An improved decision feedback equalization using a priori information

This paper addresses the exhaustive computational complexity of the maximum‐a‐posteriori equalizer and the inefficiency of the conventional decision feedback equalizer (DFE) algorithm in iterative equalization, especially when the higher‐level modulation is used with severely distorted Inter Symbol Interference channels. The new method proposed here improves the bit error rate (BER) performance by computing the extra metric r_n+1 using the feedback symbols from previous iteration and combining it with a priori information of the symbols. After each iteration, the hard‐detected symbols are saved in the memory as a priori data for next iteration. We verified the proposed algorithm for Binary Phase Shift Keying and 8‐phase shift keying modulation. The promising simulation results show that the BER performance given by the proposed low complexity DFE algorithm improved dramatically throughout the iterations when the conventional DFE has only insignificant improvement in the process of iterative equalization. Copyright © 2009 John Wiley & Sons, Ltd.     

水库调度方程组的定界迭代法

在进行水电站水库调度计算中采用逐时段递推计算时，一般求解方法是迭代算法，在生产实际中，现行迭代算法有时出现不收敛现象，通过深入分析发现，如果迭代变量的假设值小于新值，则假设值即为失代变量的下界x-,新值为其上界x^-，否则，假设值为迭代变量的上界x^-，新 值为其下界x-，迭代变量的真值必包含在上下界之间，即x^*∈（x--x^-)。随着迭代次数的增加，迭代区间将逐步减小，直至逼近真值，由此即可构造定界迭代算法，应用实例表明，提出的定界迭代算法原理简单，可保证迭代算法绝对收敛，且与迭代变量的修正方法无关。     

Iterative solvers of Ax = b derived from ode's numerical integration methods

Although iterative methods for solving linear systems has been the subject of study for a long time, the acceleration of such methods is still object of interest, research focusing in improvements of already known methods as well as on new, faster ones. In this sense we can cite among several other authors, for example, the works of Martins [8], Hadjidimos [9] and Evans [7]. The purpose of this paper is twofold. First we show that the numerical integration methods for Ordinary Differential Equations, obtained by Taylor expansions, result in a B-extrapolation method [3] for iteratively solving linear algebraic systems. Second, we compare the best rates of convergence of the algorithms developed here, with the best rate of convergence of the Jacobi Over-Relaxation method, (JOR), proving that depending on the choice of the step of integration and the behavior of the spectrum of the matrix A of the original system Ax = b the third order methods derived from the Taylor expansions can be better than the JOR. The procedure used here with the splitting A – I – J can also be easely applied to other splittings, resulting in a comparison of the convergence of the present method with the SOR or iterations methods or any other linear iterative methods of degree one.     

Hydrogen production from rotten dates by sequential three stages fermentation

This study was devoted for H₂ production from rotten fruits of date palm (Phoenix dactylifera L.) by three fermentation stages. A facultative anaerobe, Escherichia coli EGY was used in first stage to consume O₂ and maintain strict anaerobic conditions for a second stage dark fermentative H₂ production by the strictly anaerobic Clostridium acetobutylicum ATCC 824. Subsequently, a third stage photofermentation using Rhodobacter capsulatus DSM 1710 has been conducted for the H₂ production. The maximum total H₂ yield of the three stages (7.8 mol H₂ mol⁻¹ sucrose) was obtained when 5 g L⁻¹ of sucrose was supplemented to fermentor as rotten date fruits. A maximum estimated cumulative H₂ yield of the three stages (162 LH₂ kg⁻¹ fresh rotten dates) was estimated at the (5 g L⁻¹) sucrose concentration. These results suggest that rotten dates can be efficiently used for commercial H₂ production. The described protocol did not require addition of a reducing agent or flashing with argon which both are expensive.     

A relaxed gradient based algorithm for solving sylvester equations

By introducing a relaxation parameter, we derive a relaxed gradient based iterative algorithm for solving Sylvester equations. Theoretical analysis shows that the new method converges under certain assumptions. Comparisons are performed with the original algorithm, and results show that the new method exhibits fast convergence behavior with a wide range of relaxation parameters. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Iterative Learning Control Algorithm with a Fixed Step

Iterative Learning Control (ILC) captures interests of many scholars because of its capability of high precision control implement without identifying plant mathematical models, and it is widely applied in control engineering. Presently, most ILC algorithms still follow the original ideas of ARIMOTO, in which the iterative-learning-rate is composed by the control error with its derivative and integral values. This kind of algorithms will result in inevitable problems such as huge computation, big storage capacity for algorithm data, and also weak robust. In order to resolve these problems, an improved iterative learning control algorithm with fixed step is proposed here which breaks the primary thought of ARIMOTO. In this algorithm, the control step is set only according to the value of the control error, which could enormously reduce the computation and storage size demanded, also improve the robust of the algorithm by not using the differential coefficient of the iterative learning error. In this paper, the convergence conditions of this proposed fixed step iterative learning algorithm is theoretically analyzed and testified. Then the algorithm is tested through simulation researches on a time-variant object with randomly set disturbance through calculation of step threshold value, algorithm robustness testing,and evaluation of the relation between convergence speed and step size. Finally the algorithm is validated on a valve-serving-cylinder system of a joint robot with time-variant parameters. Experiment results demonstrate the stability of the algorithm and also the relationship between step value and convergence rate. Both simulation and experiment testify the feasibility and validity of the new algorithm proposed here. And it is worth to noticing that this algorithm is simple but with strong robust after improvements, which provides new ideas to the research of iterative learning control algorithms.     

A stencil of the finite-difference method for the 2D convection diffusion equation and its new iterative scheme

The paper gives the numerical stencil for the two-dimensional convection diffusion equation and the technique of elimination, and builds up the new iterative scheme to solve the implicit difference equation. The scheme's convergence and its higher rate of convergence than the Jacobi iteration are proved. And the numerical example indicates that the new scheme has the same parallelism and a higher rate of convergence than the Jacobi iteration.     

Projection data replenishment algorithm for limited angle tomography

A new iterative algorithm of tomographic reconstruction of objects on the basis of projection data available in a limited range of angles only is proposed. The algorithm is based on calculating artificial projections in those directions where projection data are unavailable. By means of numerical simulations, it is verified that the algorithm developed ensured high quality of reconstruction up to the angular interval of 45–60°.     

Efficient calculation of the radiation heat flux surrounding a jet fire

The major objective of the paper is to address the main weakness of the reverse Monte-Carlo method applied to jet fire simulation; its slow convergence rate. This is done by replacing the use of a pseudo-random number generator for calculating ray orientations with a ray direction specification based on Sobol sequences. Sobol sequences produce sequences of points on the unit hemisphere that are not independent of each other in that new points in the sequence avoid previous points generated. This has the property that a more uniform pattern of rays on the unit hemisphere is produced, giving a ray convergence rate for the incident heat flux that is asymptotically equivalent to O(N_Ray⁻¹). The use of Sobol sequences to accelerate convergence of the Monte-Carlo method has been applied in mathematical physics and financial modelling but the results presented here are the first study of quasi-Monte-Carlo methods applied to the incident heat flux integral. The use of Sobol sequences to generate ray directions means that the Central Limit Theorem no longer holds. In its place, it has been demonstrated that it is possible to construct a Gaussian variable from the incident intensity distributions calculated using Sobol sequences. This means it is possible to calculate confidence limits for a prediction of incident heat flux and the confidence limits contract with ray number at a rate of O(N_Ray⁻¹ ln(N_Ray)).     

Iterative algorithm for optimal fiducials under weak perspective projection

In previous work, we designed space fiducials with the aim of making camera pose determination as noise‐insensitive as possible. These fiducials turned out to be sets of points that formed concentric regular polyhedra. Here, we apply an idea of Dementhon and Davis and test and analyze an iterative linear algorithm in conjunction with our optimal fiducials to increase the accuracy of the computed camera pose. We also analyze under what circumstances this iterative algorithm is guaranteed to converge to the correct solution. Comprehensive computer simulations illustrate the behavior of the algorithm and the degree of improvement in pose determination in case of convergence. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 27–36, 2009