gemfull

16进制转浮点的SIMULINK模型。 希望对大家有用！(16 SIMULINK model of binary floating point turn. Hope to be useful!)

dct隐写技术matlab实现资料。算法原理：通过在一个图像块中调整两个DCT系数的相对大小，隐藏秘密信息。 将载体图像分为8×8的块，做二维DCT变换 用来表示选中的两个系数的坐标对于第i bit秘密信息 if（要隐藏信息‘1’） make else make 逆DCT变换，还原图像 (dct steganography matlab implementation information. Algorithm principle: an image block by adjusting the relative size of the two DCT coefficients, hidden secret information. The vector image is divided into 88 blocks, making two-dimensional DCT transform coefficients used to represent the coordinates of the selected two for the first i bit secret information if (to hide information ' 1 ' ) make else make the inverse DCT, restore image)

In signal processing, a window function (also known as an apodization function or tapering function[1]) is a mathematical function that is zero-valued outside of some chosen interval. For instance, a function that is constant inside the interval and zero elsewhere is called a rectangular window, which describes the shape of its graphical representation. When another function or waveform/data-sequence is multiplied by a window function, the product is also zero-valued outside the interval: all that is left is the part where they overlap, the view through the window .

人脸图像的2级小波分解，用MATLAB实现。可得到人脸图像的二级小波分解图。使用时只需把图片名称改成自己所具有的即可(Face feature extraction)

说明：  修改视图图标及标题,能够让程序员使用自己设定的标题及图标(Edit View icon and title, allowing programmers to use their own title and icon set)

An implementation of Frankot and Chellappa a algorithm for constructing an integrable surface from gradient information. matlab file

matlab图像处理实例详解第三章源码程序，推荐大家一起学习，很好很实用。(Detailed examples matlab image processing Chapter III source program, recommend everyone to learn, very very useful.)

These files contain all the code necessary to run the example in the Webinar "Signal Processing for Machine Learning in MATLAB". They also include code to automate the download and preparation of the dataset used. In that webinar we presented an example of a classification system able to identify the physical activity that a human subject is engaged in, solely based on the accelerometer signals generated by his or her smartphone. We used consolidated signal processing methods to extract a fairly small number of highly-descriptive features, and finally trained a small Neural Network to map the feature vectors into the 6 different activity classes of a pre-recorded dataset. The topics discussed include: * Signal manipulation and visualisation * Design and application of digital filters * Frequency-domain analysis * Automatic peak detection * Feature extraction from signals * Train and test of simple Neural Networks

视频中运动人捕捉，MATLAB程序，实验视频需要自己录制，程序已验证(walking body)

考虑单独变量之间的相关性，将其变成扩展变量，再用留一法进行数据处理，最后用SVM进行回归(Consider the correlation between individual variables, to expand into the variable, and then leave one for data processing, and finally with SVM regression)