A fast and adaptive method for complex-valued SAR image denoising based on <Emphasis Type="Italic">l</Emphasis><Subscript><Emphasis Type="Italic">k</Emphasis></Subscript> norm regularization

This paper developed a fast and adaptive method for SAR complex image denoising based on l _k norm regularization, as viewed from parameters estimation. We firstly establish the relationship between denoising model and ill-posed inverse problem via convex half-quadratic regularization, and compare the difference between the estimator variance obtained from the iterative formula and biased Cramer-Rao bound, which proves the theoretic flaw of the existent methods of parameter selection. Then, the analytic expression of the model solution as the function with respect to the regularization parameter is obtained. On this basis, we study the method for selecting the regularization parameter through minimizing mean-square error of estimators and obtain the final analytic expression, which resulted in the direct calculation, high processing speed, and adaptability. Finally, the effect of regularization parameter selection on the resolution of point targets is analyzed. The experiment results of simulation and real complex-valued SAR images illustrate the validity of the proposed method. Supported by the National Natural Science Foundation of China (Grant No. 60572136), the Fundamental Research Fund of NUDT (Grant No. JC0702005)     

H∞ model reduction of Markovian jump linear systems

In this paper, the H_∞ model reduction problem for linear systems that possess randomly jumping parameters is studied. The development includes both the continuous and discrete cases. It is shown that the reduced order models exist if a set of matrix inequalities is feasible. An effective iterative algorithm involving linear matrix inequalities is suggested to solve the matrix inequalities characterizing the model reduction solutions. Using the numerical solutions of the matrix inequalities, the reduced order models can be obtained. An example is given to illustrate the proposed model reduction method.     

Robust H∞ estimation of stationary discrete-time linear processes with stochastic uncertainties

The problem of H_∞ filtering of stationary discrete-time linear systems with stochastic uncertainties in the state space matrices is addressed, where the uncertainties are modeled as white noise. The relevant cost function is the expected value, with respect to the uncertain parameters, of the standard H_∞ performance. A previously developed stochastic bounded real lemma is applied that results in a modified Riccati inequality. This inequality is expressed in a linear matrix inequality form whose solution provides the filter parameters. The method proposed is applied also to the case where, in addition to the stochastic uncertainty, other deterministic parameters of the system are not perfectly known and are assumed to lie in a given polytope. The problem of mixed H₂/H_∞ filtering for the above system is also treated. The theory developed is demonstrated by a simple tracking example.     

On stability, -gain and control for switched systems

This paper addresses the issues of stability, L₂-gain analysis and H_∞ control for switched systems via multiple Lyapunov function methods. A concept of general Lyapunov-like functions is presented. A necessary and sufficient condition for stability of switched systems is given in terms of multiple generalized Lyapunov-like functions, which enables derivation of improved stability tests, an L₂-gain characterization and a design method for stabilizing switching laws. A solution to the H_∞ control problem for switched systems is also provided.     

On model reduction of K-power bilinear systems

As a special type of bilinear systems, K-power bilinear systems have a special coupled structure that should be preserved in the process of model reduction. We investigate moment matching methods for K-power systems and extract structure-preserved reduced models from the perspective of bilinear systems and coupled systems. The optimal H₂ reduction is also considered for K-power systems. We prove that there exist reduced models satisfying the optimality conditions and meanwhile preserving the coupled structure of the original models. Furthermore, such reduced models can be produced by an iterative algorithm, or alternatively by a subsystem-iteration algorithm with less computational effort and faster convergence rate. Simulation results show that the proposed iterative algorithms possess superior performance in contrast to moment matching methods.     

GH 2 control for uncertain discrete-time-delay fuzzy systems based on a switching fuzzy model and piecewise Lyapunov function

Generalized H2 （GH2） stability analysis and controller design of the uncertain discrete-time Takagi-Sugeno （T-S） fuzzy systems with state delay are studied based on a switching fuzzy model and piecewise Lyapunov function. GH2 stability sufficient conditions are derived in terms of linear matrix inequalities （LMIs）. The interactions among the fuzzy subsystems are considered. Therefore, the proposed conditions are less conservative than the previous results. Since only a set of LMIs is involved, the controller design is quite simple and numerically tractable. To illustrate the validity of the proposed method, a design example is provided.     

Closed form ℒ 2/ℋ2-optimising of zeros for model reduction of linear continuous time systems

Closed form expressions of transfer function responses are applied in this article to model reduction of nth order continuous time systems with respect to the location of zeros for a given location of poles. Explicit closed form formulae are derived for the numerator coefficients in the transfer function of the reduced system that minimise the integrated square deviation from the original system with respect to the impulse response or higher order responses, i.e. effective ?₂ or ?₂-optimisation. The relative degree of the reduced model can be selected freely, e.g. as the original model's one, by selecting the number of numerator coefficients. Constraints are dealt with by introducing a vector of Lagrange multipliers corresponding to the response order. The formulae are also related to solutions of Lyapunov and Sylvester equations based on the companion matrices of the original and reduced systems. The formulae derived can be used to enhance the results obtained from other reduction techniques such as those based on balanced Grammian reduction and singular value decomposition for mid-sized systems. Two examples, demonstrating this, are presented. The formulae can also be used as a basis for a more general optimisation approach, where the optimisation with respect to the numerator coefficients or the zeros, resulting in the solution of a linear system of equations, is combined with non-linear optimisation with respect to the denominator coefficients or the poles.     

Nehari problems and equalizing vectors for infinite-dimensional systems

For a class of infinite-dimensional systems we obtain a simple frequency domain solution for the suboptimal Nehari extension problem. The approach is via J-spectral factorization, and it uses the concept of an equalizing vector. Moreover, the connection between the equalizing vectors and the Nehari extension problem is given.     

Stability and robustness analysis of MIMO MRAC using Kp = L 2 D 2 S 2 factorization

For a class of MIMO systems with input and output unmodeled dynamics, bounded disturbances and any relative degree, using the idea of K_p = L ₂ D ₂ S ₂ factorization, the design and analysis of robust direct model reference adaptive control are further investigated in this article. By establishing the L_p and L _2δ relationship properties between the input and output, multivariable swapping lemmas and relating all the signals in the closed-loop system with the normalizing signal, stability and robustness of adaptive system are analyzed rigorously. Compared with the existing results, the proof procedure is more compact and simple. A simulation example verifies the effectiveness of the control scheme.     

Mixed <Emphasis Type="Italic">H</Emphasis><Subscript>2</Subscript>/<Emphasis Type="Italic">H</Emphasis><Subscript>∞</Subscript> filtering for a class of high-speed sampling uncertain systems

The problem of mixed H2/H∞ filtering for polytopic Delta operator systems is investigated. The aim is to design a linear asymptotically stable filter which guarantees that the filtering error system has different performances in different filtering channels. Based on a parameter-dependent Lyapunov function, a new mixed H2/H∞ performance criterion is presented. Upon this performance criterion, a sufficient condition for the full-order mixed H2/H∞ filter is derived in terms of linear matrix inequalities. The filter can be obtained from the solution of a convex optimization problem. The proposed filter design procedure is less conservative than the strategy based on the quadratic stability notion. A numerical example is given to illustrate the feasibility of the proposed approach.     

Internal positivity preserved model reduction

This article studies model reduction of continuous-time stable positive linear systems under the Hankel norm, H _∞ norm and H ₂ norm performance. The reduced-order systems preserve the stability as well as the positivity of the original systems. This is achieved by developing new necessary and sufficient conditions of the model reduction performances in which the Lyapunov matrices are decoupled with the system matrices. In this way, the positivity constraints in the reduced-order model can be imposed in a natural way. As the model reduction performances are expressed in linear matrix inequalities with equality constraints, the desired reduced-order positive models can be obtained by using the cone complementarity linearisation iterative algorithm. A numerical example is presented to illustrate the effectiveness of the given methods.     

Fast computation of sample entropy and approximate entropy in biomedicine

Both sample entropy and approximate entropy are measurements of complexity. The two methods have received a great deal of attention in the last few years, and have been successfully verified and applied to biomedical applications and many others. However, the algorithms proposed in the literature require O(N²) execution time, which is not fast enough for online applications and for applications with long data sets. To accelerate computation, the authors of the present paper have developed a new algorithm that reduces the computational time to O(N^3/2)) using O(N) storage. As biomedical data are often measured with integer-type data, the computation time can be further reduced to O(N) using O(N) storage. The execution times of the experimental results with ECG, EEG, RR, and DNA signals show a significant improvement of more than 100 times when compared with the conventional O(N²) method for N = 80,000 (N = length of the signal). Furthermore, an adaptive version of the new algorithm has been developed to speed up the computation for short data length. Experimental results show an improvement of more than 10 times when compared with the conventional method for N > 4000.     

Process controllability analysis using linear programming

Controllability analysis is concerned with determining the limitations on achievable dynamic performance. This paper proposes the use of linear programming to determine the best linear controller and corresponding dynamic performance for problems of the form: . That is, a controller, K, and a reference operating point, u₀, are selected to minimise a specified objective, J, while ensuring feasibility for all disturbances, w, within a specified set, W. When K is a linear time invariant (LTI) controller and the objective function J and the constraints c can be expressed as linear functions then the above problem can be solved by linear programming. This formulation encompasses a wide range of problems ranging from minimising the maximum deviation in the regulated outputs subject to disturbances of magnitude less than one (the l₁ optimal control problem) to optimising the expected value of a linear economic objective (the Optimal Linear Dynamic Economics (OLDE) problem). The relationship of this work to other approaches to controllability analysis is discussed. A highly flexible framework for addressing typical process performance requirements through appropriate selection of J, c and W is presented. The relative merits of alternative approaches for defining the achievable closed loop transfer functions based on the -parameterisation are carefully discussed. The feasibility of the proposed approach is demonstrated on an industrial reactor example. The needs for further work are discussed.     

GPU accelerated tensor contractions in the plaquette renormalization scheme

We use the graphical processing unit (GPU) to accelerate the tensor contractions, which is the most time consuming operations in the variational method based on the plaquette renormalized states. Using a frustrated Heisenberg J₁–J₂ model on a square lattice as an example, we implement the algorithm based on the compute unified device architecture (CUDA). For a single plaquette contraction with the bond dimensions C = 3 of each rank of the tensor, results are obtained 25 times faster on GPU than on a current CPU core. This makes it possible to simulate systems with the size 8 × 8 and larger, which are extremely time consuming on a single CPU. This technology successfully relieves the computing time dependence with C, while in the CPU serial computation, the total required time scales both with C and the system size.     

Convergent algorithms for frequency weighted L2 model reduction

This paper is concerned with computing an L₂-optimal reduced-order model for a given stable multivariable linear system in the presence of input and output frequency weightings. By parametrizing a class of reduced-order models in terms of an orthogonal projection and using manifold techniques as tools, both continuous and iterative algorithms are derived and their convergence properties are established. As an application, we show that an L₂ optimal reduced-order filter in the closed-loop sense can be computed using these algorithms.     

Bessel function of the first kind with complex argument

A new method of computing integral order Bessel functions of the first kind J_n(z) when either the absolute value of the real part or the imaginary part of the argument z = x + iy is small, is described. This method is based on computing the Bessel functions from asymptotic expressions when x∼ 0 (or y ∼ 0). These expansions are derived from the integral definition of Bessel functions. This method is necessary because some existing algorithms and methods fail to give correct results for small x small y. In addition, our overall method of computing Bessel functions of any order and argument is discussed and the logarithmic derivative is used in computing these functions. The starting point of the backward recurrence relations needed to evaluate the Bessel function and their logarithmic derivatives are investigated in order to obtain accurate numerical results. Our numerical method, together with established techniques of computing the Bessel functions, is easy to implement, efficient, and produces reliable results for all z.     

Multi-log2N交换网络的性能分析模型及控制算法

高速多平面交换网络解决了其内部冲突问题,但需要相应的路由控制算法的辅助,否则,内部冲突不能彻底解决.这是因为包在输入级路由平面的选择不够恰当,容易导致路由冲突的产生.因此,根据冲突链路集的思想,给出一种Multi-log₂N交换网络的控制算法.该算法控制分组在路由平面间的选择,不仅能够适用于RNB和SNB,还能实现单播和多播的控制,保障Multi-log₂N完全实现无阻塞.另一方面,Multi-log₂N消除了内部的链路冲突,提高了交换速率,但对其交换性能缺乏系统的理论分析.给出一种基于嵌入式马尔可夫链的分析模型,对Multi-log₂N网络中队列的使用及分组在队列中的平均等待时间、平均队长等相关性能指标进行了系统的分析,为基于Multi-log₂N的光交换节点的设计提供了良好的理论依据.     

Discrete J-spectral factorization

This paper deals with the J-spectral factorization for general discrete rational matrices. A simple approach based on the Kalman filtering in Krein space is proposed. The main idea is to construct a stochastic state space filtering model in Krein space such that the spectral matrix of the output is equal to the rational matrix to be factorized. The spectral factor is then easily derived by using the generalized Kalman filtering in Krein space, which is similar to the H₂ spectral factorization. Our approach unifies the treatment of the H₂ spectral factorization and the J-spectral factorization. The applications of the derived results in H_∞ and risk-sensitive estimation for both nonsingular and singular systems are demonstrated.     

A probabilistic solution of robust H∞ control problem with scaled matrices

This paper addresses the robust H_∞ control problem with scaled matrices. It is difficult to find a global optimal solution for this non-convex optimisation problem. A probabilistic solution, which can achieve globally optimal robust performance within any pre-specified tolerance, is obtained by using the proposed method based on randomised algorithm. In the proposed method, the scaled H_∞ control problem is divided into two parts: (1) assume the scaled matrices be random variables, the scaled H_∞ control problem is converted to a convex optimisation problem for the fixed sample of the scaled matrix and a optimal solution corresponding to the fixed sample is obtained; (2) a probabilistic optimal solution is obtained by using the randomised algorithm based on a finite number N optimal solutions, which are obtained in part (1). The analysis shows that the worst case complexity of proposed method is a polynomial.     

On the solution of coupled Riccati equations used in mixed H2 and H∞ optimal control

This paper considers a set of three coupled Riccati equations. The solution of these equations constitutes necessary conditions for mixed H₂ and H_∞ control problems. The main contributions of the paper are related to the conditions under which the solution of these equations is unique as well as to characterize necessary and sufficient conditions for the existence of solutions.