Lucy-Richardson-Algorithm

bilinear interpolation

基于RLS算法的波束整形滤波器MATLAB仿真，可直接实用。(failed to translate)

有详细的注释，利用matlab写成的窄带噪声发生，能量谱分析计算。( There are detailed notes, Using matlab written narrowband noise occurs, Energy spectrum analysis and calculation.)

matlab实现语者识别系统,可以实现录音保存播放功能，在后和库中的声音进行对比，检测出这是谁的声音。(matlab implementation speaker recognition system, can save the recording playback, the voice of the library and after comparison, to detect who is this voice.)

matlab function for electroencefilogram

说明：  数据挖掘算法，K-means聚类算法源代码，用于聚类分析(data mining algorithms, K-means clustering algorithm source code for the cluster analysis)

说明：  利用类间方差阈值算法实现图像的分割处理；利用形态学处理进行处理结果修正。 (Threshold using Otsu algorithm image segmentation use of morphological processing for processing the results of correction.)

基于于MATLAB的π/4 - DQPSK系统仿真源程序，非常有用(Based on MATLAB-π/4- DQPSK system simulation source code, very useful)

将压缩感知用于谱估计中，根据论文谱压缩感知的一些程序(Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse and compressible signals based on randomized dimensionality reduction. To recover a signal from its compressive measurements, standard CS algorithms seek the sparsest signal in some discrete basis or frame that agrees with the measurements. A great many applications feature smooth or modulated signals that are frequency sparse and can be modeled as a superposition of a small number of sinusoids. Unfortunately, such signals are only sparse in the discrete Fourier transform (DFT) domain when the sinusoid frequencies live precisely at the center of the DFT bins. When this is not the case, CS recovery performance degrades significantly. In this paper, we introduce a suite of spectral CS (SCS) recovery algorithms for arbitrary frequency sparse signals. The key ingredients are an over-sampled DFT frame, a signal model that inhibits closely spaced sinusoids, and classical sinusoid parameter e)

chapter 4 sutton ebook exercise