163

自适应信号处理，RLS递归最小二次方算法(Adaptive signal processing, RLS recursive least square algorithm twice)

小波包能量程序，支持向量机组分类时，特征的提取，小波包能量(Wavelet packet energy program, support vector machine classification, feature extraction, wavelet packet energy)

dsp source for matlab (fft ifft frequency)

采用多种方式的调制技术，在通信中具备抗干扰，抗多径等各种功能。(Modulation technique using a variety of ways, in communication with jamming, anti-multipath and other functions.)

八木天线周围 的电流分布，以及水平、垂直、三维方向图(Yagi antenna current distribution, as well as horizontal and vertical, three-dimensional pattern)

程序先进行卷积编码，然后进行viterbi译码，程序有纠错功能，中间出现误码，程序解码时会自动纠正(Convolution coding program first and then viterbi decoding program has error correction, the error appears in the middle, when the program will automatically correct decoding)

It is important to detect the edges of the image.It is not better than canny.

Matlab application, movement of planets

eve plot eve plot eve plot eve plot eve plot eve plot

Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces a new minimum mean square error-based approach to infer the signal subspace in hyperspectral imagery. The method, which is termed hyperspectral signal identification by minimum error, is eigen decomposition based, unsupervised, and fully automatic (i.e., it does not depend on any tuning parameters). It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. State-of-the-art performance of the proposed method is illustrated by using simulated and real hyperspectral images.