A Modified Projection Neural Network for Linear Variational Inequalities and Quadratic Optimization Problems
Variational inequalities provide us with a tool to study a wide class of optimization arising in pure and applied sciences. In the paper,we present a neural network for solving linear variational inequalities and quadratic optimization by using a projection techniques. We also consider the global uniqueness of the solution of the neural network as well as the convergence of the modified projection neural network. Our results present a significant improvement of previously known projection methods for solving variational inequalities and related optimization problems. Two simulation examples are provided to show the effectiveness of the approach and applicability of the proposed criteria.

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: 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: Stochastic Lotka-Volterra Competitive Systems with Variable Delay
abstract: In this paper we reveal that the environmental noise will not only suppress a potential population explosion in the stochastic Lotka-Volterra competitive systems with variable delay, but also make the solutions to be stochastically ultimately bounded. To reveal these interesting facts, we stochastically perturb the Lotka-Volterra competitive systems with variable delay ẋ(t)=diag(x1(t),...,xn(t))[b+Ax(t−δ(t))] into the Ito form dx(t)=diag(x1(t),...,xn(t))[b+Ax(t−δ(t))]dt+σx(t)dw(t) and show that although the solution to the original delay systems may explode to infinity in a finite time, with probability one that of the associated stochastic delay systems do not. We also show that the stochastic systems will be stochastically ultimately bounded without any additional conditions on the matrix A.

title: Stability of non-autonomous bidirectional associative memory neural networks with delay
abstract: 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: 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: 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: Iterative Joint Channel Estimation and Multi-User Detection for Multiple-Antenna Aided OFDM Systems
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 systems, none of the known channel estimation techniques allows the number of users to be higher than the number of receiver antennas, which is often referred to as a "rank-deficient" scenario, owing to the constraint imposed by the rank of the MIMO channel matrix. Against this background, in this paper we propose a new genetic algorithm (GA) assisted iterative joint channel estimation and multi-user detection (GA-JCEMUD) approach for multi-user MIMO SDMA-OFDM systems, which provides an effective solution to the multi-user MIMO channel estimation problem in the above-mentioned rank-deficient scenario. Furthermore, the GAs invoked in the data detection literature can only provide a hard-decision output for the forward error correction (FEC) or channel decoder, which inevitably limits the system's achievable performance. By contrast, our proposed GA is capable of providing "soft" outputs and hence it becomes capable of achieving an improved performance with the aid of FEC decoders. A range of simulation results are provided to demonstrate the superiority of the proposed scheme.

title: Towards Workflow Oriented Virtual Enterprise Based on Policy Driven and Multi-agent Service Composition
abstract: This paper presents a model of workflow-oriented virtual enterprise based on policy driven and multi-agent service composition. The cooperation of the members in virtual enterprise will be implemented through the dynamic composition and execution of the services, which is workflow-oriented. Each service has its own policies for selection of the suitable services to achieve the cooperation and composition, which are described in WSPL and also registered into UDDI center as same as service description. In the policies, it defines the conditions for composition and cooperation, such as the accepted range of QoS with corresponding price charge, etc. The whole composition process will be executed through the interactions among multi agents, which include business analysis agent, service mapping agent and service allocation agent. Through such policy driven and multi-agent service composition, it can address win-win in the dynamically establishment of the workflow-oriented virtual enterprise.

title: Convergence of the simultaneous algebraic reconstruction technique (SART)
abstract: In 1984, the simultaneous algebraic reconstruction technique (SART) was developed as a major refinement of the algebraic construction technique (ART). Since then, the SART approach remains a powerful tool for iterative image reconstruction. However, the convergence of the SART has never been established. This long-standing conjecture is proven under the condition that coefficients of the linear imaging system are non-negative. It is shown that from any initial value the sequence generated by the SART converges to a weighted least square solution. The importance of the SART and several relevant issues are also discussed.