完美的压缩感知中的稀疏度自适应算法MATLAB程序

matlab时间序列分析工具箱，各种时频分析方法的matlab源码。(matlab time series analysis toolbox, various time-frequency analysis methods matlab source.)

Matlab语音处理工具箱，很多语音处理m文件(Speech Processing Toolbox Matlab, many voice processing document m)

A Two-Level 24-Pulse Voltage Source Converter with Fundamental Frequency Switching for HVDC System

老外写的一个分割脑部组织的matlab代码，可有效分割出白质和灰质，并且计算体积，处理的图像为dicom图像，支持3d图像分割。

watershed based road segmentation

遗传算法从代表问题可能潜在的解集的一个种群开始的，而一个种群则由经过基因编码的一定数目的个体组成。本代码以旅行商问题为例，详细介绍遗传算法的使用方法。(A genetic algorithm the representation of the solution set of potentially the beginning, but after a certain number of the population the gene encoding the individual components. The code to the traveling salesman problem, for example, describes the use of genetic algorithms.)

Matlab document about induction motor softstart

灰度梯度共生矩阵 H 归一化灰度梯度矩阵 H_basic 小梯度优势 T1 大梯度优势 T2 灰度分布的不均匀性 T3 梯度分布的不均匀性 T4 能量 T5 灰度平均 T6 梯度平均 T7 灰度均方差 T8 梯度均方差 T9 相关 T10 灰度熵 T11 梯度熵 T12 混合熵 T13 惯性 T14 逆差矩 T15(GGCM H normalized gray gradient matrix H_basic small advantage T1 gradient big advantage T2 gradient gray uneven distribution of T3 gradient uneven distribution of energy T5 gray T4 average T6 average gradient gray T7 average gradient T8 variance variance T9 were related to T10 T11 gradient gray entropy entropy of mixing entropy T12 T13 inertia moment T15 T14 deficit)

Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of speech production, for recovering compressive sensed speech. Since the linear transform is signal dependent and unknown, unlike the standard CS formulation, a codebook of transfer functions is proposed in a matching pursuit (MP) framework for CS recovery. It is found that MP is efficient and effective to recover CS encoded speech as well as jointly estimate the linear model. Moderate number of CS measurements and low order sparsity estimate will result in MP converge to the same linear transform as direct VQ of the LP vector derived the original signal. There is also high positive correlation between signal domain approximation and CS measurement domain approximation for a large variety of speech spectra.

说明：  几种相干信号的doa估计方法，如空间平滑等，可以轻松实现解相干。亲测可用(Several DOA estimation methods of coherent signals, such as spatial smoothing, can easily achieve decoherence. Pro test available)