Global and Local Synchronization of General Multi-linked Delayed Complex Dynamical Networks
This paper investigates the phenomena of a new and general multi-linked complex network with delayed nodes and couplings. Via constructing Lyapunov-Krasovskii function and using inequality technique, the criteria for locally exponential synchronization and globally asymptotical synchronization are derived. The latter neither requires the outer-coupling configuration matrix to be symmetric and irreducible nor needs the network to be linearized at its synchronization state, it is obtained by Kronecker product method. Finally, a numerical example illustrates the theoretical results.

title: A Generalized LMI-Based Approach to the Global Exponential Stability of Recurrent Neural Networks with Delay
abstract: A new theoretical result on the global exponential stability of recurrent neural networks with delay is presented. It should be noted that the activation functions of recurrent neural network do not require to be bounded. The presented criterion, which has the attractive feature of possessing the structure of linear matrix inequality (LMI), is a generalization and improvement over some previous criteria. A example is given to illustrate our results.

title: Stochastic High-Order Hopfield Neural Networks
abstract: In 1984 Hopfield showed that the time evolution of a symmetric Hopfield neural networks are a motion in state space that seeks out minima in the energy function (i.e., equilibrium point set of Hopfield neural networks). Because high-order Hopfield neural networks have more extensive applications than Hopfield neural networks, and have been discussed on the convergence of the networks. In practice, a neural network is often subject to environmental noise. It is therefore useful and interesting to find out whether the high-order neural network system still approacher some limit set under stochastic perturbation. In this paper, we will give a number of useful bounds for the noise intensity under which the stochastic high-order neural network will approach its limit set. Our result cancels the requirement of symmetry of the connection weight matrix and includes the classic result on Hopfield neural networks, which is a special case of stochastic high-order Hopfield neural networks. In the end, A example is given to verify the effective of our results.

title: Asymptotic behavior of stochastic Cohen-Grossberg neural networks with variable delays
abstract: Using Chebyshev inequality and nonnegative semi-martingale convergence theorem, the paper investigates asymptotic behavior of stochastic Cohen-Grossberg neural networks with delay by constructing suitable Lyapunov functional. Algebraic criteria are given for stochastic ultimate bounded and almost exponential stability. The result in the paper extend the main conclusion. In the end, examples are given to verify the effective of our results.

title: Global Exponential Stability of Cohen-Grossberg Neural Networks with Time-Varying Delays and Continuously Distributed Delays
abstract: In this paper, we investigate the global exponential stability for a class of Cohen-Grossberg neural networks (CGNN) with time-varying delays and continuously distributed delays. CGNN herein is a general neural networks model which includes some well-known neural networks as its special cases. Firstly, applying the homeomorphism theory, we establish the new sufficient condition of existence and uniqueness of the equilibrium point to CGNN. Then, the sufficient criteria of global exponential stability of CGNN, which are easy to verify, are given by M-matrix. Our results imply and generalize some existed ones in previous literature. Furthermore, it is convenient to estimate the exponential convergence rates of the neural networks by using the criteria. Compared with the previous methods, our method does not resort to any Lyapunov functions or functionals. Finally, a example is given to illustrate the effective of our results.

title: Improved Results on Solving Quadratic Programming Problems with Delayed Neural Network
abstract: In this paper, in terms of a linear matrix inequality (LMI), using a delayed Lagrangian network to solve quadratic programming problems, sufficient conditions on delay-dependent and delay-independent are given to guarantee the globally exponential stability of the delayed neural network at the optimal solution. In addition, exponential convergence rate is estimated by the equation in the paper. Furthermore, the results in this paper improved the ones reported in the existing literatures and the proposed sufficient condition can be checked easily by solving LMI. Two simulation examples are provided to show the effectiveness of the approach and applicability of the proposed criteria.

title: Access Protocol with Efficient Slot Reuse in WDM Metro Ring
abstract: This letter provides a scalable slotted ring network architecture with nodes using one fixed transmitter, one tunable transmitter and multiple fixed receivers. Furthermore, the novel access protocol with efficient slot reuse is proposed. Theoretical analysis and simulation results show that protocol can achieve high node throughput and low queuing delay.

title: Feature Mining Paradigms for Scientific Data
abstract: Numerical simulation is replacing experimentation as a means to gain insight into complex physical phenomena. Analyzing the data produced by such simulations is extremely challenging, given the enormous sizes of the datasets involved. In order to make efficient progress, analyzing such data must advance from current techniques that only visualize static images of the data, to novel techniques that can mine, track, and visualize the important features in the data. In this paper, we present our research on a unified framework that addresses this critical challenge in two science domains: computational fluid dynamics and molecular dynamics. We offer a systematic approach to detect the significant features in both domains, characterize and track them, and formulate hypotheses with regard to their complex evolution. Our framework includes two paradigms for feature mining, and the choice of one over the other, for a given application, can be determined based on local or global influence of relevant features in the data.

title: Convergence Studies on Iterative Algorithms for Image Reconstruction
abstract: We introduce a general iterative scheme for image reconstruction based on Landweber's method. In our configuration, a sequential block-iterative (SeqBI) version can be readily formulated from a simultaneous block-iterative (SimBI) version, and vice versa. This provides a mechanism to derive new algorithms from known ones. It is shown that some widely used iterative algorithms, such as the algebraic reconstruction technique (ART), simultaneous ART (SART), Cimmino's, and the recently designed diagonal weighting and component averaging algorithms, are special examples of the general scheme. We prove convergence of the general scheme under conditions more general than assumed in earlier studies, for its SeqBI and SimBI versions in the consistent and inconsistent cases, respectively. Our results suggest automatic relaxation strategies for the SeqBI and SimBI versions and characterize the dependence of the limit image on the initial guess. It is found that in all cases the limit is the sum of the minimum norm solution of a weighted least-squares problem and an oblique projection of the initial image onto the null space of the system matrix.

title: Iterative Joint Channel Estimation and Symbol Detection for Multi-User MIMO OFDM
abstract: Multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems have recently attracted substantial research interest. However, compared to single-input-single-output (SISO) systems, channel estimation in the MIMO scenario becomes more challenging, owing to the increased number of independent transmitter-receiver links to be estimated. In the context of the Bell layered space-time architecture (BLAST) or space division multiple access (SDMA) multi-user MIMO OFDM literature, no channel estimation technique allows the number of users to be higher than the number of receiver antennas, which is often referred to as an "overloaded" scenario. In this contribution we propose a new genetic algorithm (GA) assisted iterative joint channel estimation and multi-user detection approach for MIMO SDMA-OFDM systems, which exhibits a robust performance in the above-mentioned overloaded scenario. Furthermore, GA-aided multi-user detection (MUD) techniques found in the literature can only provide a hard-decision output, while the proposed GA is capable of providing "soft" outputs, hence achieving an improved performance with the aid of channel decoders. Finally, a range of simulation results are provided to demonstrate the superiority of the proposed scheme

title: Convergence of iterative algorithms for image reconstruction
abstract: We introduce a general iterative scheme for image reconstruction based on Landweber's method. Within our configuration, the block-iterative (BI) version can be formulated from its simultaneous version easily, and vice versa. This provides the mechanism to formulate new algorithms from known algorithms. It can be shown that some of the widely used iterative algorithms, such as the ART, SART, Cimmino's Algorithm and recently designed DWE and CAV, are special examples of the general scheme or its BI version. By applying the convergence results of the general scheme, the corresponding convergence in the consistent or inconsistent cases, for the block-iterative or simultaneous versions, of those specific algorithms can be established under mild conditions on the relaxation parameters.