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A new head pose estimation technique based on Random Forest (RF) and texture features for facial image analysis using a monocular camera is proposed in this paper, especially about how to efficiently combine the random forest and the features. In the proposed technique a randomized tree with useful attributes is trained to improve estimation accuracy and tolerance of occlusions and illumination. Specifically, a number of features including Multi-scale Block Local Block Pattern (MB-LBP) are extracted from an image, and random features such as the MB-LBP scale parameters, a block coordinate, and a layer of an image pyramid in the feature pool are used for training the tree. The randomized tree aims to maximize the information gain at each node while random samples traverse the nodes in the tree. To this aim, a split function considering the uniform property of the LBP feature is developed to move sample blocks to the left or the right children nodes. The trees are independently trained with random inputs, yet they are grouped to form a random forest so that the results collected from the trees are used for make the final decision. Precisely, we use a Maximum-A-Posteriori criterion in the decision. It is demonstrated with experimental results that the proposed technique provides significantly enhanced classification performance in the head pose estimation in various conditions of illumination, poses, expressions, and facial occlusions. [ABSTRACT FROM AUTHOR]

Uncovering underlying community structures in complex networks has received considerable attention because of its importance in understanding structural attributes and group characteristics of networks. The algorithmic identification of such structures is a significant challenge. Local expanding methods have proven to be efficient and effective in community detection, but most methods are sensitive to initial seeds and built-in parameters. In this paper, we present a local expansion method by density-based clustering, which aims to uncover the intrinsic network communities by locating the structural centers of communities based on a proposed structural centrality. The structural centrality takes into account local density of nodes and relative distance between nodes. The proposed algorithm expands a community from the structural center to the border with a single local search procedure. The local expanding procedure follows a heuristic strategy as allowing it to find complete community structures. Moreover, it can identify different node roles (cores and outliers) in communities by defining a border region. The experiments involve both on real-world and artificial networks, and give a comparison view to evaluate the proposed method. The result of these experiments shows that the proposed method performs more efficiently with a comparative clustering performance than current state of the art methods. [ABSTRACT FROM AUTHOR]

Energy consumption and coverage problem are two important issues in wireless sensor networks. In this paper, we are going to maintain sensing coverage in a heterogeneous wireless sensor network with an adjustable sensing range that is randomly developed, in such a way that small numbers of sensor nodes are active, and therefore a small amount of energy is consumed, and the network lifetime increases. This is a multi-objective optimization problem. We used a Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) for solving this problem.The experimental results demonstrated that MOEA/D can perform better than NSGA-II. [ABSTRACT FROM AUTHOR]

Time synchronization is essential for almost all the wireless sensor network application. Because of the multi-nodes and low energy consumption feature of wireless sensor network, the realization of a proper time synchronization algorithm is especially important. Based on a self-developed hardware platform, an appropriate single-hop and multi-hop time synchronization algorithm of Zigbee protocol is presented and a detailed algorithm analysis and implementation flow are given. The experimental results show that the time synchronization algorithm has a favorable performance in the single-hop and multi-hop beacon networks,which ensur.es a high synchronization accuracy and the low power consumption. At the same time of implementing Zigbee energy-saving mechanism, the algorithm provides high synchronization. [ABSTRACT FROM AUTHOR]