高速SDRAM控制器设计的FPGA实现

同步动态存储器(SDRAM)控制器通常用有限状态机实现,对于一般的设计方法,由于状态数量多,状态转换通常伴随大的组合逻辑而影响运行速度,因此,SDRAM控制器的速度限制了SDRAM存储器的访问速度。该文从结构优化入手来优化方法,利用状态机分解的思想将大型SDRAM控制状态机用若干小的子状态机实现,达到简化逻辑的目的,不仅提高了速度还节省了资源,对该类大型SDRAM控制器的实现有一定参考意义。     

DBF系统的通道校正技术

描述了影响DBF系统特性的主要因素,研究了阵元间互耦对自适应方向图旁瓣和零深的影响及校正方法,讨论了在DBF阵中校正接收通道幅、相误差和I/Q支路正交误差的技术途径。计算机模拟和测试证明,按照所述方法进行校正可以得到满意的结果。另外,为了减小I/Q支路产生正交误差,建议采用中频直接采样和数字化的接收机方案。     

BAYESIAN SUBSET SELECTION AND MODEL AVERAGING USING A CENTRED AND DISPERSED PRIOR FOR THE ERROR VARIANCE

This article proposes a new data‐based prior distribution for the error variance in a Gaussian linear regression model, when the model is used for Bayesian variable selection and model averaging. For a given subset of variables in the model, this prior has a mode that is an unbiased estimator of the error variance but is suitably dispersed to make it uninformative relative to the marginal likelihood. The advantage of this empirical Bayes prior for the error variance is that it is centred and dispersed sensibly and avoids the arbitrary specification of hyperparameters. The performance of the new prior is compared to that of a prior proposed previously in the literature using several simulated examples and two loss functions. For each example our paper also reports results for the model that orthogonalizes the predictor variables before performing subset selection. A real example is also investigated. The empirical results suggest that for both the simulated and real data, the performance of the estimators based on the prior proposed in our article compares favourably with that of a prior used previously in the literature.     

洋桔梗叶盘高频率不定芽诱导的初步研究

采用正交设计法研究了细胞分裂素ZT,生长素NAA,培养基pH值和光照强度对洋桔梗叶盘不定芽诱导的影响.结果表明,实验的4个因子对洋桔梗叶盘不定芽都具有极显著的诱导作用,最佳培养基和培养条件为:MS ZT 0.5 mg/L NAA 0.01 mg/L,培养基pH值为6.3,光照强度为3000 lx.该处理中每叶盘再生不定芽数平均达9.41个.     

Fuzzy K-means clustering models for triangular fuzzy time trajectories

We focus our attention on the classification of fuzzy time trajectories with triangular membership function, described by a given set of individuals. To this purpose, we adopt a fullyinformational approach, explicitly recognizing the informational nature shared by the ingredients of the classification procedure: the observed data (Empirical Information) and the classification model (Theoretical Information). In particular, by supposing that the informational paradigm has a fuzzy nature, we suggest three fuzzy clustering models allowing the classification of the triangular fuzzy time trajectories, based on the analysis of the cross sectional and/or longitudinal characteristics of their components (centers and spreads). Two applicative examples are illustrated.     

Three-level main-effects designs exploiting prior information about model uncertainty

To explore the projection efficiency of a design, Tsai, et al [2000. Projective three-level main effects designs robust to model uncertainty. Biometrika 87, 467–475] introduced the Q criterion to compare three-level main-effects designs for quantitative factors that allow the consideration of interactions in addition to main effects. In this paper, we extend their method and focus on the case in which experimenters have some prior knowledge, in advance of running the experiment, about the probabilities of effects being non-negligible. A criterion which incorporates experimenters’ prior beliefs about the importance of each effect is introduced to compare orthogonal, or nearly orthogonal, main effects designs with robustness to interactions as a secondary consideration. We show that this criterion, exploiting prior information about model uncertainty, can lead to more appropriate designs reflecting experimenters’ prior beliefs.     

Approximation of density functions by orthogonal series with grouped data

Summary Letg(x) andf(x) be continuous density function on (a, b) and let {ϕ_j} be a complete orthonormal sequence of functions onL ₂(g), which is the set of squared integrable functions weighted byg on (a, b). Suppose that over (a, b). Given a grouped sample of sizen fromf(x), the paper investigates the asymptotic properties of the restricted maximum likelihood estimator of density, obtained by setting all but the firstm of the ϑ_j’s equal to0. Practical suggestions are given for performing estimation via the use of Fourier and Legendre polynomial series. Research partially supported by: CNR grant, n. 93. 00837. CT10.     

Implementing partial least squares

Partial least squares (PLS) regression has been proposed as an alternative regression technique to more traditional approaches such as principal components regression and ridge regression. A number of algorithms have appeared in the literature which have been shown to be equivalent. Someone wishing to implement PLS regression in a programming language or within a statistical package must choose which algorithm to use. We investigate the implementation of univariate PLS algorithms within FORTRAN and the Matlab (1993) and Splus (1992) environments, comparing theoretical measures of execution speed based on flop counts with their observed execution times. We also comment on the ease with which the algorithms may be implemented in the different environments. Finally, we investigate the merits of using the orthogonal invariance of PLS regression to improve the algorithms.     

超声波提取胡椒中有效成分条件优化

试验研究温度、时间、超声波频率对超声波-食用乙醇提取胡椒有效成分的影响,以胡椒粗提取物的提取率为指标,正交实验确定较适宜提取条件为,温度55℃、时间55 min,超声波频率60 KHz,胡椒粗提取物的提取率为4.29%.     

Cumulative correspondence analysis using orthogonal polynomials

Taguchi's statistic has long been known to be a more appropriate measure of association of the dependence for ordinal variables compared to the Pearson chi-squared statistic. Therefore, there is some advantage in using Taguchi's statistic in the correspondence analysis context when a two-way contingency table consists at least of an ordinal categorical variable. The aim of this paper, considering the contingency table with two ordinal categorical variables, is to show a decomposition of Taguchi's index into linear, quadratic and higher-order components. This decomposition has been developed using Emerson's orthogonal polynomials. Moreover, two case studies to explain the methodology have been analyzed.