chap9

MATLAB实现图像的DCT变换及选择性重构(MATLAB for image reconstruction of DCT transform )

Enegry of a Speech signal.

IEEE 30 BUS 數據資料 以便大家對於分析配電系統(IEEE 30 BUS data to facilitate analysis of the distribution system)

DC Motor Driver Block using Soft PWM of WiringPi Library

LMS identification volterra order 3,degree 2

多维颜色直方图：：文中利用Matlab软件对真彩色图像高堆直方因数据的可视化进行了实践研究，并给出了 制作高雏直方图的相应程序。高雒直方图的可视化对真彩色图像分割中正确选择合适的彩色模型研 究起到了很好的作用。(multi-dimensional color histogram : : The authors use the Matlab software for true color images directly to the high rockfill for data visualization of practice, and given the high production of child histogram of the corresponding procedures. Luo high histogram visualization of the true color image segmentation correct choice of color model played a very good role.)

image segmentation in multiobjects

Matlab平台DSP自动代码生成技术研究(Research on DSP automatic code generation technology with Matlab platform)

包含1593手写体数字0 ~ 9。从semeion.data通过MATLAB semeion.mat，可以直接使用。 每一个行为样本，其中256是手写数字的16×16，在10栏的数字识别标签，例如：如果第一行是1，然后是0号，其次是1，1。等等。 在Matlab的小例子，可以得出每一个数字，一个更好的理解。你想翻转和旋转的是写作的习惯相一致的图像。(Contains 1593 handwritten digit 0~9. from semeion.data by MATLAB semeion.mat, can be used directly. In addition of MATLAB small example, can draw each digital, a better understanding of. The image you want to flip and rotation is consistent with the habit of writing.)

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