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。     

通过尘障后的激光束特征

从辐射亮度方程出发,推导了在大粒子近似条件下,激光束通过尘障以后约化通量密度和漫射通量密度的表达式,并在四个常用激光波长上作了数值计算.结果表明,当散射粒子浓度较大时,尘障后表面上的漫射通量密度通常比约化通量密度高很多,且散布在较宽的范围内.随着光束远离尘障,漫射通量密度迅速减小,约化通量密度重新占据主导地位.     

三维Minkowski空间中有相等主曲率函数的一类特殊曲面的位置向量场

讨论三维Minkowski空间R2 .1中一类特殊曲面在主曲率函数相等时的情形 ,给出了这类曲面在这一情形下的位置向量场     

复射线ABCD定律及初步应用

根据复变函数解析开拓原理提出了复射线的ＡＢＣＤ定律，并用它说明了高斯光束复参数的ＡＢＣＤ定律与几何先学ＡＢＣＤ定律的内在联系。     

国产梁式引线器件平面集成混频器

介绍鳍线单平衡混频器电路的设计。混频器使用国产梁式引线混频二极管。混频器在31～37GHz的频率范围内,变频损耗小于10dB,最小值为7.2dB。     

Curved Chaotic Map Time Series Models and Their Stochastic Reversals

This paper considers two types of chaotic map time series models, including the well-known tent, logistic and binary-shift maps as special cases; these are called curved tent and curved binary families. Deterministic behaviour is investigated by invariant distributions, Lyapunov exponents, and by serial dependency. Stochastic time reversal of the families is shown to produce models which have a broader range of stochastic and chaotic properties than their deterministic counterparts. The marginal distributions may have concentrations and restricted supports and are shown to be a non-standard class of invariant distribution. Dependenc y is generally weaker with the reversed stochastic models. The work gives a broad statistical account of deterministic and stochastically reversed map models, such as are emerging in random number generation, communica tion systems and cryptography     

空间光通信ATP技术及其进展

对自由空间激光通信光束自动捕获、跟踪、瞄准（ＡＴＰ）系统的结构与系统主要技术指标进行了分析和讨论。通过对其中典型系统的介绍,进一步描述了ＡＴＰ技术的最新进展,论证了空间激光通信的可行性。     

曲线箱梁桥实用设计方法的研究

曲线箱梁桥目前还缺乏比较实用的设计方法．本文对曲线箱梁桥的现有设计方法作了研究，并推荐一种实用设计方法：采用空间梁单元，每个节点取六个自由度；等效梁格的模拟考虑了横向梁格附近有无横隔梁的情况．结果证明，这是一种简便、较精确的设计方法．     

Correlated random effects regression analysis for a log-normally distributed variable

In regression analysis, it is assumed that the response (or dependent variable) distribution is Normal, and errors are homoscedastic and uncorrelated. However, in practice, these assumptions are rarely satisfied by a real data set. To stabilize the heteroscedastic response variance, generally, log-transformation is suggested. Consequently, the response variable distribution approaches nearer to the Normal distribution. As a result, the model fit of the data is improved. Practically, a proper (seems to be suitable) transformation may not always stabilize the variance, and the response distribution may not reduce to Normal distribution. The present article assumes that the response distribution is log-normal with compound autocorrelated errors. Under these situations, estimation and testing of hypotheses regarding regression parameters have been derived. From a set of reduced data, we have derived the best linear unbiased estimators of all the regression coefficients, except the intercept which is often unimportant in practice. Unknown correlation parameters have been estimated. In this connection, we have derived a test rule for testing any set of linear hypotheses of the unknown regression coefficients. In addition, we have developed the confidence ellipsoids of a set of estimable functions of regression coefficients. For the fitted regression equation, an index of fit has been proposed. A simulated study illustrates the results derived in this report.     

A Five-Decision Testing Procedure to Infer the Value of a Unidimensional Parameter

ABSTRACT

A statistical test can be seen as a procedure to produce a decision based on observed data, where some decisions consist of rejecting a hypothesis (yielding a significant result) and some do not, and where one controls the probability to make a wrong rejection at some prespecified significance level. Whereas traditional hypothesis testing involves only two possible decisions (to reject or not a null hypothesis), Kaiser’s directional two-sided test as well as the more recently introduced testing procedure of Jones and Tukey, each equivalent to running two one-sided tests, involve three possible decisions to infer the value of a unidimensional parameter. The latter procedure assumes that a point null hypothesis is impossible (e.g., that two treatments cannot have exactly the same effect), allowing a gain of statistical power. There are, however, situations where a point hypothesis is indeed plausible, for example, when considering hypotheses derived from Einstein’s theories. In this article, we introduce a five-decision rule testing procedure, equivalent to running a traditional two-sided test in addition to two one-sided tests, which combines the advantages of the testing procedures of Kaiser (no assumption on a point hypothesis being impossible) and Jones and Tukey (higher power), allowing for a nonnegligible (typically 20%) reduction of the sample size needed to reach a given statistical power to get a significant result, compared to the traditional approach.