Global stability of periodic solution for bidirectional associative memory neural networks with varying-time delay
This paper investigates the global stability of periodic solution for bidirectional associative memory neural networks by using linear matrix inequality, and presents new sufficient conditions on the existence, uniqueness, global exponential stability and asymptotic stability of periodic solution for bidirectional associative memory neural networks with varying-time delays. In addition, exponential convergence rate is estimated by the equation in the paper. Furthermore, the results in this paper are generalized and the ones reported in the existing literatures are improved. Numerical simulations are given to verify the effectiveness of our main 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: 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: 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: 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: 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: 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.

title: Uplink Multi-User MIMO OFDM Enhancement Using Genetically Improved Turbo Receiver
abstract: Despite the assistance of well-recognized hot technologies such as Multiple-Input Multiple-Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM), cell-edge mobile users still suffer significant performance degradation in uplink, thus restricting the system coverage. In the context of the European B3G/4G WINNER system, we propose a hybrid solution which takes the advantages of both Genetic Algorithm (GA) and turbo processing, resulting in an enhanced MIMO OFDM receiver. Simulation results show that the proposed scheme is capable of achieving a good link performance, whilst improving the robustness of the multi-links and thus the cell coverage.

title: Automated Extraction of Microtubules and Their Plus-Ends
abstract: Though electron tomography opens up new possibilities in imaging the microtubule and the fine plus-end structures, the interpretation of the acquired data remains an obstacle due to the low SNR and the cluttered cellular environment. The automatic extraction of plus-end is especially challenging since they have complex and varying conformations beyond the capacity of existing segmentation methods. We propose an automated approach to extracting the microtubule plus-end with a coarse to fine scale scheme consisting of volume enhancement and plus-end segmentation. To make the segmentation robust against confusing image features, we have fully incorporated the prior knowledge of microtubules and plus-ends into our model-based framework. Experimental results demonstrate that our automated method produces results comparable to the manual segmentation but using only a fraction of the manual segmentation time. The automated approach also segments more fine structures that could be overlooked by human operators.

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.