wide variety of applications use texture analysis method. Texture analysis methods can be used for image segmentation, pattern analysis and pattern classification tasks. The application areas range from remote sensing, biomedical imaging, image synthesis, image inpainting and image processing. However, the preliminary step in all these applications refers to the extraction of intricate features from the given image. As a result, a wide verity of feature extraction methods exists in the literature. All the feature extraction methods have their own advantages and shortfalls. For example, some of the methods are computationally expensive, some are rotation and scale invariant whereas the others are easy to implement. This article provides an insight regarding different texture feature extraction techniques. The article bifurcate these techniques into different techniques according to their working principles. Besides provision of the basic working principle of every technique, the article provides an insight regarding their advantages and shortfalls. Moreover, this article considers deep learning and entropy based methods interesting for texture evaluation. Besides, the article also proposes a thorough study of these methods in texture analysis.

In this paper, we present TriBA (Triplet based) NoC (network on-chip) architecture with 40 nodes referred as TriBA-NoC. TriBA-NoC is implemented in a multi-core 40 tile DDR-3 hardware. The nodes in the TriBA-NoC are connected through recursive triplets. We carried out the performance analysis in network simulator (NS-3) using several perspectives. We adopted TR-132 shortest routing path algorithm with novel router architecture for TriBA-NoC systems. The results of average packet latency for various patterns of traffic of a 40-core TriBA-NoC model are demonstrated. The results of the proposed 40-core TriBA-NoC model achieved better performances while compared to the TriBA.