描述光伏效应的新解析模型

 对陆启生等人提出的描述光伏效应的解析模型涉及的边界条件进行了讨论，提出了一个适用性更宽的解析模型。通过对新模型、陆的模型以及另一个解析模型的比较，对前两个模型能够描述光伏型光电探测器在强光辐照时的信号饱和效应的原因进行了解释。     

微扫描技术对光电图像混淆噪声的影响

在分析采样理论和微扫描理论的基础上,引入了一个评价光电成像系统混淆噪声的品质因数,定量地分析了不同模式的微扫描对系统混淆噪声的改善程度。     

传输线中脉冲信号反射波的测量和应用

将高频单脉冲信号输入同轴电缆，借助数字存储示波器记录输入、输出端的波形和相对延时，可以测量传输线的电缆长度和衰减系数，同时观察脉冲波形因色散引起的畸变．     

基于小波包分析及支持向量机的超音速目标识别方法研究

提出了基于小波包分析及支持向量机的超音速目标识别方法 .通过 5 .5 6mm ,7.6 2mm和 12 .7mm三种枪弹试验获取信号 ,用小波包分解激波信号 ,统计每个频带的能量特征 ,用支持向量机方法训练测试样本 ,获得了很好的分类效果 .仿真结果表明基于超音速飞行体产生的激波信号来识别目标是可行的 .     

调制不稳定性对级联放大光纤传输系统信噪比的影响

在考虑光纤损耗和级联放大器的情况下，推导了调制不稳定性的产生条件和增益的普适解析表达式，分析了调制不稳定性对信噪比的影响，给出了一个新的计算信噪比的表达式。     

变参数波振器中小信号增益公式

利用微扰展开技术推导出变参数波振器的小信号增益两个近似求积表达式并将其计算结果与数值模拟结果进行了比较,二者吻合很好。利用近似解析式对小信号增益进行了初步探讨,得到了电子入射能量可低于初始谐振能,能散度允许范围受波振器设计影响以及降低锥度改变率可提高小信号增益等新结论。     

Model-Free Analysis of Mixtures by NMR Using Blind Source Separation

The concept of blind source separation is described and examples of its use in 1D and 2D NMR spectroscopy are presented. The goal of this data processing method is to extract the spectra of components molecules when only mixtures are available.     

Characterization of semiconductor laser frequency chirp based on signal distortion in dispersive optical fiber

In the paper, the simple method of laser chirp parameters estimation is presented. It is based on measuring time-domain distortions of chirped signal transmitted through dispersive fiber and finding laser chirp parameters by matching measured distortions to calculated ones. Experiments undertaken with 1.55 μm telecommunication grade distributed feedback (DFB) lasers and standard single-mode fiber are described, together with some practical remarks on measurement setup and main conclusions.     

无相移滤波技术用于分析化学信号处理

把无相移滤波技术引入了分析化学信号处理领域。其方法为：先将输入序列按顺序滤波，然后将所得结果逆转后反向通过滤波器，再将所得结果逆转后输出。通过对色谱信号的实验研究表明，与普通的数字滤波方法相比较，无相移滤波不但具有普通数字滤波的优点，而且不会产生滤波前后的相位偏移，具有良好的应用前景，尤其对于需要准确计算保留时间的场合，用此预处理方法非常适合。     

Multivariate calibration of near infrared spectra by orthogonal WAVElet correction using a genetic algorithm

Orthogonal WAVElet correction (OWAVEC) is a pre-processing method aimed at simultaneously accomplishing two essential needs in multivariate calibration, signal correction and data compression, by combining the application of an orthogonal signal correction algorithm to remove information unrelated to a certain response with the great potential that wavelet analysis has shown for signal processing. In the previous version of the OWAVEC method, once the wavelet coefficients matrix had been computed from NIR spectra and deflated from irrelevant information in the orthogonalization step, effective data compression was achieved by selecting those largest correlation/variance wavelet coefficients serving as the basis for the development of a reliable regression model. This paper presents an evolution of the OWAVEC method, maintaining the first two stages in its application procedure (wavelet signal decomposition and direct orthogonalization) intact but incorporating genetic algorithms as a wavelet coefficients selection method to perform data compression and to improve the quality of the regression models developed later. Several specific applications dealing with diverse NIR regression problems are analyzed to evaluate the actual performance of the new OWAVEC method. Results provided by OWAVEC are also compared with those obtained with original data and with other orthogonal signal correction methods.