watermarking_extraction

说明：  边缘提取源码，国内一高手写的，对计算机视觉等很有用，代码精炼(Edge source, the domestic one written by a master of computer vision and other useful code Refining)

电子文档介绍利用matlab进行计算机仿真教程。(Electronic document describes the use of computer simulation matlab tutorials.)

Traditional optical flow technique that developed by Lucas and Kanade was improved with the optimized variable setting. It is quite simple and also helpful.

基于遗传算法的断点估计，对非平稳信号进行分段，使其分段平稳，便于对信号的处理，尤其对于语音信号，非常常用(Based on genetic algorithms breakpoint estimates for non-stationary signal segment, making it piecewise smooth, easy signal processing, especially for the speech signal, very common)

matlab学习手册是里面内容介绍的很详细，是学习MATLAB很好的学习资料。(matlab manual is inside the content of the study described in great detail, learning MATLAB is a very good learning materials.)

在MATLAB环境下实现了CDMA通信系统仿真，程序流程为：随机序列产生--WALSH码扩频--BPSK调制--加入（和不加入）高斯白噪声--BPSK解调--WALSH码解扩，对过程波形进行了存储，对加噪与不加噪情况下的解码结果进行了对比，并绘制出信噪比与误码率关系图。(In the MATLAB environment to achieve a CDMA communication system simulation, the program flow as follows: random sequence generated- WALSH yards Spread Spectrum- BPSK modulation- to join (and not to join) Gaussian white noise- BPSK demodulator- WALSH yards Solution expansion of the process of waveform storage, to add noise and without noise cases, the decoding results were compared, and draw out the signal to noise ratio and bit error rate diagram.)

book about how to use matlab

matlab符号运算的ppt讲解教程，讲解通俗易懂，易于学习(ppt matlab symbolic computation tutorials explain, easy to understand explanation, easy to learn)

This program simulates the bit-error-rate (BER) performance of quasi-orthogonal space time block code (QOSTBC)

考虑L的三个不同值：L＝256（3个数据段），L＝128(7个数据段)和L=64（15个数据段）。各自的谱估计图如上图所示。可以明显的看到，加窗明显的减小了频谱上的假谱峰，但也更加进一步平滑了谱峰。所以，对于L＝64的情况，在ω＝0.8π的谱线可以很确定的辨认，但是那两个靠近的谱峰不容易区分。对于L＝128的情况，这种情况提供了在分离和检测间最好的均衡。当然，对于在L＝256时的情况，效果是更好的，能够从谱估计图上明显的分辨出三条谱线的存在以及它们幅度关系的强弱。 除了Welch法外，还可以采用对多个周期图求平均的功率谱估计方法的其他方法如Bartlett法等等，在功率谱估计上也能取得较好的结果。(consider three different values : L = 256 (3 data), L = 128 (7 data) and L = 64 (15 data). Their spectral estimation map as shown in Fig. It is clear to see that increase significantly decreasing window of the spectrum of false peaks, but much more to further smooth the peaks. Therefore, L = 64, in the case = 0.8 spectrum can determine the identification, but that the two peaks around is not easy to distinguish. L = 128 for the situation, which provided the separation and detection among the best balanced. Of course, L = 256 in the case, the effect is better, from the spectral estimation map clearly distinguish between the three lines of the existence and their relationship to the rate of strength. In addition to Welch law, but also can be used to map multiple cycle for the average power)