Problem statement: In this study a skin disease diagnosis system was developed and tested. The system was used for diagnosis of psoriases skin disease. Approach: Present study relied on both skin color and texture features (features derives from the GLCM) to give a better and more efficient recognition accuracy of skin diseases. We used feed forward neural networks to classify input images to be psoriases infected or non psoriasis infected. Results: The system gave very encouraging results during the neural network training and generalization face. Conclusion: The aim of this worked to evaluate the ability of the proposed skin texture recognition algorithm to discriminate between healthy and infected skins and we took the psoriasis disease as example.
This study proposes a new method for designing adaptive routing algorithms for three-dimensional (3D) networks-onchip (NoCs). This method is based on extending the existing 2D turn model adaptive routing to a 3D scenario. A 3D planebalanced approach with maximal degree of adaptiveness is achieved by applying a well-defined set of rules for different strata of the 3D NoC. The proposed method is applicable to any of the turn models. In this study, the authors employ odd-even turn model as a basis for introducing the proposed strategy. Experimental results show that the new 3D odd-even turn model can achieve up to 28.5% improvement in performance over conventional 3D odd-even approach. The improvement is consistent for different traffic types and selection strategies. The proposed method enables a new avenue to explore adaptive approaches for future large-scale 3D integration.
Power supply integrity has become a critical concern with the rapid shrinking of device dimensions and the ever increasing power consumption in nano-scale integration. Particularly, power supply noise is strongly correlated to the spatial distribution of activity densities and this can be attributed to the on-chip communication, which dictates the power dissipation and overall system performance in networkson-chip. In this paper, we propose a new mapping strategy aiming to create a balanced activity distribution across the whole chip. We formulate the problem of application mapping as a minimization of the activity density by employing a repulsive force-based objective function. Metrics of regional activity density and characteristics of its impact on power supply noise are considered. The proposed method has been rigorously evaluated based on a large set of real-application benchmarks. Significant reduction in power supply noise can be achieved with negligible energy overhead. This new approach would provide a more scalable solution for future large-scale system integration.
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