目标方位估计的相关矩阵最大特征值法

研究均匀线阵(ULA)波束形成时作者发现，当操纵线阵指向方位不断变化，相关矩阵的最大特征值也随着有规律地变化着。通过分析最大特征值的变化，能够估计目标方位和数目。本文从实验数据和计算机仿真两方面对单目标和多目标进行处理，验证上述方法的可行性。     

Correlation of infinite dilution activity coefficients of solvents in polymer solutions using connectivity indices

In this work, correlations for the estimation of the infinite dilution activity coefficients of non-polar solvents in polystyrene (PS), poly(vinyl acetate) (PVAc), poly(n-butyl methacrylate) (PBMA), poly(dimethyl siloxane), poly(methyl methacrylate) (PMMA), poly(ethylene oxide) (PEO), poly(vinyl chloride) (PVC), polyisobutylene and polyethylene (PE), and that of polar solvents in PS, PVAc, PBMA, PMMA, PEO, PVC and PE are proposed. A total of 205 polymer/non-polar solvent systems with 1708 data points, and 118 polymer/polar solvent systems with 695 data points were used to develop the correlations. The overall average errors were 9.6% and 11.3%, respectively, significantly lower than other predictive models. Since the new correlations require only the connectivity indices of the solvents in the calculations, and the connectivity indices can be calculated easily once the molecular structure of the substance in question is known, they are easy to apply, and are useful for process design and development.     

短时非线性预测方法对汉语语音特性的研究

本文应用Short[8-10]改进了的短时非线性预测方法对正常语速的汉语音节和短语进行了分析!究，揭示了汉语语音中浊音和清音的短时非线性预测能力的差异，并且发现这种差异即使在强背景噪声下仍能用短时非线性预测方法加以辨别。这些为浊音和清音的切分提供了一种可能性手段。     

基于光学小波微分预处理的联合变换相关目标识别

提出了一种基于光学小波变换微分预处理的光电混合联合变换相关系统,该系统利用同一光路可实时实现对输入图象的小波变换微分预处理和联合变换相关识别.实验结果表明:该系统能得到很尖锐的互相关峰,可显著提高联合变换相关器的识别能力.     

3（或）4—取代苯甲亚氨酸酯中取代基效应的^13C—NMR研究

本文测定了三个3(或4)-取代苯甲亚氨酸乙酯(1)和八个N-氰甲基-3(或4)-取代苯甲亚氨酸乙酯(2)的~(13)C-NMR谱。归属了1 和2和各碳化学位移。求得了的取代基化学位移(substituent Chemical Shift,SCS)。碳-13化学位移与单取代苯的取代基化学位移(SCS)的相关分析表明:4-取代苯甲亚氨酸酯1 b～g和2b～e中,除C_(2,6)外,其他各芳碳的化学位移值与加和规则基本相符;3-取代苯甲亚氨酸酯1h～j和2f～h中,除C_1外,其他各芳碳的化学位移值与加和规则基本相符。另外,本文还进行了碳-13化学位移与σ_(I)/σ_(R)~O和F/M的双参数相关分析。     

Analysis of patient self-assessment visual analogue scales

The present paper describes an example of practical analysis of data from an application of visual analogue scales. Potentially useful and simple methods for analysing visual analogue scales are described for effective handling of complex data when the desire is to compare two or more groups of patients. Visual analogue scales were used to assess subjective tolerability in the International Prospective Primary Prevention Study in Hypertension, a large scale clinical trial. The methods are illustrated on the IPPPSH data.     

可可西里-金沙江地震带震级频度关系及其参数的相关性分析

本文通过可可西里-金沙江地震带震级频度关系的确定及其参数b值与a值、ν值(地震年平均发生率)和Mmax(最大震级a/b)之间的相关性分析, 以说明地震危险性概率分析中的地震活动性参数ν值与b值也应有某种相关性。当地震带未来一定时期内地震活动震级上限Muz和b值被确定后, 未来地震活动的ν值的取值范围似将受到一定的限制。如假设可可西里-金沙江地震带的Muz为8级, b值为0.73后, 相应的ν值若取13.943, 可能偏小。     

Determination and correlation of the solubility of tricin in water and ethanol mixtures

The solubility of tricin in water and ethanol mixtures was measured over the temperature range of (288.15 to 328.15) K. The concentrations of tricin in the aqueous mixtures were assayed by the ultraviolet spectrophotometric method. The experimental solubility data indicated that the solubility of tricin increases with an increase in temperature and an enrichment in ethanol content. The two models, including the modified Apelblat equation and λh equation were used to correlate the experimental solubility data. The calculated solubility of tricin shows good agreement with the experimental results. Additionally, the estimation of thermodynamic properties including the activity coefficients, dissolution enthalpy, and entropy were obtained from the experimental data. Within the studied temperature range the dissolution process of tricin is endothermic, and the driving force is the entropy.     

ThrEEBoost: Thresholded Boosting for Variable Selection and Prediction via Estimating Equations

Most variable selection techniques for high-dimensional models are designed to be used in settings, where observations are independent and completely observed. At the same time, there is a rich literature on approaches to estimation of low-dimensional parameters in the presence of correlation, missingness, measurement error, selection bias, and other characteristics of real data. In this article, we present ThrEEBoost (Thresholded EEBoost), a general-purpose variable selection technique which can accommodate such problem characteristics by replacing the gradient of the loss by an estimating function. ThrEEBoost generalizes the previously proposed EEBoost algorithm (Wolfson 2011 Wolfson, J. (2011), “EEBoost: A General Method for Prediction and Variable Selection Based on Estimating Equations,” Journal of the American Statistical Association, 106, 296–305.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) by allowing the number of regression coefficients updated at each step to be controlled by a thresholding parameter. Different thresholding parameter values yield different variable selection paths, greatly diversifying the set of models that can be explored; the optimal degree of thresholding can be chosen by cross-validation. ThrEEBoost was evaluated using simulation studies to assess the effects of different threshold values on prediction error, sensitivity, specificity, and the number of iterations to identify minimum prediction error under both sparse and nonsparse true models with correlated continuous outcomes. We show that when the true model is sparse, ThrEEBoost achieves similar prediction error to EEBoost while requiring fewer iterations to locate the set of coefficients yielding the minimum error. When the true model is less sparse, ThrEEBoost has lower prediction error than EEBoost and also finds the point yielding the minimum error more quickly. The technique is illustrated by applying it to the problem of identifying predictors of weight change in a longitudinal nutrition study. Supplementary materials are available online.     

Signal enhancement of a novel multi-address coding lidar backscatters based on a combined technique of demodulation and wavelet de-noising

Multi-address coding (MAC) lidar is a novel lidar system recently developed by our laboratory. By applying a new combined technique of multi-address encoding, multiplexing and decoding, range resolution is effectively improved. In data processing, a signal enhancement method involving laser signal demodulation and wavelet de-noising in the downlink is proposed to improve the signal to noise ratio (SNR) of raw signal and the capability of remote application. In this paper, the working mechanism of MAC lidar is introduced and the implementation of encoding and decoding is also illustrated. We focus on the signal enhancement method and provide the mathematical model and analysis of an algorithm on the basis of the combined method of demodulation and wavelet de-noising. The experimental results and analysis demonstrate that the signal enhancement approach improves the SNR of raw data. Overall, compared with conventional lidar system, MAC lidar achieves a higher resolution and better de-noising performance in long-range detection.