High-Order Hopfield Neural Networks
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, the paper will discuss the convergence of high-order Hopfield neural networks. The obtained results ensure that high-order Hopfield neural networks ultimately converge to the equilibrium point 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 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: 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: 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: 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: 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: Soft-Information Assisted Near-Optimum Nonlinear Detection for BLAST-type Space Division Multiplexing OFDM Systems
abstract: In this contribution, a nonlinear hybrid detection scheme based on a novel soft-information assisted genetic algorithm (GA) is proposed for a turbo convolutional (TC) coded space division multiplexing (SDM) aided orthogonal frequency division multiplexing (OFDM) system. Our numerical results show that the performance of the currently known GA-assisted system can be improved by about 2 dB with the aid of the GA's population-based soft solution, approaching the optimum performance of the soft-information assisted maximum likelihood (ML) detection, while exhibiting a lower complexity, especially in high-throughput scenarios. Furthermore, the proposed scheme is capable of achieving a good performance even in the so-called overloaded systems, where the number of transmit antennas is higher than the number of receiver antennas

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: A Surface Reconstruction Method for Highly Noisy Point Clouds
abstract: In this paper we propose a surface reconstruction method for highly noisy and non-uniform data based on minimal surface model and tensor voting method. To deal with ill-posedness, noise and/or other uncertainties in the data we processes the raw data first using tensor voting before we do surface reconstruction. The tensor voting procedure allows more global and robust communications among the data to extract coherent geometric features and saliency independent of the surface reconstruction. These extracted information will be used to preprocess the data and to guide the final surface reconstruction. Numerically the level set method is used for surface reconstruction. Our method can handle complicated topology as well as highly noisy and/or non-uniform data set. Moreover, improvements of efficiency in implementing the tensor voting method are also proposed. We demonstrate the ability of our method using synthetic and real data.

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.