河南省经济发展水平空间自相关性分析

通过GEODA 095i软件对河南省各县市人均GDP进行空间自相关性研究,得出各县市人均GDP间的空间关系和各区域内的空间结构特征,有助于了解河南省经济发展水平的空间差异。     

一种新变步长LMS算法及在自适应波束形成中的应用

LMS算法是智能天线自适应波束形成算法中的经典算法,由于其步长固定,造成收敛速度和稳态失调之间的矛盾。为了解决这一问题,提出一种新的变步长LMS算法,并在算法中引入误差信号的自相关估计,大大降低了噪声的干扰,对算法进行仿真,得到了最优的参数设置。利用蒙特卡罗方法对算法进行了性能评估,与传统的LMS算法、NLMS算法相比,新的变步长LMS算法具有更快的收敛速度和较小的稳态误差及优良的抗噪性能。     

基于EMD-小波包的随钻测量信号去噪方法

为了降低随钻测量过程中噪声对信号的影响,针对经验模态分解(EMD)去噪方法粗糙和小波包去噪方法缺乏自适应的问题,提出了一种基于EMD-小波包的随钻测量信号去噪方法。利用EMD分解自适应的特点将随钻测量信号分解成几个IMF分量;根据信号自相关函数的特性找出主要含噪的IMF分量,再利用小波包阈值去噪方法将含噪IMF分量中的噪声去除;将去噪后的IMF分量和剩余的分量重构,得到去噪后的随钻测量信号。为验证方法的有效性,进行了随钻测量实验并用该方法对采集的信号进行处理。结果表明该方法能够很好地去除随钻测量信号中噪声的干扰,提高信号的信噪比。     

Temporal tracking of spectral variations

We propose a data adaptive spectral estimation algorithm which is suitable for nonstationary estimation situations. This algorithm is based on the conventional Fourier transform of the estimated autocorrelation function. The data adaptive feature is implemented into the autocorrelation function estimation. The algorithm is computationally efficient due to its recursive nature. Its frequency tracking performance is tested against another adaptive algorithm based on the frequently used least mean square algorithm (LMS) of Widrow and Hoff (1960). The two algorithms demonstrate similar performance in many situations. Computer simulations indicate that, when applied to a signal composed of two sinusoids with different power levels, the proposed algorithm tracks the lower-powered sinusoid better than the LMS algorithm.     

基于MPSK信号的实用高精度的载频盲估计研究

在现代电子信息中,精确的载频估计是接收信息正确解调的基础。基于MPSK调制信号提出一种实用性、高精度的载频盲估计新算法。利用自相关函数的原理,把谱相关的计算量降到了一般的数字电路可以实现的程度,并且在低信噪比的情况下估计结果优良;通过粗估计和精估计两次运算来提高载频的盲估计精度。经过理论分析和MATLAB仿真验证说明本算法的正确性和可行性。     

Adaptive Cluster Sampling: Estimating Density of Spatially Autocorrelated Larvae of the Sea Lamprey with Improved Precision

Adaptive cluster sampling (ACS) provides researchers with an alternative technique to estimate the abundance of rare or spatially clustered organisms, but its application in field investigations has been limited to relatively few studies. We used ACS to estimate parameters of a spatially autocorrelated population of larval sea lampreys, Petromyzon marinus, in Wilmot Creek, a Lake Ontario tributary. When compared with simple random sampling (SRS), ACS significantly increased catch per sample as well as the percentage of samples that contained larvae. Although ASC-generated samples are spatially biased, the use of established formulae enabled us to calculate unbiased estimators of mean larval density and variance. With ACS, variance was reduced, improving the precision around estimates of mean density, however; increased precision came at the price of greater sampling effort. When variance was adjusted for effort, ASC provided equal or greater efficiency than SRS in 33% of sampling events, with no apparent site or seasonal bias. Based on the knowledge that larval sea lampreys are spatially aggregated, we anticipated that ACS would result in higher precision for a greater proportion of sampling events. Nonetheless, we consider ACS to be a useful technique for evaluating larval sea lamprey populations and anticipate increased application for investigating other spatially over-dispersed species.     

Minimum Autocorrelation Spreading Codes

Intersymbol Interference (ISI) always appears when the communication channel is a multi-path channel. Many methods are used to reduce the effect of ISI. In this paper we focus on the autocorrelation property of the spreading codes and the role it plays in minimizing the ISI effect, where we obtain the spreading codes with minimum autocorrelation property. The results of comparing the average autocorrelation of the obtained codes with that of the well known Hadamard codes show a great enhancement in the performance, where for example, for codes of length 8 a gain of 408% was achieved at one shift, and for codes of length 16 a gain of 530% was achieved at one shift. Ahmad I. Amayrah was born in Amman in 1976. He received his Master of Science in Electrical Engineering from University of Jordan in 2003. He works as a lecturer in Al-Balqaa' University Abdallah K. Farraj was born in Amman in 1977. He earned his Bachelor of Science and Master of Science degrees in Electrical Engineering from University of Jordan, Amman, Jordan. He is currently working towards a PhD degree at the Department of Electrical and Computer Engineering, Texas A&M University. He is a fellow of the Graduate Teaching Academy, Texas A&M University, and he was awarded the Fulbright Scholarship for the 2005–2006 and 2006–2007 academic years.     

The local spatial autocorrelation and the kernel method for identifying black zones: A comparative approach

This article aims to determine the location and the length of road sections characterized by a concentration of accidents (black zones). Two methods are compared: one based on a local decomposition of a global autocorrelation index, the other on kernel estimation. After explanation, both methods are applied and compared in terms of operational results, respective advantages and shortcomings, as well as underlying conceptual elements. The operationality of both methods is illustrated by an application to one Belgian road.     

Almost Boolean Functions: The Design of Boolean Functions by Spectral Inversion

The design of Boolean functions with properties of cryptographic significance is a hard task. In this paper, we adopt an unorthodox approach to the design of such functions. Our search space is the set of functions that possess the required properties. It is "Boolean-ness" that is evolved.     

偏振模色散自相关函数的实验测量

采用宽光源和光谱分析仪测量偏振模色散自相关函数,来替代传统的可调谐激光器和偏振计的测量方法,得到了与理论分布曲线基本吻合的结果,这种方法大大缩短了测量时间,使得测量结果更接近光纤中偏振模色散的真实情况.