Maximizing connected coverage via controlled actor relocation in wireless sensor and actor networks

Wireless sensor and actor networks (WSANs) have recently emerged with the idea of combining wireless sensor networks (WSNs) and mobile ad hoc networks (MANETs). In addition to resource constrained sensors, resource rich and mobile actor nodes are employed in WSANs. These actors can collect data from the sensors and perform appropriate actions as a result of processing such data. To perform the actions at all parts of the region in a timely manner, the actors should be deployed in such a way that they might be able to communicate with each other and cover the whole monitored area. This requires that the actors should be placed carefully prior to network operation in order to maximize the coverage and maintain the inter-actor connectivity. In this paper, we propose a distributed actor deployment algorithm that strives to maximize the coverage of actors without violating the connectivity requirement. The approach applies repelling forces between neighboring actors and from the sensors that sit on the boundaries in order to spread them in the region. The spreading of the nodes is done using a tree of actors which can provide more freedom for the movement of the nodes but at the same time maintain the required connectivity among the nodes. We present two techniques for creation of such an actor tree which are based on local pruning of the actor links and spanning tree of the inter-actor network. The performance of our approach is validated both analytically and experimentally.     

A new feature selection algorithm and composite neural network for electricity price forecasting

In a competitive electricity market, the forecasting of energy prices is an important activity for all the market participants either for developing bidding strategies or for making investment decisions. In this paper, a new forecast strategy is proposed for day ahead prediction of electricity price, which is a complex signal with nonlinear, volatile and time dependent behavior. Our forecast strategy includes a new two stage feature selection algorithm, a composite neural network (CNN) and a few auxiliary predictors. The feature selection algorithm has two filtering stages to remove irrelevant and redundant candidate inputs, respectively. This algorithm is based on mutual information (MI) criterion and selects the input variables of the CNN among a large set of candidate inputs. The CNN is composed of a few neural networks (NN) with a new data flow among its building blocks. The CNN is the forecast engine of the proposed strategy. A kind of cross-validation technique is also presented to fine-tune the adjustable parameters of the feature selection algorithm and CNN. Moreover, the proposed price forecast strategy is equipped with a few auxiliary predictors to enrich the candidate set of inputs of the forecast engine. The whole proposed strategy is examined on the PJM, Spanish and Californian electricity markets and compared with some of the most recent price forecast methods.     

Community discovery method with uncertainty measure of overlappingnodes based on topological potential

Community discovery of complex networks,esp.of social networks,has been a hotly debated topic in academic circles in recent years.Since actual networks usually contain some overlapping nodes that are d...     

Distributed estimation via iterative projections with application to power network monitoring

This work presents a distributed method for control centers to monitor the operating condition of a power network, i.e., to estimate the network state, and to ultimately determine the occurrence of threatening situations. State estimation has been recognized to be a fundamental task for network control centers to operate safely and reliably a power grid. We consider (static) state estimation problems, in which the state vector consists of the voltage magnitude and angle at all network buses. We consider the state to be linearly related to network measurements, which include power flows, current injections, and voltage phasors at some buses. We admit the presence of several cooperating control centers, and we design two distributed methods for them to compute the minimum variance estimate of the state, given the network measurements. The two distributed methods rely on different modes of cooperation among control centers: in the first method an incremental mode of cooperation is used, whereas, in the second method, a diffusive interaction is implemented. Our procedures, which require each control center to know only the measurements and the structure of a subpart of the whole network, are computationally efficient and scalable with respect to the network dimension, provided that the number of control centers also increases with the network cardinality. Additionally, a finite-memory approximation of our diffusive algorithm is proposed, and its accuracy is characterized. Finally, our estimation methods are exploited to develop a distributed algorithm to detect corrupted network measurements.     

Synchronization analysis of coupled Lienard-type oscillators by averaging

Sufficient conditions for the synchronization of coupled Lienard-type oscillators are investigated via averaging technique. The coupling considered here is fixed, nonsymmetric, and nonlinear. Under the assumption that the interconnection topology defines a connected graph, it is shown that the solutions of oscillators converge arbitrarily close to each other, starting from initial conditions arbitrarily far apart, provided that the frequency of oscillations is large enough and the initial phases of oscillators all lie in an open semicircle. It is also shown that the nearly-synchronized oscillations always take place around some fixed magnitude independent of the initial conditions and the coupling functions.     

基于小波统计特征的行块级朝汉文种辨识

提出了一种基于小波统计特征的朝汉文种识别方法．首先计算行文档图像的垂直及水平方向的投影;其次对垂直方向的投影及水平方向的投影进行一维小波分解并分别计算小波统计特征,然后将两个方向上的小波统计特征进行合并作为该文档图像的特征向量（本文方法构造的特征向量仅为11维）;最后通过神经网络进行训练和测试,结果显示平均测试准确率超过94％．     

Compact yet efficient hardware implementation of artificial neural networks with customized topology

There are several neural network implementations using either software, hardware-based or a hardware/software co-design. This work proposes a hardware architecture to implement an artificial neural network (ANN), whose topology is the multilayer perceptron (MLP). In this paper, we explore the parallelism of neural networks and allow on-the-fly changes of the number of inputs, number of layers and number of neurons per layer of the net. This reconfigurability characteristic permits that any application of ANNs may be implemented using the proposed hardware. In order to reduce the processing time that is spent in arithmetic computation, a real number is represented using a fraction of integers. In this way, the arithmetic is limited to integer operations, performed by fast combinational circuits. A simple state machine is required to control sums and products of fractions. Sigmoid is used as the activation function in the proposed implementation. It is approximated by polynomials, whose underlying computation requires only sums and products. A theorem is introduced and proven so as to cover the arithmetic strategy of the computation of the activation function. Thus, the arithmetic circuitry used to implement the neuron weighted sum is reused for computing the sigmoid. This resource sharing decreased drastically the total area of the system. After modeling and simulation for functionality validation, the proposed architecture synthesized using reconfigurable hardware. The results are promising.     

Vessel segmentation and width estimation in retinal images using multiscale production of matched filter responses

Automated segmentation of blood vessels in retinal images can help ophthalmologists screen larger populations for vessel abnormalities. However, automated vessel extraction is difficult due to the fact that the width of retinal vessels can vary from very large to very small, and that the local contrast of vessels is unstable. Further, the small vessels are overwhelmed by Gaussian-like noises. Therefore the accurate segmentation and width estimation of small vessels are very challenging. In this paper, we propose a simple and efficient multiscale vessel extraction scheme by multiplying the responses of matched filters at three scales. Since the vessel structures will have relatively strong responses to the matched filters at different scales but the background noises will not, scale production could further enhance vessels while suppressing noise. After appropriate selection of scale parameters and appropriate normalization of filter responses, the filter responses are then extracted and fused in the scale production domain. The experimental results demonstrate that the proposed method works well for accurately segmenting vessels with good width estimation.     

基于结构信息的图像质量评价模型

针对客观数字图像质量评价方法结构相似度（SSIM）算法对严重失真类型的评价值与主观评价（MOS）值之间存在着明显差异的问题,对几种将SSIM算法与人眼视觉系统（HVS）特性相结合的算法基于图像边缘的ESSIM算法、基于空间域频域敏感的HSSIM算法,以及基于信息内容权值的IWSSIM算法进行了比较。从实验数据可知,ESSIM,HSSIM算法的准确性有不同程度的提高,而IWSSIM模型在比较主客观值相关的单调性方面更为稳定。实验结果为今后结构相似度算法的改进提供了依据,指明研究方向。     

基于特征的三维装配尺寸／公差链生成研究

针对目前基于特征的尺寸／公差链生成主要集中于二维的情况，本文进行了三维装配尺寸／公差链生成的研究。基于特征的尺寸工程语义的表达以及基于特征自由度变动的三维公差信息的表示，给出了三维尺寸／公差信息的公差图存储结构；在分析零件配合关系及建立装配特征关联矩阵的基础上，提出了根据产品装配特征和指定封闭环信息的装配尺寸／公差链自动生成算法，该算法考虑到零件特征配合的方向性，可以简化装配尺寸／公差链自动生成的复杂度；通过实例验证了所提出的算法，证明有效。