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Addressing the optimization problem during the simultaneous energy and information transmission stage in the simultaneous wireless information and power transfer (SWIPT), an optimal energy efficiency strategy based on mmWave cooperative communication small cell is proposed to maximize the link energy efficiency. This strategy, under the joint constraints of minimum link transmission rate and minimum collected energy, employs a power splitting mode at the receding end of energy-constrained user devices. By optimizing transmit power control and power splitting factors, it aims to maximize the systems link energy efficiency. Recognizing the original problem as a nonconvex fractional programming problem with NP-hard characteristics, the Dinkelbach method is utilized to transform the objective function into a convex optimization problem that is easier to solve. An iterative algorithm is designed to find the optimal solution through cross iterations. Simulation results demonstrate that the proposed strategy outperforms traditional power control methods and maximum transmit power approaches in optimizing system energy efficiency. [ABSTRACT FROM AUTHOR]

Partially blurred images caused by motion or out of focus are common in life, we need to detect and segment blurred image region when doing some multimedia analyzing tasks. As for the problem of blurred region detection and segmentation of partially blurred images, we propose a method based on singular value decomposition and image matting is presented in this paper. Making use of the different changes of image blurred regions and clear regions under a low-pass filtering, partially blurred image is firstly partitioned into patches and each image patch is re-blurred by a Gaussian function, then a trimap is got after comparing the singular value feature difference of the patches to distinguish blurred regions and clear regions. Secondly, a image matting technique is combined with the trimap to automatically segment blurred regions from the partially blurred images. Experiments show that this method can detect and segment the blurred regions accurately. [ABSTRACT FROM AUTHOR]