This paper presents a study of the computational enhancement of a Graphics Processing Unit (GPU) enabled 2D flood model.
The recent revival of interest in retro-synthetic analysis [6, 7] is also an example of computational enhancement of our scientific knowledge.
Hence, a delamination prediction strategy via K-means clustering, ANN and optimization algorithms integrated with surrogate models based on ANN for computational enhancement have been successfully developed and found efficient for detection of the interface of delamination, its size and location in FRP composite laminates using variations in natural frequencies.
This paper surveys this topic in terms of computational image enhancement, feature extraction, classification schemes and designed hardware-based acquisition set-ups.
We focus on some basic linear and nonlinear MIMO detection and precoding algorithms and their optimization for a DSP target, and a few principal steps for computational performance enhancement are outlined.
Computational intensive image enhancement algorithms such as the Retinex [2] and its variants including optimization through variational methods [3, 4] are not discussed.
We attempt to rationalise the noted enhancements through computational studies of the adsorption, diffusion and reactivity of the various species in the zeolites, with some success, in order to allow future design of electronic nose devices.
This multi-domain approach allows for significant reductions of the number of grid points in the azimuthal direction for the inner grid domain and thus for corresponding increases of the time step and enhancements of computational efficiency.
Given an m × n image, the computational complexity of edge enhancement and GVF computation is O Kmn log(mn)), where K is the number of filters or the number of iterations.
This problem can be resolved by combining a Gaussian-pyramid-based adaptive scale selection method [6] or a multi-scale convolution method [12] with the proposed method; however, this design usually requires lots of computations and decreases the computational efficiency of the entire enhancement process.
