SVD-VS-NMF

Matlab source code for DCT-based watermarking for grayscale images. DCT-based watermark recovering without resorting to the uncorrupted original image. Digital watermarking has been proposed as a viable solution to the need of copyright protection and authentication of multimedia data in a networked environment, since it makes possible to identify the author, owner, distributor or authorized consumer of a document. A new watermarking technique to add a code to digital images is presented: the method operates in the frequency domain embedding a pseudo-random sequence of real numbers in a selected set of DCT coefficients. Watermark casting is performed by exploiting the masking characteristics of the Human Visual System, to ensure watermark invisibility. The embedded sequence is extracted without resorting to the original image, so that the proposed technique represents a major improvement to methods relying on the comparison between the watermarked and original images.

electric machines analysis

用PCA在一组数维中找到主十得到主元分析，用于MACHINE LEARNING 中初步学习者(use pca find the principle component int the dataset)

用MATLAB中的SIMULINK工具实现DDS的仿真模型，不过这里的控制字是常量(SIMULINK with MATLAB tools in DDS realize the simulation model, but here the control word is a constant)

The objective of this project is to design and simulate a single phase half-bridge switched power supply for driving a series of CET coils. The power supply will act like a current source and the switching frequency fs = 1/Ts of the output voltage waveform equals 2.78 MHz.

matlab code for beging cc motor...

插值与拟合实验MATLAB程序,以及中文介绍方法(MATLAB interpolation and fitting the experimental procedures, as well as introduction of Chinese methods)

是用MATLAB作的SSB实现的解调与调制(MATLAB　　SSB)

This function calculates the RND from a cross-section of put prices using the PCA method as described in O. Bondarenko. ``Recovering Risk-Neutral Densities: A New Nonparametric Approach, UIC working paper, September 2000. (This function calculates the RND from a cross-section of put prices using the PCA method as described in O. Bondarenko. ``Recovering Risk-Neutral Densities: A New Nonparametric Approach, UIC working paper, September 2000. )

This contains the data mining implementation for classifiers