Wu Minghu

Dynamic global-principal component analysis sparse representation for distributed compressive video sampling

Video reconstruction quality largely depends on the ability of employed sparse domain to adequately represent the underlying video in Distributed Compressed Video Sensing (DCVS). In this paper, we propose a novel dynamic global-Principal Component Analysis (PCA) sparse representation algorithm for video based on the sparse-land model and nonlocal similarity. First, grouping by matching is realized at the decoder from key frames that are previously recovered. Second, we apply PCA to each group (sub-dataset) to compute the principle components from which the sub-dictionary is constructed. Finally, the non-key frames are reconstructed from random measurement data using a Compressed Sensing (CS) reconstruction algorithm with sparse regularization. Experimental results show that our algorithm has a better performance compared with the DCT and K-SVD dictionaries.

A Lanchester Equation and its Solution Considering the Factor of Terrain

Terrain is an important factor in computer combat simulation. After analyzing the disadvantage of ignoring the important factors such as weather, terrain and tactics in the classical Lanchester equation, an improved Lanchester equation considering the factor of terrain of battlefield is proposed, the solution of the equation, the computation of the residual strength of the winner, and the forecast of the battle are given. The impact values of terrain are also analyzed. Finally an example is used to verify the validity of the model. The results showed that the model is feasible, and can be used to judge the losses of force strength more accurately and effectively in combat simulation systems.