FortranandMatlab

超宽带二进制相位调制和功率谱密度、波形图(Ultra-wideband binary-phase modulation and power spectral density, waveform graph)

说明：  最优化学习的Matlab程序:包括最速下降法,黄金分割法,曲线拟合等.(Matlab optimization learning process: including the steepest descent method, golden section method, such as curve fitting.)

用matlab编程进行计算lyapunov指数的程序源码 (using Matlab programming lyapunov index calculation procedures FOSS)

摆动筛，物体左移、右移、跳跃条件，k线，曲线图(Were screening )

This function finds the forward kinematics of the RA-01 Robotic Arm made by Images SI, Inc. The RA-01 has five degrees of freedom. This funciton outputs two vectors. The first vector is the forward kinematics of the central position of the end effector, while the second vector is the forward kinematics of a finger of the end effector (a gripper). Note that it does not matter which finger it is as the position of one finger is simply the negative of the other hence, the value gotten for the y-direction is actually "plus or minus y".

改进加速度平均值的一种跟踪算法。对目标的速度、加速度、运动轨迹等参数进行了对比，与CSM进行了比较。(An improved tracking algorithm acceleration average. The target speed, acceleration, trajectory parameters were compared with CSM compared.)

说明：  ％Jacobi迭代法求方程组等程序，内含有七个解放程组的程序，希望这些程序对大家有一点用处( Jacobi iteration procedures for equations, etc., which contain seven-way group liberation process, we hope that these procedures there is one point on the usefulness of)

自己编的，基于自然梯度的盲源分离算法，如果想对自然梯度有所了解，可以参考Amari的经典文章。网络上一搜就行。(own series, based on the natural gradient algorithm blind source separation, if you want to understand the natural gradient. Amari can refer to the classic article. Networks found on a trip.)

matlab宝典，包括了最简单最实用的实例，帮助新手学习(help greener study matlab easily)

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.