Competition between two kinds of information among random-walking individuals

A model is proposed to describe the competition between two kinds of information among N random-walking individuals in an L x L square, starting from a half-and-half mixture of two kinds of information. Individuals remain or change their information according to their neighbors＇ information. When the moving speed of individuals v is zero, the two kinds of information typically coexist, and the ratio between them increases with L and decreases with N. In the dynamic case （v 〉 0）, only one information eventually remains, and the time required for one information being left scales as Td -v^αL^β^γ.

RESEARCH AND APPLICATION OF A NEURAL NETWORK CLASSIFIER BASED ON DYNAMIC THRESHOLD

In this study, a Multi-Layer BP neural network(MLBP) with dynamic thresholds is employed to build a classifier model.As to the design of the neural network structure, theoretical guidance and plentiful experiments are combined to optimize the hidden layers' parameters which include the number of hidden layers and their node numbers.The classifier with dynamic thresholds is used to standardize the output for the first time, and it improves the robustness of the model to a high level.Finally, the classifier is applied to forecast box office revenue of a movie before its theatrical release.The comparison results with the MLP method show that the MLBP classifier model achieves more satisfactory results, and it is more reliable and effective to solve the problem.

Chaos Control and Synchronization of Cellular Neural Network with Delays Based on OPNCL Control

混乱控制的问题和有延期的细胞的神经网络的完全的同步被学习。加非线性的关上的循环(OPNCL ) 基于户外方法，控制计划和同步计划被设计。两个都，计划能分别地完成混乱控制和混乱神经网络的完全的同步，并且他们的有效性被数字模拟实验进一步验证。

Projective synchronization of a complex network with different fractional order chaos nodes

Based on the stability theory of the linear fractional order system, projective synchronization of a complex network is studied in the paper, and the coupling functions of the connected nodes are identified. With this method, the projective synchronization of the network with different fractional order chaos nodes can be achieved, besides, the number of the nodes does not affect the stability of the whole network. In the numerical simulations, the chaotic fractional order Lu system, Liu system and Coullet system are chosen as examples to show the effectiveness of the scheme.

Prediction of Rolling Load in Hot Strip Mill byInnovations Feedback Neural Networks

Because the structure of the classical mathematical model of rolling load is simple, even with the self-adapting technology, it is difficult to accommodate the increasing dimensional accuracy. Motivated by this fact, an Innovations Feedback Neural Networks （IFNN） was presented based on the idea of Kalman prediction. The neural networks used the Back Propagation （BP） algorithm and applied it to the prediction of rolling load in hot strip mill. The theoretical results and the off-line simulation show that the prediction capability of IFNN is better than that of normal BP networks, namely, for the prediction of the rolling load in hot strip mill, the prediction precision of IFNN is higher than that of normal BP networks. Finally, a relative complete rolling load prediction system was developed on Windows 2003/XP platform using the OOP programming method and the SQL server2000 database. With this sys- tem, the rolling load of a 1700 strip mill was calculated, and the prediction results obtained correspond well with the field data. It shows that IFNN is valid for rolling load prediction.

New method for discretization of continuous attributes in rough set theory

A new method for discretization of continuous attributes is put forward to overcome the limitation of the traditional rough sets, which cannot deal with continuous attributes.The method is based on an improved algorithm to produce candidate cut points and an algorithm of reduction based on variable precision rough information entropy. With the guarantee of consistency of decision system, the method can reduce the number of cut points and im- prove efficiency of reduction. Adopting variable precision rough information entropy as measure criterion, it has a good tolerance to noise. Experiments show that the algorithm yields satisfying reduction results.

AMENDATORY DEFINITION OF SECURITY FOR STEGANOGRAPHIC SYSTEM

The security of steganographic system is significant in this research field. The security defined by relative entropy D (P C || PS )is widely cited to measure the security of different steg-anographic systems. However,two examples are presented to show that some limitations exist in Cachin’s definition. Based on the analysis of a basal hypothesis testing problem,a very useful con-clusion can be drawn to define the security of steganographic system. According to the above illation,an amendatory definition is presented based on the probability of the empirical probability distribution. With the help of new definition,the relationship between security and capacity can be interpreted clearly,and truly secure steganographic method could be designed.

Effect of Trader Composition on Stock Market

在这研究，我们造一个双拍卖市场模型，包含代理人商人的二种类型，，噪音商人和信奉正统派基督教的人，在证券市场上调查商人作文的效果。它被发现那，重要林中小丘代表和办理价格的跟踪脂肪的分发能在噪音商人的任何比率被观察。在价格变化性质上分析，包括林中小丘代表和价格变化区域，当比率是中等的时，这些性质是稳定的表演。然而，包括交易量和二种代理人的盈利，非价格变化性质不在噪音商人的比率的任何间隔 untrivially 保持稳定。

Chaotic Synchronization of Two Electrical Coupled Neurons with Unknown Parameters Based on Adaptive Control

Chaotic synchronization of two electrical coupled FitzHugh-Nagumo(FHN)neurons with unknown parameters via adaptive control is investigated.Based on the Lyapunov stability theory,an adaptive controller and a parameter update law are designed,which can achieve the synchronization of the two gap junction coupled FHN neurons when the individual neuron is chaotic,without considering the coupling strength.Moreover,the unknown parameters are identified successfully and the controller is robust to the random noise.The numerical simulation results confirm the effectiveness of the designed controller.

FABRICATION OF PIEZOELECTRIC BIMORPH USING LEAD ZIRCONATE TITANATE THIN FILM DEPOSITED BY HYDROTHERMAL METHOD

In order to describe the characteristics of piezoelectric bimorph, properties of lead zirconate titanate （LZT） film are studied by X-ray diffraction （XRD） and scanning eletron microscope （SEM）. The ratio of PbTiOJPbZrO3 in LZT is 53/47, which is around morphotropic phase boundary （MPB）. LZT film is composed of cubic particles with the average size of 5 ~ma. Density of thin film is figured out through the datum measured in experiments. The displacement model used to analyze the driving ability of bimorph is set up, and the effect of elastic intermediate layer is taken into account. Piezoelectric coefficient of LZT film is worked out by using the displacement model. Experiments of driving ability show that deformation of bimorph free end does not increase with times of crystal growth processes and the maximum deformation is obtained after two times crystal growth processes. Finally, the ferroelectric property of the bimorph is investigated and coercive voltage of the bimorph is obtained.

The information content of rules and rule sets and its application

The information content of rules is categorized into inner mutual information content and outer impartation information content. Actually, the conventional objective interestingness measures based on information theory are all inner mutual information, which represent the confidence of rules and the mutual information between the antecedent and consequent. Moreover, almost all of these measures lose sight of the outer impartation information, which is conveyed to the user and help the user to make decisions. We put forward the viewpoint that the outer impartation information content of rules and rule sets can be represented by the relations from input universe to output universe. By binary relations, the interaction of rules in a rule set can be easily represented by operators： union and intersection. Based on the entropy of relations, the outer impartation information content of rules and rule sets are well measured. Then, the conditional information content of rules and rule sets, the independence of rules and rule sets and the inconsistent knowledge of rule sets are defined and measured. The properties of these new measures are discussed and some interesting results are proven, such as the information content of a rule set may be bigger than the sum of the information content of rules in the rule set, and the conditional information content of rules may be negative. At last, the applications of these new measures are discussed. The new method for the appraisement of rule mining algorithm, and two rule pruning algorithms, λ-choice and RPClC, are put forward. These new methods and algorithms have predominance in satisfying the need of more efficient decision information.

SECURE STEGANOGRAPHY BASED ON BINARY PARTICLE SWARM OPTIMIZATION

The objective of steganography is to hide message securely in cover objects for secret communication.How to design a secure steganographic algorithm is still major challenge in this re-search field.In this letter,developing secure steganography is formulated as solving a constrained IP(Integer Programming) problem,which takes the relative entropy of cover and stego distributions as the objective function.Furthermore,a novel method is introduced based on BPSO(Binary Particle Swarm Optimization) for achieving the optimal solution of this programming problem.Experimental results show that the proposed method can achieve excellent performance on preserving neighboring co-occurrence features for JPEG steganography.

High Resolution Multipath Time Delay Estimation Based on FLOCCS-ESPRIT

The non-Gaussian α-stable distribution is introduced to model impulsive noise. Based on the theory of fractional lower order statistics (FLOS), the fractional lower order cross-covariance (FLOCC) sequence for two received signals is obtained and the fractional lower order cross-covariance spectrum (FLOCCS) can be approached by taking a Fourier transform for the FLOCC sequence. When the FLOCCS is treated as a sequence in the time domain, the problem of multipath time delay estimation (TDE) may be converted into one on multi-frequencies estimation or directions of arrival estimation. Accordingly, the high resolution multipath TDE can be realized with the ESPRIT technology. This idea on multipath TDE is referred to as FLOCCS-ESPRIT in this paper. Computer simulations show that this method has good performance both in a Gaussian noise and in an impulsive noise environment.

A new family of two-dimensional triple-codeweight asymmetric optical orthogonal code for OCDMA networks

A new generation algorithm of two-dimensional triple-codeweight asymmetric optical orthogonal codes for optical code division multiple access （OCDMA） networks is proposed. The code cardinality is obtained and the error-probability performance for corresponding OCDMA system is analyzed. The codes with two constraints （i.e., auto- and cross-correlation properties） being unequal are taken into account. On the premise of fixed system resources, the code of the error-probability performance, it is shown cardinality can be significantly improved. By analysis that the codes with different parameters have different performances. Therefore, this type of codes can be applied to support diverse quality of service （QoS） and satisfy the quality requirement of different multimedia or distinct users, and simultaneously make the better use of bandwidth resources in oDtical networks.

Spectrum study of top-emitting organic light-emitting devices with micro-cavity structure

Blue and white top-emitting organic light-emitting devices OLEDs with cavity effect have been fabricated. TBADN：3%DSAPh and Alq3：DCJTB/TBADN：TBPe/Alq3：C545 were used as emitting materials of microcavity OLEDs. On a patterned glass substrate, silver was deposited as re？ective anode, and copper phthalocyanine （CuPc） layer as HIL and 4’-bis[N-（1-Naphthyl）- N-phenyl-amino]biphenyl （NPB） layer as HTL were made. Al/Ag thin films were made as semi-transparent cathode with a transmittance of about 30%. By changing the thickness of indium tin oxide ITO, deep blue with Commission Internationale de L’Eclairage chromaticity coordinates （CIEx,y） of （0.141, 0.049） was obtained on TBADN：3%DSAPh devices, and different color（red, blue and green）was obtained on Alq3：DCJTB/TBADN：TBPe/Alq3：C545 devices, full width at half maxima （FWHM） was only 17 nm. The spectral intensity and FWHM of emission in cavity devices have also been studied.

A Discrete Newton's Method for Gain Based Predistorter

Gain based predistorter (PD) is a highly effective and simple digital baseband predistorter which compensates for the nonlinear distortion of PAs. Lookup table (LUT) is the core of the gain based PD. This paper presents a discrete Newton’s method based adaptive technique to modify LUT. We simplify and convert the hardship of adaptive updating LUT to the roots finding problem for a system of two element real equations on athematics. And we deduce discrete Newton’s method based adaptive iterative formula used for updating LUT. The iterative formula of the proposed method is in real number field, but secant method previously published is in complex number field. So the proposed method reduces the number of real multiplications and is implemented with ease by hardware. Furthermore, computer simulation results verify gain based PD using discrete Newton’s method could rectify nonlinear distortion and improve system performance. Also, the simulation results reveal the proposed method reaches to the stable statement in fewer iteration times and less runtime than secant method.

CT reconstruction from a single X-ray image for a particular patient via progressive learning

Computed tomography(CT)has enjoyed widespread applications,especially in the assistance of clinical diagnosis and treatment.However,fast CT imaging is not available for guiding adaptive precise radiotherapy in the current radiation treatment process because the conventional CT reconstruction requires numerous projections and rich computing resources.This paper mainly studies the challenging task of 3 D CT reconstruction from a single 2 D X-ray image of a particular patient,which enables fast CT imaging during radiotherapy.It is widely known that the transformation from a 2 D projection to a 3 D volumetric CT image is a highly nonlinear mapping problem.In this paper,we propose a progressive learning framework to facilitate 2 D-to-3 D mapping.The proposed network starts training from low resolution and then adds new layers to learn increasing high-resolution details as the training progresses.In addition,by bridging the distribution gap between an X-ray image and a CT image with a novel attention-based 2 D-to-3 D feature transform module and an adaptive instance normalization layer,our network obtains enhanced performance in recovering a 3 D CT volume from a single X-ray image.We demonstrate the effectiveness of our approach on a ten-phase 4 D CT dataset including 20 different patients created from a public medical database and show its outperformance over some baseline methods in image quality and structure preservation,achieving a PSNR value of 22.76±0.708 dB and FSIM value of 0.871±0.012 with the ground truth as a reference.This method may promote the application of CT imaging in adaptive radiotherapy and provide image guidance for interventional surgery.