Hierarchical Adaptive Eddy-Capturing Approach for Modeling and Simulation of Turbulent Flows

A short review of wavelet-based adaptive methods for modeling and simulation of incompressible turbulent flows is presented. Wavelet-based computational modeling approaches of different fidelities are recast into an integrated hierarchical adaptive eddy-capturing turbulence modeling framework. The wavelet threshold filtering procedure and the associated wavelet-filtered Navier–Stokes equations are briefly discussed, along with the adaptive wavelet collocation method that is used for numerical computations. Depending on the level of wavelet thresholding, the simulation is possibly supplemented with a localized closure model. The latest advancements in spatiotemporally varying wavelet thresholding procedures along with the adaptive-anisotropic wavelet-collocation method make the development of a fully adaptive approach feasible with potential applications for complex turbulent flows.

A Sub-band Anisotropic Diffusion Technique for de-speckling of Ultrasound Images of Breast Cancer

Ultrasound imaging technique finds crucial application in clinical diagnosis of breast cancer. Presence of noise in ultrasound image due to different factor degrades the image quality and so the accuracy of diagnosis. Wavelet thresholding have been used from very beginning for de-noising of ultrasound image. Here in this paper we propose an intervention of anisotropic diffusion techniques in wavelet thresholding. In wavelet thresholding the thresholding operation usually applied after various feature extraction step, but in this study, we proposed to use a combinational approach. The approach reduces computational complexity of previous techniques. The proposed technique provides a Peak Signal to Noise Ratio of 28.46 and Mean Square Error of about 92.5537. The technique was practiced over large dataset of breast cancer images.