stft

matlab学习教程 讲学习的基本内容，对新手很有帮助 英文讲解，但很容易听得懂(matlab tutorial stresses learning to learn the basic content helpful for the novice English to explain, but easy to understand)

一種可用於SAR影像上的應用，並且提供使用者簡單的把圖片慮波得到更好的影像辨識(An application can be used on the SAR images, and provides users with a simple wave to get a better picture into account image recognition)

Matlab code On Fruit quality grading

说明：  matlab 模型保护程序对模型进行保护，编译成obj文件(matlab model protection ,we complier the sfunction .c file to mexw file and .obj file)

在过程控制中，按偏差的比例（P）、积分（I）和微分（D）进行控制的PID控制器（亦称PID调节器）是应用最为广泛的一种自动控制器。(In process control, according to the percentage of deviation (P), Integral (I) and differential (D) control of PID controller (also known as PID regulators) is the most widely used of an automatic controller.)

最新的基于matlab的tdpca的人脸识别的算别和代码，对人脸识别的有帮助(Based on the latest Matlab tdpca face recognition and other numerical code, Face recognition of the help! !)

matlab, these are file-functions for modeling linear oscilyator and pendulum in stable and ustable case.

Turbo code bit interleaved code modulation(BICM)

算术编码的应用实例matlab版本 1.可以对字符进行算术编码 2.开头不必制定字符串的大小 3.编码精度高，达到小数点8位以上 4.以函数的形式给出，易于调用和移值实现 5.显示界面人机交互较出色 (arithmetic coding examples of the application of a version of Matlab. Be right characters arithmetic coding 2. Not the beginning develop the size of strings 3. Coding accuracy, reaching more than eight decimal point four. to function in the form given, easy to call and shifts to achieve 5. HCI interface shows more successful)

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)