shuanbihuan

kalman滤波的程序及ppt，很有价值，我们做的大作业，欢迎分享(kalman filtering procedures and ppt, great value, we do great work, please share)

说明：  1对一段音频进行matlab双声道限时取样，并画图 2产生一段随机整数噪声，并加入到原样本中(1 on a two-channel audio matlab limited sampling, and drawing a random integer generated noise 2, and added to the original sample)

关于凸优化很好的一本书，是斯坦福大学的，学习凸优化必看的书，就是内容有点多(convex optimization)

MATLAB经典案例系列视频,数据传递和多窗口编程(MATLAB classic case series video, data transfer and multi window programming)

性能比较好的LDA人脸识别代码一份，用lda对人脸进行识别(LDA face recognition code with good performance and recognition of human face with LDA)

高阶谱分析软件。matlab编程环境。整理了相关的软件在一起，使用于相关学习的人下载学习该算法。(High-order spectral analysis software. matlab programming environment. Collate the relevant software together, learning to use in the relevant download learn the algorithm.)

ASK调制解调模块 FSK调制解调模块 4FSK调制解调模块 8FSK调制解调模块 MSK调制解调模块 DBPSK调制解调模块 4PSK调制解调模块 8PSK调制解调模块 (ask)

压缩机的matlab 仿真，燃气轮机的仿真模块(yasuoji simulink)

gray model in matlab analysis

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.