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Overview on the prior designs of the mixed-voltage I/O buffers is provided in this work. A new 2.5/5-V mixed-voltage I/O buffer realized with only thin gate-oxide devices is proposed. The new proposed mixed-voltage I/O buffer with simpler dynamic n-well bias circuit and gate-tracking circuit can prevent the undesired leakage current paths and the gate-oxide reliability problem, which occur in the conventional CMOS I/O buffer. The new mixed-voltage I/O buffer has been fabricated and verified in a 0.25-μm CMOS process to serve 2.5/5-V I/O interface. Besides, another 2.5/5-V mixed-voltage I/O buffer without the subthreshold leakage problem for high-speed applications is also presented in this work. The speed, power consumption, area, and noise among these mixed-voltage I/O buffers are also compared and discussed. The new proposed mixed-voltage I/O buffers can be easily scaled toward 0.18- μm (or below) CMOS processes to serve other mixed-voltage I/O interfaces, such as 1.8/3.3-V interface. [ABSTRACT FROM AUTHOR]

We propose a new technique for controlling disorder in traffic system. A kind of control signal which can be considered as a perturbation has been designated at a given site (perturbation point) of the single-lane highway. When a vehicle passes the perturbation point at a time, the velocity of the vehicle will be changed at the next time by the perturbation. This technique is tested for the deterministic NaSch traffic model. The simulation results indicate that the traffic system can be transited from the disorder states to the order states, such as fixed-point, periodic motion, etc.

We introduce the Olami-Feder-Christensen (OFC) model on a square lattice with some "rewired" long-range connections having the properties of small world networks. We find that our model displays the power-law behavior, and connectivity topologies are very important to model's avalanche dynamical behaviors. Our model has some behaviors different from the OFC model on a small world network with "added" long-range connections in our previous work [LIN Min, ZHAO Xiao-Wei, and CHEN Tian-Lun, Commun. Theor. Phys. (Beijing, China) 41 (2004) 557.].

In this paper, we introduce a new modified evolution model on a small world network. In our model, the spatial and temporal correlations and the spatial-temporal evolve pattern of mutating nodes exhibit some particular behaviors different from those of the original BS evolution model. More importantly, these behaviors will change with , the density of short paths in our network.

Based on the standard self-organizing map neural network model and an integrate-and-fire mechanism, we investigate the effect of the nonlinear interactive function on the self-organized criticality in our model. Based on these we also investigate the effect of the refractoryperiod on the self-organized criticality of the system.

A modified Olami-Feder-Christensen model of self-organized criticality on a square lattice with the properties of small world networks has been studied. We find that our model displays power-law behavior and the exponent ?of the model depends on ?, the density of long-range connections in our network.

Two methods are presented for controlling spatiotemporal chaotic motion in coupled map lattices to a kind of periodic orbit where the dynamical variables of all lattice sites are equal and act periodically as time evolves. Stability analysis of the periodic orbits is presented. We prove that especially the second controlling method can stabilize all the periodic orbits we concern. Basin of attraction and noise problem are discussed.

Based on the standard self-organizing map neural network model and an integrate-and-fire mechanism, we introduce a kind of coupled map lattice system to investigate scale-invariance behavior in the activity of model neural populations. We let the parameter ?, which together with ? represents the interactive strength between neurons, have different function forms, and we find the function forms and their parameters are very important to our model's avalanche dynamical behaviors, especially to the emergence of different avalanche behaviors in different areas of our system.

Based on the standard self-organizing map (SOM) neural network model and an integrate-and-fire mechanism, we introduce a kind of coupled map lattice system to investigate scale-invariance behavior in the activity of model neural populations. We find power-law distribution behavior of avalanche size in our model. But more importantly, we find there are different avalanche distribution behaviors in different specific areas of our system, which are formed by the topological learning process of the SOM net.

A new prediction technique is proposed for chaotic time series. The usefulness of the technique is that it can kick off some false neighbor points which are not suitable for the local estimation of the dynamics systems. A time-delayed embedding is used to reconstruct the underlying attractor, and the prediction model is based on the time evolution of the topological neighboring in the phase space. We use a feedforward neural network to approximate the local dominant Lyapunov exponent, and choose the spatial neighbors by the Lyapunov exponent. The model is tested for the Mackey-Glass equation and the convection amplitude of lorenz systems. The results indicate that this prediction technique can improve the prediction of chaotic time series.

In the research of using Radial Basis Function Neural Network (RBF NN) forecasting nonlinear time series, we investigate how the different clusterings affect the process of learning and forecasting. We find that ?-means clustering is very suitable. In order to increase the precision we introduce a nonlinear feedback term to escape from the local minima of energy, then we use the model to forecast the nonlinear time series which are produced by Mackey-Glass equation and stocks. By selecting the ?-means clustering and the suitable feedback term, much better forecasting results are obtained.

Through studying several kinds of chaotic mappings' distributions of orbital points, we analyze the capability of the chaotic mutations based on these mappings. Numerical experiments support our conclusions very well. The capability analysis also led to a self-adaptive mechanism of chaotic mutation. The introducing of the self-adaptive chaotic mutation can improve the performance of genetic algorithm very prominently.

A new nonlinear prediction technique is proposed by feedforward neural network, the learning algorithm for network is a chaotic one. A time-delay embedding is used to reconstruct the underlying attractor, the prediction model is based on the time evolution of the topological neighboring in the phase space, the spatial neighbors are chosen by the rate of exponential divergence of close trajectory. The model is tested for the Mackey-Glass delay equation and Lorentz equations, good results are obtained for the prediction.

Through replacing Gaussian mutation operator in real-coded genetic algorithm with a chaotic mapping, we present a genetic algorithm with chaotic mutation. To examine this new algorithm, we applied our algorithm to function optimization problems and obtained good results. Furthermore the orbital points' distribution of chaotic mapping and the effects of chaotic mutation with different parameters were studied in order to make the chaotic mutation mechanism be utilized efficiently.

Based on the LISSOM neural network model, we introduce a model to investigate self-organized criticality in the activity of neural populations. The influence of connection (synapse) between neurons has been adequately considered in this model. It is found to exhibit self-organized criticality (SOC) behavior under appropriate conditions. We also find that the learning process has promotive influence on emergence of SOC behavior. In addition, we analyze the influence of various factors of the model on the SOC behavior, which is characterized by the power-law behavior of the avalanche size distribution.

A nonlinear feedback term is introduced into the evaluation equation of weights of the backpropagation algorithm for neural network, the network becomes a chaotic one. For the purpose of that we can investigate how the different feedback terms affect the process of learning and forecasting, we use the model to forecast the nonlinear time series which is produced by Makey-Glass equation. By selecting the suitable feedback term, the system can escape from the local minima and converge to the global minimum or its approximate solutions, and the forecasting results are better than those of backpropagation algorithm.

Through adding a nonlinear self-feedback term in the evolution equations of neural network, we introduced a transiently chaotic neural network model. In order to utilize the transiently chaotic dynamics mechanism in optimization problem efficiently, we have analyzed the dynamical procedure of the transiently chaotic neural network model and studied the function of the crucial bifurcation parameter which governs the chaotic behavior of the system. Based on the dynamical analysis of the transiently chaotic neural network model, chaotic annealing algorithm is also examined and improved. As an example, we applied chaotic annealing method to the traveling salesman problem and obtained good results.

In this paper, we construct a uniform second-order difference scheme for a class of boundary value problems of fourth-order ordinary differential equations. Finally, a numerical example is given.

In this paper we construct a completely exponentially fitted finite difference scheme for the boundary value problem of differential equation with turning points, extending Miller's method[1] and simplifying the method of the proof. We prove the first order uniform convergence of the scheme. The numerical results show that it is better than that scheme.

In this paper we studied the multi-scale symmetry breaking for unification model probably including both light monopoles and fractons. It is found that for the given value of Weinberg angle, the mass of monopole is upper-bounded and the relation between the mass of monopole and the coupling constant of is obtained.

The Monte Carlo renormalization group method is applied to discussing the nature of phase transition of XY model on 2-dimensional random triangle lattices. A line of fixed point and un-universal phase transition are found. The results are in agreement with Kosterlitz-Thouless theory. The susceptibility shows a clear size-dependent low temperature region. This means that it should be divergent in this region.

Using an optimal Monte Carlo renormalization group method, the scaling exponent for six-state Potts model on 2-dimensional random triangle lattice is studied. The scaling exponent , is consistent with the expectant value of the scaling theory.

We have studied the time response of the capacitance of nematic liquid crystal cells by applying electric fields. When AC fields are applied, the capacitance exhibited a normal behavior: it first increased monotonically with time, and then reached a saturating value. With sufficient high DC fields, capacitance bounces and bumps were observed during the rising time for 100-µm thick cells with low and high impurity concentrations, respectively. We believe that these anomalous capacitance characteristics of liquid crystal cells are resulted from the coupling effects between induced polarization from liquid crystal molecules and space-charge polarization from fast ions.