huffman

Matlab编写的弹道分析算法-MATLAB源代码(Trajectory analysis algorithms written in Matlab-MATLAB source code)

Newton Rhaś on method matlab

产生不同频率、不同幅度的两种正弦波信号 对两个信号进行叠加并做频谱分析 还原原来两个信号（LPF)(Produce different frequency, the two sine wave signals of different magnitudes of the two signals are superimposed and do spectral analysis to restore the original two signals (LPF))

本书给出很多有关小波变换的相关例子，有的源码很经典，值得大家学习！(The book makes a lot of relevant example of wavelet transform, some source code very classic, worth learning!)

高光谱遥感图像异常检测算法KRX算法matlab程序(Hyperspectral remote sensing image anomaly detection algorithm KRX algorithm)

对adc采样信号进行频谱分析，计算信噪比，有效位数等等重要参数(Adc sampling signal for spectrum analysis, calculate SNR, the effective number of bits so the important parameters)

ests for KeyGenerator constructor and methods.

How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)

使用线性调频信号构造雷达回波，并对其进行距离维和多普勒维处理(Using a linear FM signal constructed radar echo, and its distance-dimensional, and Duopulewei at)

imnverted pendulum bymyself