Link reliability based hybrid routing for tactical mobile ad hoc network

Tactical mobile ad hoc network （MANET） is a collection of mobile nodes forming a temporary network, without the aid of pre-established network infrastructure. The routing protocol has a crucial impact on the network performance in battlefields. Link reliability based hybrid routing （LRHR） is proposed, which is a novel hybrid routing protocol, for tactical MANET. Contrary to the traditional single path routing strategy, multiple paths are established between a pair of source-destination nodes. In the hybrid routing strategy, the rate of topological change provides a natural mechanism for switching dynamically between table-driven and on-demand routing. The simulation results indicate that the performances of the protocol in packet delivery ratio, routing overhead, and average end-to-end delay are better than the conventional routing protocol.

A comparison of coal supply-demand in China and in the US based on a network model

Through analysis the actual coal supply and demand in the US and China, the properties of the coal supply-demand market in both countries are investigated based on the energy supply-demand network. The validity of our model is verified by comparing numerical results with empirical results. The comparison of empirical results and the comparison of coal network model parameters between in the US and in China reveal the essence of the internal differences and similarities of coal supply and demand in these two countries. The third stage of China＇s coal network was close to that of the US in 1995, indicating that the evolutional situation of China＇s coal market begins to transit to an oligopolistic type. Finally, suggestions for China＇s coal supply-demand strategy are put forward.

Exploring the Constrained Maximum Edge-weight Connected Graph Problem

Given an edge weighted graph, the maximum edge-weight connected graph （MECG） is a connected subgraph with a given number of edges and the maximal weight sum. Here we study a special case, i.e. the Constrained Maximum Edge-Weight Connected Graph problem （CMECG）, which is an MECG whose candidate subgraphs must include a given set of k edges, then also called the k-CMECG. We formulate the k-CMECG into an integer linear programming model based on the network flow problem. The k-CMECG is proved to be NP-hard. For the special case 1-CMECG, we propose an exact algorithm and a heuristic algorithm respectively. We also propose a heuristic algorithm for the k-CMECG problem. Some simulations have been done to analyze the quality of these algorithms. Moreover, we show that the algorithm for 1-CMECG problem can lead to the solution of the general MECG problem.

Minimum Spanning Trees Across Well-Connected Cities and with Location-Dependent Weights

Consider n nodes{X_(i)}_(1≤i≤n) independently and identically distributed(i.i.d.)across N cities located within the unit square S.Each city is modelled as an r_(n)×r_(n)square,and MSTC_(n)denotes the weighted length of the minimum spanning tree containing all the n nodes,where the edge length between nodes X_(i)and X_(j)is weighted by a factor that depends on the individual locations of X_(i)and X_(j).We use approximation methods to obtain variance estimates for MSTC_(n)and prove that if the cities are well connected in a certain sense,then MSTC_(n)appropriately centred and scaled converges to zero in probability.Using the above proof techniques we also study MST_(n),the length of the minimum weighted spanning tree for nodes distributed throughout the unit square S with location-dependent edge weights.In this case,the variance of MST_(n)grows at most as a power of the logarithm of n and we use a subsequence argument to get almost sure convergence of MST_(n),appropriately centred and scaled.

Robust interactive image segmentation via graph-based manifold ranking

Interactive image segmentation aims at classifying the image pixels into foreground and background classes given some foreground and background markers. In this paper, we propose a novel framework for interactive image segmentation that builds upon graph-based manifold ranking model, a graph-based semi-supervised learning technique which can learn very smooth functions with respect to the intrinsic structure revealed by the input data. The final segmentation results are improved by overcoming two core problems of graph construction in traditional models: graph structure and graph edge weights. The user provided scribbles are treated as the must-link and must-not-link constraints. Then we model the graph as an approximatively k-regular sparse graph by integrating these constraints and our extended neighboring spatial relationships into graph structure modeling. The content and labels driven locally adaptive kernel parameter is proposed to tackle the insufficiency of previous models which usually employ a unified kernel parameter. After the graph construction,a novel three-stage strategy is proposed to get the final segmentation results. Due to the sparsity and extended neighboring relationships of our constructed graph and usage of superpixels, our model can provide nearly real-time, user scribble insensitive segmentations which are two core demands in interactive image segmentation. Last but not least, our framework is very easy to be extended to multi-label segmentation,and for some less complicated scenarios, it can even get the segmented object through single line interaction. Experimental results and comparisons with other state-of-the-art methods demonstrate that our framework can efficiently and accurately extract foreground objects from background.

A Shortest Path Problem

In this paper, we deal with the problem of selecting the location of coal concentratedstation. Where should we found the station, in a certain range, to mininize the total transporting cost ? The corresponding mathematical model will be founded. Furthermore, we prove that S reaches its minimum at its certain vertex. An algorithm used to find the optimum solution will be given.