geometry

一个用matlab编写的最近邻插值法求图像的范大于缩小的小程序(Matlab prepared using a nearest neighbor interpolation for images larger than narrow the scope of small programs)

This program describes how kalman filter and extended kalman filter reduces error so that we get the better output

OFDM 系统信噪比估计，matlab源代码(OFDM system, SNR estimation, matlab source code)

Diffusion k-tensor Estimation from Q-ball imaging using discretized principal axes

利用四阶统计量求三个信号混合后的独立成分，包括超高斯、高斯和亚高斯信号(Demand the use of three fourth-order statistics of independent mixed signal components, including the super-Gaussian, Gaussian and sub-Gaussian signal)

matlab优化方面的电子书籍。对想学习matlab的同学比较有用(matlab optimization aspects of e-books. Useful to the students want to learn matlab)

基于simulink建立了飞机六自由度模型，并计算其迎角。(Based on simulink established six degrees of freedom model aircraft, and calculate the angle of attack.)

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.)

m a t l a b 编 程 实 例！(matlab program)

谱减法是利用噪声的统计平稳性以及加性噪声与语音不相关的特点而提出来的。即让有语音期间噪声幅度谱的期望值与无语音间隙噪声的幅度谱的期望值相等。(Spectral subtraction method is the use of stationary noise statistics and the additive noise and speech characteristics not related to the initiative. During that speech so that the expected value of noise amplitude spectrum and noise-free audio gap equal to the expected amplitude spectrum.)