功率受限的无线网络的传输速率控制

无线自组织网络是没有预置基础设施支撑的自组织可重构的自治网络.由于需要克服远近效应问题、干扰问题以及提高信道的空间复用度,并且为了降低网络节点的能耗,提高网络的生存时间和系统的能量效率,网络节点的发射功率受到限制.因此,如何在功率受限的情况下保证网络数据传榆性能,成为无线网络的关键问题.提出了功率受限的无线自组织网络模型,该模型用发送功率的凸函数作为度量效用的指标之一,更加贴切地反映了网络节点功率受限的特点.采用对偶分解的方法求解模型,得到分布式算法,协调节点的传输功率和数据速率,达到全网效用最大化.最后用具体的拓扑和效用函数进行仿真,验证了算法的收敛性,并考察传输速率和功率的关系对网络性能的影响.     

一种基于改进的SROD算法的电影胶片

电影胶片由于长期存放和多次播放,产生了灰尘、污垢、霉斑、图像抖动、划痕、闪烁、噪声、变色、模糊等问题,其中由灰尘、污垢等引起的斑块是出现最频繁的损伤之一。为了快速有效地对这些斑块进行检测,提出了一种检测电影胶片斑块损伤的技术,该技术首先用改进的SROD算法对受损帧的斑块进行初步检测,然后通过两次EM估计算法对检测结果进行后处理来消除噪声的影响,同时确定斑块位置及其边缘信息。实验结果显示,该技术不仅可以有效地检测斑块,而且算法简单快速。     

虚拟企业在技术创新扩散过程中决策采纳优化算法

在技术创新扩散过程中很少创新是一进入市场就被立刻采用的。当具备采纳创新技术所需的财务条件时,企业主要考虑何时及如何配置各资源比例达到效用最大化的问题,这是一个多阶段有限理性的决策过程。本文提出运用反向传播算法进行定量计算,解决了企业在创新技术决策采纳过程中资源权重配置的问题,通过虚拟企业实证为企业决策优
优化采纳提供了可行性的参考依据。     

Topology Driven Cooperative Self Scheduling for Improved Lifetime Maximization in WSN

In Wireless Sensor Network (WSN), scheduling is one of the important issues that impacts the lifetime of entire WSN. Various scheduling schemes have been proposed earlier to increase the lifetime of the network. Still, the results from such methods are compromised in terms of achieving high lifetime. With this objective to increase the lifetime of network, an Efficient Topology driven Cooperative Self-Scheduling (TDCSS) model is recommended in this study. Instead of scheduling the network nodes in a centralized manner, a combined approach is proposed. Based on the situation, the proposed TDCSS approach performs scheduling in both the ways. By sharing the node statistics in a periodic manner, the overhead during the transmission of control packets gets reduced. This in turn impacts the lifetime of all the nodes. Further, this also reduces the number of idle conditions of each sensor node which is required for every cycle. The proposed method enables every sensor to schedule its own conditions according to duty cycle and topology constraints. Central scheduler monitors the network conditions whereas total transmissions occurs at every cycle. According to this, the source can infer the possible routes in a cycle and approximate the available routes. Further, based on the statistics of previous transmissions, the routes towards the sink are identified. Among the routes found, a single optimal route with energy efficiency is selected to perform data transmission. This cooperative approach improves the lifetime of entire network with high throughput performance.     

An Influence Maximization Algorithm Based on Improved K-Shell in Temporal Social Networks

Influence maximization of temporal social networks (IMT) is a problem that aims to find the most influential set of nodes in the temporal network so that their information can be the most widely spread. To solve the IMT problem, we propose an influence maximization algorithm based on an improved K-shell method, namely improved K-shell in temporal social networks (KT). The algorithm takes into account the global and local structures of temporal social networks. First, to obtain the kernel value Ks of each node, in the global scope, it layers the network according to the temporal characteristic of nodes by improving the K-shell method. Then, in the local scope, the calculation method of comprehensive degree is proposed to weigh the influence of nodes. Finally, the node with the highest comprehensive degree in each core layer is selected as the seed. However, the seed selection strategy of KT can easily lose some influential nodes. Thus, by optimizing the seed selection strategy, this paper proposes an efficient heuristic algorithm called improved K-shell in temporal social networks for influence maximization (KTIM). According to the hierarchical distribution of cores, the algorithm adds nodes near the central core to the candidate seed set. It then searches for seeds in the candidate seed set according to the comprehensive degree. Experiments show that KTIM is close to the best performing improved method for influence maximization of temporal graph (IMIT) algorithm in terms of effectiveness, but runs at least an order of magnitude faster than it. Therefore, considering the effectiveness and efficiency simultaneously in temporal social networks, the KTIM algorithm works better than other baseline algorithms.     

企业利润最大化的成本分配方法

提出了在多产品生产及成本导向价格的条件下,考虑需求的价格弹性使企业利润最大化的公共固定成本的分配方法．     

Maximum contrast analysis for nonnegative blind source separation

In this paper, we propose a maximum contrast analysis (MCA) method for nonnegative blind source separation, where both the mixing matrix and the source signals are nonnegative. We first show that the contrast degree of the source signals is greater than that of the mixed signals. Motivated by this observation, we propose an MCA-based cost function. It is further shown that the separation matrix can be obtained by maximizing the proposed cost function. Then we derive an iterative determinant maximization algorithm for estimating the separation matrix. In the case of two sources, a closed-form solution exists and is derived. Unlike most existing blind source separation methods, the proposed MCA method needs neither the independence assumption, nor the sparseness requirement of the sources. The effectiveness of the new method is illustrated by experiments using X-ray images, remote sensing images, infrared spectral images, and real-world fluorescence microscopy images.     

The Network as a Storage Device: Dynamic Routing with Bounded Buffers

We study dynamic routing in store-and-forward packet networks where each network link has bounded buffer capacity for receiving incoming packets and is capable of transmitting a fixed number of packets per unit of time. At any moment in time, packets are injected at various network nodes with each packet specifying its destination node. The goal is to maximize the throughput, defined as the number of packets delivered to their destinations. In this paper, we make some progress on throughput maximization in various network topologies. Let n and m denote the number of nodes and links in the network, respectively. For line networks, we show that Nearest-to-Go (NTG), a natural distributed greedy algorithm, is -competitive, essentially matching a known lower bound on the performance of any greedy algorithm. We also show that if we allow the online routing algorithm to make centralized decisions, there is a randomized polylog(n)-competitive algorithm for line networks as well as for rooted tree networks, where each packet is destined for the root of the tree. For grid graphs, we show that NTG has a competitive ratio of while no greedy algorithm can achieve a ratio better than . Finally, for arbitrary network topologies, we show that NTG is -competitive, improving upon an earlier bound of O(mn). An extended abstract appeared in the Proceedings of the 8th Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2005, Berkeley, CA, USA, pp. 1–13, Lecture Notes in Computer Science, vol. 1741, Springer, Berlin. S. Angelov is supported in part by NSF Career Award CCR-0093117, NSF Award ITR 0205456 and NIGMS Award 1-P20-GM-6912-1. S. Khanna is supported in part by an NSF Career Award CCR-0093117, NSF Award CCF-0429836, and a US-Israel Binational Science Foundation Grant. K. Kunal is supported in part by an NSF Career Award CCR-0093117 and NSF Award CCF-0429836.     

基于多例学习的Web图像聚类

在图像分类和自动标注系统中,多例学习(MIL)是研究的热点.目前MIL中的算法多为监督学习方法.针对非监督学习,在基于EM算法和启发式迭代优化算法的框架下,提出了6种多例聚类算法,并通过它们对来自于真实Web环境下的图像进行聚类以分析用户的搜索兴趣.由于一幅图像含有若干个区域,每个区域可被看为一个样例,属于同一个图像的区域则组成一个包.因此如何理解图像语义内容的问题即转化为多例学习.在多例学习的经典数据集MUSK数据和来自于Web图像集上的比较实验表明,提出的多例聚类算法具有优良的聚类性能.