Stability of non-autonomous bidirectional associative memory neural networks with delay
The paper discusses the stability of non-autonomous bidirectional associative memory neural networks. The existence and uniqueness of periodic solutions of the neural networks are investigated by using Gaines and Mawhin's continuation theorem of coincidence degree theory in the paper. Moreover, criterions on asymptotic stability and exponential stability of periodic solutions of the neural networks are obtained by using matrix function inequality, and algorithms for the criterions on the neural networks are provided. Some of the results in the paper generalize and improve the results in the existing references. In the end, an illustrate example is given to verify 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: 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: Finite-Time Boundedness Analysis of Uncertain CGNNs with Multiple Delays
abstract: The problem of finite-time boundedness(FTB) for a class of Cohen-Grossberg neural networks(CGNNs) with multiple delays and parameter perturbations is analyzed in this paper By way of extending the concept of FTB for time delay system and constructing a suitable Lyapunov function and using linear matrix inequality(LMI) technique, some delay-dependent criteria are derived to guarantee FTB for uncertain and certain CGNNs with multiple delays Meanwhile, an algorithm is also presented Finally, simulation examples are given to demonstrate the effectiveness of the conclusion.

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: Quantitative steganalysis of binary images
abstract: We propose a quantitative steganalysis method to detect hidden information embedded by flipping pixels along boundaries in binary images. We model steganographic embedding as an additive noise process and use compression rate as a distinguishing statistic that aids in discriminating between stego-images and cover-images. We specifically use the JBIG 2 binary image compression algorithm to derive a quantitative relation between compression rate and embedding rate. Based on this relationship, a practical steganalysis technique is proposed by examining the change of compression rate as embedding rate increases. Experiments conducted show that the proposed technique can reliably detect a steganographic embedding process that flips boundary pixels. Furthermore, it can estimate embedding rate with reasonable accuracy.

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: Determining the hamilton-connectedness of certain vertex-transitive graphs
abstract: Faber and Moore have proposed a class of vertex-transitive digraphs as a model of directed inconnection networks. These networks have attractive degree versus diameter properties. We show that these digraphs are Hamiltonian and provide necessary and sufficient conditions for the existence of a Hamilton path between any two vertices. Particular cases of these digraphs are the right rotation Cayley digraphs Cay ( X n : S n where X n is the set of right rotations (1 2 s i −1 i) for i=2,3,…,n . Another class of vertex-transitive graphs that have been proposed as models of inconnection networks are the arrangement graphs introduced by Day and Tripathi. They have the same vertex set as the Faber–Moore digraphs. We prove that arrangement graphs are Hamilton-connected.

title: Iterative MIMO Channel Estimation for Next Generation Wireless Systems
abstract: The combination of multiple-input multiple-output (MIMO) technology and orthogonal frequency division multiplexing (OFDM) has been considered as an attractive solution for the next generation wireless communication networks. In comparison with 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 this paper, we introduce an enhanced iterative channel estimation method, which is capable of providing genetically optimized channel information for uplink multi-user MIMO OFDM receivers, approaching the optimal benchmark arrangement utilizing perfect channel knowledge. The effectiveness of the proposed scheme is demonstrated by a range of numerical results under realistic scenarios.

title: Robust Document Image Authentication
abstract: In this paper, we propose a novel method for robust document image authentication. In the proposed method, characters and symbols on a binary document are first grouped into different classes based on k-means clustering in the feature space. Labels are then assigned to the different classes. An ordered sequence of labels formed from the characters and symbols on the document is then used to compute a digital signature. This is achieved by using a cryptographic hash function and a secret key. The computed signature can be appended at the end of an electronic document file, or printed on a hardcopy document in the form of a bar code. Using this method, we will be able to detect any intentional content alteration to the document, but at the same time tolerate moderate amount of noise introduced by printing, scanning, and photocopying of the document. Experimental results demonstrate the effectiveness of the proposed approach.