Quantized control for networked switched systems under denial-of-service attacks via a barrier event-triggered mechanism

This paper studies the quantized control problem for networked switched systems (NSSs) under denial-of-service (DoS) attacks. The quantized state information, together with the switching signal, is transmitted to the controller through a network. In order to reduce communication consumption and controller update frequency, a barrier event-triggered mechanism is utilized to monitor the state at discrete time. Because of the event-triggered mechanism and the DoS attacks on the network, the mismatch between the system mode and the controller mode is thus frequently encountered, which may lead to quantization saturation and system instability. To solve the problem, an update rule is presented for the dynamic quantizer by switching between zooming in and zooming out of the zooming variable, and a feedback controller is proposed with a jointly designed event-triggered mechanism and a dynamic quantizer. Sufficient conditions on the constraints of DoS frequency and duration are obtained to ensure the exponential stability of the switched system. The effectiveness of the obtained results is illustrated by simulation examples and comparative studies.     

Optimizing active ranges for consistent dynamic map labeling

Map labeling encounters unique issues in the context of dynamic maps with continuous zooming and panning—an application with increasing practical importance. In consistent dynamic map labeling, distracting behavior such as popping and jumping is avoided. We use a model for consistent dynamic labeling in which a label is represented by a 3d-solid, with scale as the third dimension. Each solid can be truncated to a single scale interval, called its active range, corresponding to the scales at which the label will be selected. The active range optimization (ARO) problem is to select active ranges so that no two truncated solids intersect and the sum of the heights of the active ranges is maximized. Simple ARO is a variant in which the active ranges are restricted so that a label is never deselected when zooming in. We investigate both the general and simple variants, for 1d- as well as 2d-maps.Different label shapes define different ARO variants. We show that 2d-ARO and general 1d-ARO are NP-complete, even for quite simple shapes. We solve simple 1d-ARO optimally with dynamic programming, and present a toolbox of algorithms that yield constant-factor approximations for a number of 1d- and 2d-variants.     

矩阵在图形学几何变换中的应用

借助于计算机图形学中的缩放变换、旋转变换、反射变换及平移变换等的直观性来说明＂矩阵是用来表示变换的一种工具＂,进而加强对分块矩阵、对角矩阵及正交矩阵的认识.     

A theoretical investigation of regular equivalences for fuzzy graphs

The notion of regular equivalence or bisimulation arises in different applications, such as positional analysis of social networks and behavior analysis of state transition systems. The common characteristic of these applications is that the system under modeling can be represented as a graph. Thus, regular equivalence is a notion used to capture the similarity between nodes based on their linking patterns with other nodes. According to Borgatti and Everett, two nodes are regularly equivalent if they are equally related to equivalent others. In recent years, fuzzy graphs have also received considerable attention because they can represent both the qualitative relationships and the degrees of connection between nodes. In this paper, we generalize the notion of regular equivalence to fuzzy graphs based on two alternative definitions of regular equivalence. While the two definitions are equivalent for crisp graphs, they induce different generalizations for fuzzy graphs. The first generalization, called regular similarity, is based on the characterization of regular equivalence as an equivalence relation that commutes with the underlying graph edges. The regular similarity is then a fuzzy binary relation that specifies the degree of similarity between nodes in the graph. The second generalization, called generalized regular equivalence, is based on the definition of coloring. A coloring is a mapping from the set of nodes to a set of colors. A coloring is regular if nodes that are mapped to the same color, have the same colors in their neighborhoods. Hence, generalized regular equivalence is an equivalence relation that can determine a crisp partition of the nodes in a fuzzy graph.     

An improved GPS method with a new pseudo-peripheral nodes finder in finite element analysis

Finding pseudo-peripheral nodes with the largest eccentricity is important in matrix bandwidth and profile reduction algorithms in finite element analysis. A heuristic parameter, called the “width-depth ratio” and denoted by κ, is presented for finding the pseudo-peripheral nodes with larger pseudo-diameter compared with the GPS (Gibbs-Poole-Stockmeyer) pseudo-peripheral nodes finder. A novel nodes renumbering algorithm is thus developed by using our nodes finder based on GPS method. Simulations show that proposed nodes finder is reliable and effective in locating the proper pseudo-peripheral nodes with larger pseudo-diameters. A shielded microstrip line is given as an example to testify the ability of the proposed algorithm in application. The results, including time, pseudo-diameter, bandwidths and profiles, all indicate that our method is more competitive than GPS algorithm to be used as the nodes renumbering algorithm.     

Some topological properties of star graphs: The surface area and volume

The star graph, as an interesting network topology, has been extensively studied in the past. In this paper, we address some of the combinatorial properties of the star graph. In particular, we consider the problem of calculating the surface area and volume of the star graph, and thus answering an open problem previously posed in the literature. The surface area of a sphere with radius i in a graph is the number of nodes in the graph whose distance from a given node is exactly i. The volume of a sphere with radius i in a graph is the number of nodes within distance i from the given node. In this paper, we derive explicit expressions to calculate the surface area and volume in the star graph.     

Adaptive Image Zo oming Algorithm Combining Total Variation Minimization and a Second-order Functional

An image zooming algorithm by using partial differential equations(PDEs) is proposed here. It combines the second-order PDE with a fourth-order PDE. The combined algorithm is able to preserve edges and at the same time avoid the blurry effect in smooth regions. An adaptive function is used to combine the two PDEs. Numerical experiments illustrate advantages of the proposed model.     

基于全变分与二阶泛函联合的自适应图像放大算法（英文）

An image zooming algorithm by using partial differential equations(PDEs) is proposed here. It combines the second-order PDE with a fourth-order PDE. The combined algorithm is able to preserve edges and at the same time avoid the blurry effect in smooth regions. An adaptive function is used to combine the two PDEs. Numerical experiments illustrate advantages of the proposed model.     

Optimization of passive optical network planning

Network planning problem typically involves large capital investment and can be formulated as an optimization problem where the objective is minimization of the first installed cost. We consider a passive optical network (PON) planning problem based on a residential area in Adana (Turkey). There are four possible primary node locations, twenty possible secondary node locations, and twenty-eight customers. We use genetic algorithm and mathematical modeling techniques to optimize the position of the primary and secondary nodes, their split levels and assigning customers to secondary nodes and secondary nodes to primary nodes under some constraints such as system’s attenuations and technical characteristics of all the equipment.     

一种基于网格模型的无限传感器网络K区域覆盖算法

目前无限传感器网络K覆盖问题的解决机制大都有颇为苛刻的假设条件,如要求节点具有很强的能量、节点的感知区域能被精确定义等.提出了一个基于布尔感知理论的分布式网格模型及相应的K区域覆盖算法,能够很好地处理感知区域形状不规则及大小发生变化的K覆盖问题,算法时间复杂度小,适用范围广泛.仿真实验证明了算法的有效性.     

A Fast Augmented Lagrangian Method for Euler's Elastica Models

In this paper, a fast algorithm for Euler's elastica functional is proposed, in which the Euler's elastica functional is reformulated as a constrained minimization problem. Combining the augmented Lagrangian method and operator splitting techniques, the resulting saddle-point problem is solved by a serial of subproblems. To tackle the nonlinear constraints arising in the model, a novel fixed-point-based approach is proposed so that all the subproblems either is a linear problem or has a closed-form solution. We show the good performance of our approach in terms of speed and reliability using numerous numerical examples on synthetic, real-world and medical images for image denoising, image inpainting and image zooming problems.     

Random walks on finitely structured transfinite networks

A general theory for random walks on transfinite networks whose ranks are arbitrary natural numbers is established herein. In such networks, nodes of higher ranks connect together transfinite networks of lower ranks. The probabilities for transitions through such nodes are obtained as extensions of the Nash-Williams rule for random walks on ordinary infinite networks. The analysis is based on the theory of transfinite electrical networks, but it requires that the transfinite network have a structure that generalizes local-finiteness for ordinary infinite networks. The shorting together of nodes of different ranks are allowed; this complicates transitions through such nodes but provides a considerably more general theory. It is shown that, with respect to any finite set of nodes of any ranks, a transfinite random walk can be represented by an irreducible reversible Makov chain, whose state space is that set of nodes.This work was supported by the National Science Foundation under the grants DMS-9200738 and MIP-9200748.     

A new method for constructing infinite families of k-tight optimal double loop networks

The double loop network (DLN) is a circulant digraph with n nodes and outdegree 2. DLN has been widely used in the designing of local area networks and distributed systems. In this paper, a new method for constructing infinite families of k-tight optimal DLN is presented. For k = 0,1,…,40, the infinite families of k-tight optimal DLN can be constructed by the new method, where the number nk(t,a) of their nodes is a polynomial of degree 2 in t and contains a parameter a. And a conjecture is proposed.     

A Priori Feedback Estimates for Multiscale Reaction-Diffusion Systems

We study the approximation of a multiscale reaction–diffusion system posed on both macroscopic and microscopic space scales. The coupling between the scales is done through micro–macro flux conditions. Our target system has a typical structure for reaction–diffusion flow problems in media with distributed microstructures (also called, double porosity materials). Besides ensuring basic estimates for the convergence of two-scale semi-discrete Galerkin approximations, we provide a set of a priori feedback estimates and a local feedback error estimator that help in designing a distributed-high-errors strategy to allow for a computationally e?cient zooming in and out from microscopic structures. The error control on the feedback estimates relies on two-scale-energy, regularity, and interpolation estimates as well as on a fine bookeeping of the sources responsible with the propagation of the (multiscale) approximation errors. The working technique based on a priori feedback estimates is in principle applicable to a large class of systems of PDEs with dual structure admitting strong solutions.     

Geometrical grouplets

Grouplet orthogonal bases and tight frames are constructed with association fields that group points to take advantage of geometrical image regularities in space or time. These association fields have a multiscale geometry that can incorporate multiple junctions. A fast grouplet transform is computed with orthogonal multiscale hierarchical groupings. A grouplet transform applied to wavelet image coefficients defines an orthogonal basis or a tight frame of grouping bandlets. Applications to noise removal and image zooming are described.     

Displacement Regularization with Application to Image Zooming

We study the problem of displacement errors, i.e. errors induced by a sampling process with distorted locations of the sampling points. Starting with a non-convex regularization method, we apply a semi-group concept and derive a partial differential equation, which allows for correcting displacement errors. As main application for correction of displacement errors we consider image interpolation, in particular zooming, of digital color images. (© 2010 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Image magnification based on similarity analogy

Aiming at the high time complexity of the decoding phase in the traditional image enlargement methods based on fractal coding, a novel image magnification algorithm is proposed in this paper, which has the advantage of iteration-free decoding, by using the similarity analogy between an image and its zoom-out and zoom-in. A new pixel selection technique is also presented to further improve the performance of the proposed method. Furthermore, by combining some existing fractal zooming techniques, an efficient image magnification algorithm is obtained, which can provides the image quality as good as the state of the art while greatly decrease the time complexity of the decoding phase.     

Application of modal analysis in assessing attack vulnerability of complex networks

In this paper we propose an alternative way to study robustness and vulnerability of complex networks, applying a modal analysis. The modal weights of the network nodes are considered as a measure for their busyness, which is further used for preferential removal of nodes and attack simulation. Analyses of the attack vulnerability are carried out for several generic graphs, generated according to ER and BA algorithms, as well as for some examples of manmade networks. It was found that a modal weight based attack causes significant disintegration of manmade networks by removing a small fraction of the busiest nodes, comparable to the one based on the node degree and betweenness centrality.     

Variable elimination for influence diagrams with super value nodes

In the original formulation of influence diagrams (IDs), each model contained exactly one utility node. In 1990, Tatman and Shachtar introduced the possibility of having super value nodes that represent a combination of their parents’ utility functions. They also proposed an arc-reversal algorithm for IDs with super value nodes. In this paper we propose a variable-elimination algorithm for influence diagrams with super value nodes which is faster in most cases, requires less memory in general, introduces much fewer redundant (i.e., unnecessary) variables in the resulting policies, may simplify sensitivity analysis, and can speed up inference in IDs containing canonical models, such as the noisy OR.     

The construction of infinite families of any κ-tight optimal and singular κ-tight optimal directed double loop networks

The double loop network (DLN) is a circulant digraph with n nodes and outdegree 2. It is an important topological structure of computer interconnection networks and has been widely used in the designing of local area networks and distributed systems. Given the number n of nodes, how to construct a DLN which has minimum diameter? This problem has attracted great attention. A related and longtime unsolved problem is: for any given non-negative integer k, is there an infinite family of k-tight optimal DLN? In this paper, two main results are obtained: (1) for any k ≥ 0, the infinite families of k-tight optimal DLN can be constructed, where the number n(k,e,c) of their nodes is a polynomial of degree 2 in e with integral coefficients containing a parameter c. (2) for any k ≥ 0,an infinite family of singular k-tight optimal DLN can be constructed.