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According to the fact that the sites of wired video monitoring system cannot move, wiring is not flexible and high network bandwidth is required to transmit video data stream. H.264 video compression standard is adopted, WiFi is introduced as the wireless transmission network, and video coding and compression are produced on embedded S3C6410-based platform, which makes compressed data streams transmitted via WiFi. Based on the above, a wireless video monitoring system is built. It is proved that the site amount of the monitoring system could be increased or decreased in demand and moved arbitrarily through the experiment. Monitor image is clear and stable, and the system could be actually applied in petroleum exploitation, geological exploration, and so on.

In the past decade, eigenvalue optimization has gained remarkable attention in various engineering applications. One of the main difficulties with numerical analysis of such problems is that the eigenvalues, considered as functions of a symmetric matrix, are not smooth at those points where they are multiple. We propose a new explicit nonsmooth second-order bundle algorithm based on the idea of the proximal bundle method on minimizing the arbitrary eigenvalue over an affine family of symmetric matrices, which is a special class of eigenvalue function–D.C. function. To the best of our knowledge, few methods currently exist for minimizing arbitrary eigenvalue function. In this work, we apply the-Lagrangian theory to this class of D.C. functions: the arbitrary eigenvalue function λiwith affine matrix-valued mappings, where λiis usually not convex. We prove the global convergence of our method in the sense that every accumulation point of the sequence of iterates is stationary. Moreover, under mild conditions we show that, if started close enough to the minimizerx*, the proposed algorithm converges tox* quadratically. The method is tested on some constrained optimization problems, and some encouraging preliminary numerical results show the efficiency of our method. [ABSTRACT FROM PUBLISHER]

A decomposition algorithm based on proximal bundle-type method with inexact data is presented for minimizing an unconstrained nonsmooth convex function ƒ. At each iteration, only the approximate evaluation of ƒ and its approximate subgradients are required which make the algorithm easier to implement. It is shown that every cluster of the sequence of iterates generated by the proposed algorithm is an exact solution of the unconstrained minimization problem. Numerical tests emphasize the theoretical findings. [ABSTRACT FROM AUTHOR]

Background: Identifying gene regulatory network (GRN) from time course gene expression data has attracted more and more attentions. Due to the computational complexity, most approaches for GRN reconstruction are limited on a small number of genes and low connectivity of the underlying networks. These approaches can only identify a single network for a given set of genes. However, for a large-scale gene network, there might exist multiple potential sub-networks, in which genes are only functionally related to others in the sub-networks., Results: We propose the network and community identification (NCI) method for identifying multiple subnetworks from gene expression data by incorporating community structure information into GRN inference. The proposed algorithm iteratively solves two optimization problems, and can promisingly be applied to large-scale GRNs. Furthermore, we present the efficient Block PCA method for searching communities in GRNs., Conclusions: The NCI method is effective in identifying multiple subnetworks in a large-scale GRN. With the splitting algorithm, the Block PCA method shows a promosing attempt for exploring communities in a large-scale GRN.