Heuristic based data scheduling algorithm for OFDMA wireless network

A system model based on joint layer mechanism is formulated for optimal data scheduling over fixed point-to-point links in OFDMA ad-hoc wireless networks. A distributed scheduling algorithm （DSA） for system model optimization is proposed that combines the randomly chosen subcarrier according to the channel condition of local subcarriers with link power control to limit interference caused by the reuse of subcarrier among links. For the global fairness improvement of algorithms, a global power control scheduling algorithm （GPCSA） based on the proposed DSA is presented and dynamically allocates global power according to difference between average carrier-noise-ratio of selected local links and system link protection ratio. Simulation results demonstrate that the proposed algorithms achieve better efficiency and fairness compared with other existing algorithms.     

Identification of linear continuous-time system using wavelet modulating filters

An approach to identification of linear continuous-time system is studied with modulating functions. Based on wavelet analysis theory, the multi-resolution modulating functions are designed, and the corresponding filters have been analyzed. Using linear modulating filters, we can obtain an identification model that is parameterized directly in continuous-time model parameters. By applying the results from discrete-time model identification to the obtained identification model, a continuous-time estimation method is developed. Considering the accuracy of parameter estimates, an instrumental variable (Ⅳ) method is proposed, and the design of modulating integral filter is discussed. The relationship between the accuracy of identification and the parameter of modulating filter is investigated, and some points about designing Gaussian wavelet modulating function are outlined. Finally, a simulation study is also included to verify the theoretical results.     

Combined model based on optimized multi-variable grey model and multiple linear regression

The construction method of background value is improved in the original multi-variable grey model (MGM(1,m)) from its source of construction errors.The MGM(1,m) with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables,and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model.Finally,a combined model of the MGM(1,2) with optimized background value and the binary linear regression is constructed by an example.The results show that the model has good effects for simulation and prediction.     

Game-theoretic approach to power and admission control in hierarchical wireless sensor networks

Power efficiency and link reliability are of great importance in hierarchical wireless sensor networks (HWSNs),especially at the key level,which consists of sensor nodes located only one hop away from the sink node called OHS.The power and admission control problem in HWSNs is comsidered to improve its power efficiency and link reliability.This problem is modeled as a non-cooperative game in which the active OHSs are considered as players.By applying a double-pricing scheme in the definition of OHSs’ utility function,a Nash Equilibrium solution with network properties is derived.Besides,a distributed algorithm is also proposed to show the dynamic processes to achieve Nash Equilibrium.Finally,the simulation results demonstrate the effectiveness of the proposed algorithm.     

Simplified unscented particle filter for nonlinear/non-Gaussian Bayesian estimation

Particle filters have been widely used in nonlinear/nonGaussian Bayesian state estimation problems.However,efficient distribution of the limited number of particles in state space remains a critical issue in designing a particle filter.A simplified unscented particle filter(SUPF) is presented,where particles are drawn partly from the transition prior density(TPD) and partly from the Gaussian approximate posterior density(GAPD) obtained by a unscented Kalman filter.The ratio of the number of particles drawn from TPD to the number of particles drawn from GAPD is adaptively determined by the maximum likelihood ratio(MLR).The MLR is defined to measure how well the particles,drawn from the TPD,match the likelihood model.It is shown that the particle set generated by this sampling strategy is more close to the significant region in state space and tends to yield more accurate results.Simulation results demonstrate that the versatility and estimation accuracy of SUPF exceed that of standard particle filter,extended Kalman particle filter and unscented particle filter.     

Stability condition of FAST TCP in high speed network on the basis of control theory

Considering the instability of data transferred existing in high speed network, a new method is proposed for improving the stability using control theory. Under this method, the mathematical model of such a network is established. Stability condition is derived from the mathematical model. Several simulation experiments are performed. The results show that the method can increase the stability of data transferred in terms of the congestion window, queue size, and sending rate of the source.     

Virtual reality mobility model for wireless ad hoc networks

For wireless ad hoc networks simulation, node＇s mobility pattern and traffic pattern are two key elements. A new simulation model is presented based on the virtual reality collision detection algorithm in obstacle environment, and the model uses the path planning method to avoid obstacles and to compute the node＇s moving path. Obstacles also affect node＇s signal propagation. Considering these factors, this study implements the mobility model for wireless ad hoc networks. Simulation results show that the model has a significant impact on the performance of protocols.     

一种适用于系统仿真的文氏管动态模型

当前用于系统仿真的文氏管模型过于简单,而考虑两相流的CFD模型却难以集成到发动机系统仿真中,通过综合采用数值计算、实验验证、系统辨识的方法,建立了一种适用于系统仿真的4阶文氏管动态模型。计算表明：即使在汽蚀状态下,文氏管也可能出现3.3%的流量波动,而不是所谓的恒定流量源。因此,该模型将有利于提高发动机系统仿真的可靠性。

Abstract：

The models of venturi used in system simulation are always too simple to reflect the real flow process,but the CFD models are too complex to be enclosed into the propulsion system in spite of its accuracy.A 4th order dynamic model of venturi which is applicable to system simulation is brought forward through numerical simulations,experiments and system identifications.Result shows that there is a 3.3% flow fluctuation even under cavitation circumstance,other than constant flow rate.This model is helpful to improve the fidelity of propulsion system simulation.     

New approach to eliminate structural redundancy in case resource pools using α mutual information

Structural redundancy elimination in case resource pools (CRP) is critical for avoiding performance bottlenecks and maintaining robust decision capabilities in cloud computing services. For these purposes, this paper proposes a novel approach to ensure redundancy elimination of a reasoning system in CRP. By using α entropy and mutual information, functional measures to eliminate redundancy of a system are developed with respect to a set of outputs. These measures help to distinguish both the optimal feature and the relations among the nodes in reasoning networks from the redundant ones with the elimination criterion. Based on the optimal feature and its harmonic weight, a model for knowledge reasoning in CRP (CRPKR) is built to complete the task of query matching, and the missing values are estimated with Bayesian networks. Moreover, the robustness of decisions is verified through parameter analyses. This approach is validated by the simulation with benchmark data sets using cloud SQL. Compared with several state-of-the-art techniques, the results show that the proposed approach has a good performance and boosts the robustness of decisions.     

Energy efficient target tracking algorithm using cooperative sensors

Target tracking is one of the applications of wireless sensor networks(WSNs).It is assumed that each sensor has a limited range for detecting the presence of the object,and the network is sufficiently dense so that the sensors can cover the area of interest.Due to the limited battery resources of sensors,there is a tradeoff between the energy consumption and tracking accuracy.To solve this problem,this paper proposes an energy efficient tracking algorithm.Based on the cooperation of dispatchers,sensors in the area are scheduled to switch their working mode to track the target.Since energy consumed in active mode is higher than that in monitoring or sleeping mode,for each sampling interval,a minimum set of sensors is woken up based on the select mechanism.Meanwhile,other sensors keep in sleeping mode.Performance analysis and simulation results show that the proposed algorithm provides a better performance than other existing approaches.     

Prediction-based protocol for mobile target tracking in wireless sensor networks

Remote tracking for mobile targets is one of the most important applications in wireless sensor networks (WSNs). A target tracking protoco–exponential distributed predictive tracking (EDPT) is proposed. To reduce energy waste and response time, an improved predictive algorithm–exponential smoothing predictive algorithm (ESPA) is presented. With the aid of an additive proportion and differential (PD) controller, ESPA decreases the system predictive delay effectively. As a recovery mechanism, an optimal searching radius (OSR) algorithm is applied to calculate the optimal radius of the recovery zone. The simulation results validate that the proposed EDPT protocol performes better in terms of track failed ratio, energy waste ratio and enlarged sensing nodes ratio, respectively.     

Spatial Linear Combination Forecasting Model and Its Application

The accuracy of spatial forecasting is close relation to the selection of spatial forecasting model. Each model from special aspects using special spatial data has its own advantage or disadvantage. A more accurate spatial forecasting model can be obtained by a linear combination of some models. In this study, first-order spatial autoregressive （SAR（1）） model, Kriging algorithm interpolation （KAI） model and back-propagation neural network （BPNN） model are established by using cross-section data or time series data. A spatial linear combination forecasting （SLCF） model is obtained by the combination models mentioned above. An empirical research by these models is carried out with forecasting some areas＇ GDP per capita in Fujian, 2003. It is found that the best one is the SLCF model.     

Dynamics in small worlds of tree topologies of wireless sensor networks

Tree topologies, which construct spatial graphs with large characteristic path lengths and small clustering coefficients,are ubiquitous in deployments of wireless sensor networks. Small worlds are investigated in tree-based networks. Due to link additions, characteristic path lengths reduce rapidly and clustering coefficients increase greatly. A tree abstract, Cayley tree, is considered for the study of the navigation algorithm, which runs automatically in the small worlds of tree-based networks. In the further study, epidemics in the small worlds of tree-based wireless sen-sor networks on the large scale are studied, and the percolation threshold is calculated, at which the outbreak of the epidemictakes place. Compared with Cayley tree, there is a smaller percolation threshold suffering from the epidemic.     

Personal Credit Risk .Scoring Model Based on Rough Set and Neural Network

In order to improve the precision of personal credit risk assessment, applying rough set and neural network to the credit risk scoring prediction problem in an attempt to suggest a new model with better classification accuracy. To evaluate the prediction accuracy of the model, we compare its performance with those of SVM, linear discriminate analysis, logistic regression analysis, K-nearest neighbors, classification and regression tree, neural network and PCA-NN. The experimental results show the model have a very good prediction accuracy     

Analysis for MIMO correlated frequency-selective channel in the presence of interference

The space time spreading, superimposed training sequences, and space-time coding are used to present a multiple input and multiple output （MIMO） systems model, and a closed-form of average error probability upper bound expression for MIMO correlated frequency-selective channel in the presence of interference （co-channel interference and jamming signals） is derived. Moreover, the correlation at both ends of the wireless link that can be incorporated equivalently into correlation at the transmit end is also derived, which is significant to analyze space-time link algorithm of MIMO systems.     

IF signals simulation of three-dimensional radar

Based on the analysis of the principles of frequency-phase scanning 3-D （three-dimensional） radar and the scattering mechanism of 3-D radar, the target and clutter IF （intermediate frequency） signals model of frequencyphase scanning 3-D radar is presented. The IF signals model of different channels of 3-D radar is presented in one simple formula in which complex waveform effects are considered. The simulation results obtained during tests are also provided in the end.     

STAP method based on atomic norm minimization with array amplitude-phase error calibration

In this paper,a space-time adaptive processing(STAP)method is proposed for the airborne radar with the array amplitude-phase error considered,which is based on atomic norm minimization(ANM).In the conventional ANM-based STAP method,the influence of the array amplitude-phase error is not considered and restrained,which inevitably causes performance deterioration.To solve this problem,the array amplitude-phase error is firstly estimated.Then,by pre-estimating the array amplitude-phase error information,a modified ANM model is built,in which the array amplitude-phase error factor is separated from the clutter response and the clutter covariance matrix(CCM)to improve the estimation accuracy of the CCM.To prove that the atomic norm theory is applicable in the presence of the array amplitude-phase error,the clutter sparsity is analyzed in this paper.Meanwhile,simulation results demonstrate that the proposed method is superior to the state-of-the-art STAP method.Moreover,the measured data is used to verify the effectiveness of the proposed method.     

Multi-agent based modeling and simulating for evacuation process in stadium

A multi-agent evacuation model is proposed in this paper to simulate the pedestrian evacuation process in stadium with or without obstacles. The authors give a multi-agent individual decisionmaking framework, in which the action direction of each pedestrian （called agent） is affected by the distance of the agent to the exits and the occupant number and density within the view field of the agent. Different from the existing results, the authors divide all the pedestrians in the stadium into four classes： Young male, young female, old male, and old female. In evacuation process, the weighting that affects individual decision-making between each class of agents is different. In the simulation, the authors present the effects of obstacles, crowd distribution and the exit position in evacuation process. Simulation results show that the proposed model can reproduce exactly the real evacuation process in stadium. Therefore, this method might be useful to assess public buildings design.     

End-to-end rate-based congestion control with random loss: convergence and stability

The convergence and stability analysis for two end-to-end rate-based congestion control algorithms with unavoidable random loss in packets are presented, which can be caused by, for example, errors on wireless links. The convergence rates of these two algorithms are analyzed by linearizing them around their equilibrium points, since they are globally stable and can converge to their unique equilibrium points. Some sufficient conditions for local stability in the presence of round-trip delay are obtained based on the general Nyquist criterion of stability. The stability conditions can be considered to be more general. If random loss in the first congestion control algorithm is not considered, they reduce to the local stability conditions which have been obtained in some literatures. Furthermore, sufficient conditions for local stability of a new congestion control algorithm have also been obtained if random loss is not considered in the second congestion control algorithm.     

Governing Equation and Mathematical Simulation on Ablation and Transpiration Control

This paper represents a physical model of two -dimensional transpiration cooling control system with surface heating and ablating. The governing equation is derived and the third boundary conditions are given. They constitute a two -dimensional variable -domain distributed parameter control system in which the control parameter appears in both the governing equation and the boundary conditions. Applying time semi-step alternating direction method to difference this mathematical model, the tridiagonal difference equations are obtained. For a flying shell of electromagnetic railgun, the mathematical simulation results agree with the realistic physical process, and the control parameter not only can control the temperature of heat shield, but also can control the boundary motion effectively.