To improve the total throughput of the uplink orthogonal frequency division multiple access system, a low complexity hybrid power distribution (HPD) combined with subcarrier allocation scheme is proposed. For the fairness mechanism for the subcarrier, the inter-cell interference is first analyzed to calculate the capacity of the multi-cell. The user selects the subcarrier with the largest channel gain. Based on the above subcarrier allocation scheme, a new kind of HPD scheme is proposed, which adopts the waterfilling-power-distributed scheme and the equal-power-distributed scheme in the cell-boundary and the cellcenter, respectively. Simulation results show that compared with the waterfilling-power-distributed scheme in the whole cell, the proposed HPD scheme decreases the system complexity significantly, meanwhile its capacity is 2% higher than that of the equal-powerdistributed scheme over the same subcarrier allocation.

The electromagnetic scattering of chiral metamaterials is simulated with the Mie series method. Based on the spherical harmonics vector function in chiral metamaterials, the electromagnetic fields inside and outside of chiral metamaterials sphere are expanded. By applying the continuous boundary condition between the chiral metamaterials and surrounding medium, and the transformation from linearly to circularly polarized electric field components, the co-polarized and cross-polarized bistatic radar cross scattering (RCS) of chiral metamaterials sphere are given. How to overcome the instability of chiral metamaterials sphere of Mie series formula is discussed. The electromagnetic scattering of chiral metamaterials, normal media and metamaterials are compared. The numerical results show that the existence of chirality ξ of chiral  etamaterials can decrease the bistatic RCS compared with the same size as normal media sphere.

An innovative method of cooperative frequency domain differential modulation and demodulation is presented. This method applies the prior knowledge of channel propagation to selecting the variable differential length and carrying out frequency domain modulation. This strategy optimizes the design of system parameters to effectively improve the anti-interference ability of the differential system in time-varied multipath channel circumstance without making the execution more complicating. The simulations and comparisons demonstrate the proposed method is effective, and the results show that it is especially suitable for the fading channel with strong propagation and fast time-variation.

Sparse representation has recently been proved to be a powerful tool in image processing and object recognition. This paper proposes a novel small target detection algorithm based on this technique. By modelling a small target as a linear combination of certain target samples and then solving a sparse 0-minimization problem, the proposed apporach successfully improves and optimizes the small target representation with innovation. Furthermore, the sparsity concentration index (SCI) is creatively employed to evaluate the coefficients of each block representation and simpfy target identification. In the detection frame, target samples are firstly generated to constitute an over-complete dictionary matrix using Gaussian intensity model (GIM), and then sparse model solvers are applied to finding sparse representation for each sub-image block. Finally, SCI lexicographical evalution of the entire image incorparates with a simple threshold locate target position. The effectiveness and robustness of the proposed algorithm are demonstrated by the exprimental results.

Because the conventional ultra wideband (UWB) radar imaging algorithm cannot meet the demand in the capability of multiple targets detection, a novel UWB radar imaging algorithm based on the near field radiation theory of dipole is presented. On the foundation of researching the principle of a time domain imaging algorithm, the back projection (BP) algorithm is derived and analyzed. Firstly, the far field sampling data are transferred to the near field sampling data by using the near field radiation theory of dipole. Then the BP algorithm is applied to target detection. The capability of the new algorithm to detect the multi-target is verified by using the finite-difference time-domain method, and the threedimensional images of targets are obtained. The coupling effect between targets for imaging is analyzed. The simulation results show that the new UWB radar imaging algorithm based on the near field radiation theory of dipole could weaken the coupling effect for imaging, and as a result the quality of imaging is improved.

A new polarization measurement algorithm by using the sum and difference beam differential property of mono-pulse radar is given. Based on the generation mechanism differences between the target scattering and multi-false-target jamming, the signal models of real targets and digital deceptive false target jamming for sum and delta channel are presented. The polarization discrimination parameters are designed, and the discrimination method and its performance are discussed. This novel method does not need the accurate estimation of the absolute value of full target polarization scattering matrix, but only requires the relative estimation of the orthogonal polarized component of the targets. Without the need to add additional polarization channels, the proposed method is more suitable for engineering realization. The simulation experiment verifies that the correctly identifying probability can be better than 90%.

A lot of routing algorithms have been proposed for low earth orbit (LEO) satellite IP networks in recent years, but most of them cannot achieve global optimization. The dynamic characters of LEO satellite networks are reflected in two aspects: topology and traffic change. The algorithms mentioned above are “hard routing” which only realize local optimization. A distributed soft routing algorithm combined with multi-agent system (MASSR) is proposed. In MASSR, mobile agents are used to gather routing information actively, and blackboard is introduced to achieve direct information exchange between agents. MASSR provides traffic adaptive routing and tracks the change of LEO satellite network topology. The performance of ant colony optimization (ACO) and MASSR are compared in Iridium constellation, and MASSR presents better end-to-end delay as well as enhanced robustness.

This paper addresses the problem of sensor search scheduling in the complicated space environment faced by the low-earth orbit constellation. Several search scheduling methods based on the commonly used information gain are compared via simulations first. Then a novel search scheduling method in the scenarios of uncertainty observation is proposed based on the global Shannon information gain and beta density based uncertainty model. Simulation results indicate that the beta density model serves a good option for solving the problem of target acquisition in the complicated space environments.

This paper expresses the efficient outputs of decisionmaking unit (DMU) as the sum of “average outputs” forecasted by a GM (1, N) model and "increased outputs" which reflect the difficulty to realize efficient outputs. The increased outputs are solved by linear programming using data envelopment analysis efficiency theories, wherein a new sample is introduced whose inputs are equal to the budget in the issue No. n + 1 and outputs are forecasted by the GM (1, N) model. The shortcoming in the existing methods that the forecasted efficient outputs may be less than the possible actual outputs according to developing trends of input-output rate in the periods of pre-n is overcome. The new prediction method provides decision-makers with more decisionmaking information, and the initial conditions are easy to be given.

The capture probability of interceptors has been deeply studied. Firstly, the definition of capture probability is analyzed. It is transformed into calculating the probability that the relative position vector between the target and the interceptor locates in a certain cone. The relative position vector and associated covariance matrix are projected in line-of-sight coordinates, and the 3-dimensional integral of a probability function in a cone is calculated to obtain the capture probability. The integral equation is a complicated expression of probability, and it is simplified to an explicit approximate expression according to some assumptions based on the characteristics of the engineering problems. The approximation precision is analyzed by comparative simulation difference, which indicates that approximate assumptions are reasonable. Utilizing the explicit  xpression, the characteristics of capture probability are analyzed respectively with the factors, such as the distance between the interceptor and the target, the precision of relative position vector, the maximum capture distance and the maximum field angle of interceptor seeker.

According to the characteristic of cruise missiles, navigation point setting is simplified, and the principle of route planning for saturation attack and a concept of reference route are put forward. With the help of the shortest-tangent idea in route-planning and the algorithm of back reasoning from targets, a reference route algorithm is built on the shortest range and threat avoidance. Then a route-flight-time algorithm is built on navigation points. Based on the conditions of multi-direction saturation attack, a route planning algorithm of multi-direction saturation attack is built on reference route, route-flight-time, and impact azimuth. Simulation results show that the algorithm can realize missiles fired in a salvo launch reaching the target simultaneously from different directions while avoiding threat.

A new adaptive neural network (NN) output-feedback stabilization controller is investigated for a class of uncertain stochastic nonlinear strict-feedback systems with discrete and distributed time-varying delays and unknown nonlinear functions in both drift and diffusion terms. First, an extensional stability notion and the related criterion are introduced. Then, a nonlinear observer to estimate the unmeasurable states is designed, and a systematic backstepping procedure to design an adaptive NN output-feedback controller is proposed such that the closed-loop system is stable in probability. The effectiveness of the proposed control scheme is demonstrated via a numerical example.

A robust adaptive fuzzy control scheme is presented for a class of strict-feedback nonaffine nonlinear systems with modeling uncertainties and external disturbances by using a backstepping approach. Fuzzy logic systems are employed to approximate the unknown parts of the desired virtual controls, and the approximation errors of fuzzy systems are only required to be norm-bounded. The function tanh( ) is introduced to avoid problems associated with sgn( ). The tracking error is guaranteed to be uniformly ultimately bounded with the aid of an additional adaptive compensation term. Chua's circuit system and Rossler system are presented to illustrate the feasibility and effectiveness of the proposed control technique.

A new method to design a quantum controller which directly controls the probability density function (PDF) of quantum systems is proposed. Based on the quantum model from the PDF perspective, two specific control algorithms are proposed with uniform and non-uniform fields, respectively. Then a detailed control algorithm with convergence analysis is given for the small error case. By appropriately estimating the selected Lyapunov function, more accurate control effect is achieved. The proposed scheme provides a constructive method to find appropriate parameters for controller design.

The problem of fault detection for linear discrete timevarying systems with multiplicative noise is dealt with. By using an observer-based robust fault detection filter (FDF) as a residual generator, the design of the FDF is formulated in the framework of H∞ filtering for a class of stochastic time-varying systems. A sufficient condition for the existence of the FDF is derived in terms of a Riccati equation. The determination of the parameter matrices of the filter is converted into a quadratic optimization problem, and an analytical solution of the parameter matrices is obtained by solving the Riccati equation. Numerical examples are given to illustrate the effectiveness of the proposed method.

Two protocols are presented, which can make agents reach consensus while achieving and preserving the desired formation in fixed topology with and without communication timedelay for multi-agent network. First, the protocol without considering the communication time-delay is presented, and by using Lyapunov stability theory, the sufficient condition of stability for this multi-agent system is presented. Further, considering the communication time-delay, the effectiveness of the protocol based on Lyapunov-Krasovskii function is demonstrated. The main contribution of the proposed protocols is that, as well as the velocity consensus is considered, the formation control is concerned for multi-agent systems described as the second-order equations. Finally, numerical examples are presented to illustrate the effectiveness of the proposed protocols.

This paper deals with the problem of stability for systems with delay varying in an interval. A new Lyapunov functional, which makes use of the information of both the lower and upper bounds of the interval time-varying delay, is proposed to derive some new stability criteria. Furthermore, the relationship of the time-varying delay and its lower bound and upper bound is taken into account. As a result, some less conservative delay-dependent stability criteria are obtained without ignoring any useful information in the derivative of Lyapunov functional, which are established in the forms of linear matrix inequalities. Numerical examples are provided to show that the obtained results are better than existing ones.

A new approach to extraction of affine invariant features of contour image and matching strategy is proposed for shape recognition. Firstly, the centroid distance and azimuth angle of each boundary point are computed. Then, with a prior-defined angle interval, all the points in the neighbor region of the sample point are considered to calculate the average distance for eliminating noise. After that, the centroid distance ratios (CDRs) of any two opposite contour points to the barycenter are achieved as the representation of the shape, which will be invariant to affine transformation. Since the angles of contour points will change non-linearly among affine related images, the CDRs should be resampled and combined sequentially to build one-by-one matching pairs of the corresponding points. The core issue is how to determine the angle positions for sampling, which can be regarded as an optimization problem of path planning. An ant colony optimization (ACO)-based path planning model with some constraints is presented to address this problem. Finally, the Euclidean distance is adopted to evaluate the similarity of shape features in different images. The experimental results demonstrate the efficiency of the proposed method in shape recognition with translation, scaling, rotation and distortion.

The optimal and suboptimal structured algorithms of linear block codes from the geometrical perspective are represented. The minimum distance and weight property lemmas and the theorem are proved for the generator matrix. Based upon the property of generator matrix, the structured algorithms of linear block codes are demonstrated. Since the complexity of optimal structured algorithm is very high, the binary linear block codes is searched by using the suboptimal structured algorithm. The comparison with Bose-Chaudhuri-Hocquenqhem (BCH) codes shows that the searched linear block codes are equivalent on minimum distance and can be designed for more block lengths. Because the linear block codes are used widely in communication systems and digital applications, the optimal and suboptimal structured algorithms must have great future being widely used in many applications and perspectives.

This paper presents a methodology for automatically generating risk scenarios for dynamic reliability applications in which some dynamic characteristics (e.g., the order, timing and magnitude of events, the value of relevant process parameters and initial conditions) have a significant influence on the evolution of the system. The main idea of the methodology is: (i) making the system model "express itself" through simulation by having the model driven by an elaborated simulation engine; (ii) exploiting uniform design to pick out a small subset of representative design points from the space of relevant dynamic characteristics; (iii) for each selected design point, employing a depth-first systematic exploration strategy to cover all possible scenario branches at each branch point. A highly dynamic example adapted from the literature (a chemical batch reactor) is studied to test the effectiveness of the proposed methodology.

A Bayesian sequential testing method is proposed to evaluate system reliability index with reliability growth during development. The method develops a reliability growth model of repairable systems for failure censored test, and figures out the approach to determine the prior distribution of the system failure rate by applying the reliability growth model to incorporate the multistage test data collected from system development. Furthermore, the procedure for the Bayesian sequential testing is derived for the failure rate of the exponential life system, which enables the decision to terminate or continue development test. Finally, a numerical example is given to illustrate the efficiency of the proposed model and procedure.

The network reliability is difficult to be evaluated because of the complex relationship among the network components. It can be quite different for different users running different applications on the same network. This paper proposes a new concept and a model of application reliability. Different from the existing models that ignores the effects of applications, the proposed application reliability model considers the effects of different applications on the network performance and different types of network faults and makes the analysis of network components relationship possible. This paper also provides a method to evaluate the application reliability when the data flow satisfies Markov properties. Finally, a case study is presented to illustrate the proposed network reliability model and the analysis method.