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We propose a novel two dimensional least mean square (2D-LMS) algorithm for sparse image de-noising, which exploits the sparsity information of images by utilizing a priori. To efficiently accelerate convergence speed, the proposed algorithm utilizes the image sparsity level information to modify the cost function of traditional 2D-LMS via a penalty of tap-weight. Since the largest q norm is not differentiable...
In our previous works, based on characteristics of emergency systems such as quick response to emergencies, a polynomial time algorithm was proposed to solve task schedulable problem. However, the above mentioned task scheduling method did not consider importance and urgency differences among tasks in selecting task to accomplish. In order to make the task scheduling be more close to actual process...
In this thesis, we studied the theory of OLAM which combined data mining with On-Line Analytical Processing (OLAP). This thesis presents a kind of distributed data mining service architecture aiming to resolve the problems that appear widely in the current data mining tools, such as low level of resource sharing, functional coupling too closely, difficult to reuse software and so on. On the basis...
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