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空气动力学高超声速 简单飞行器 气动力计算 (Aerodynamics of hypersonic aircraft aerodynamic calculations)

系统参数优化算法，可以用于系统的pid参数寻优，在matlab可以直接寻优(System parameter optimization algorithm can be used for the pid of the system parameter optimization, optimization in matlab can)

fuzzy control in matlab

针对线性混合信号盲分离时，源信号概率密度与激活函数难以确定时（尤其是源信号中既含超高斯信号，又含亚高斯信号时），依据信号pdf的一种测度--峭度，自适应的确定激活函数，实现信号的盲分离，是一种基于峭度开关的盲分离算法。(Linear mixed-signal for blind source separation, the source signal probability density function with the activation when it is difficult to determine (especially the source of the signal with both super-Gaussian signals, but also with sub-Gaussian signal), signal pdf based on a measure-- Kurtosis, adaptive activation function of determining and achieving blind signal separation, is a switch-based kurtosis blind source separation algorithm.)

dream theater berjudul dream theater

基于电抗加载的单通道五元多波束扫描天线阵设计方法研究(Based reactance loaded single five yuan multi-beam scanning antenna array design method)

The sourcecode of Universally-Convergent Squared-Operator Iteration Methods for fig5 using MSOM

Simplex Program made on Matlab

本书主要介绍了matlab的图形绘制和用户界面实现技术，书中通过大量例程深入浅出的介绍了图形用户界面的设计和编程。(This book mainly introduces the matlab graphics and user interface technology, the book tells his story through a lot of routines of the graphical user interface design and programming are introduced.)

A. Fusion at the Feature Extraction Level The data obtained from each sensor is used to compute a feature vector. As the features extracted from one biometric trait are independent of those extracted from the other, it is reasonable to concatenate the two vectors into a single new vector. The primary benefit of feature level fusion is the detection of correlated feature values generated by different feature extraction algorithms and, in the process, identifying a salient set of features that can improve recognition accuracy [14]. The new vector has a higher dimension and represents the identity of the person in a different hyperspace. Eliciting this feature set typically requires the use of dimensionality reduction/selection methods and, therefore, feature level fusion assumes the availability of a large number of training data.