dist2.m

基于Matlab GUI的图像处理的基本方法的整合，包括直方图、滤波、小波、水印、分割等等，参数自由可调，重要代码有详细注释，完全原创，上传交流(Matlab GUI for image processing based on the basic methods of integration, including histograms, filtering, wavelet, watermarks, split, etc., free adjustable parameters, an important source notes are detailed, completely original, upload exchange)

立体图像质量评价，文章以cyclopean image为核心构建算法。(Full-reference quality assessment of stereopairs accounting for rivalry)

该方法给出了一个CG算法求对称正定矩阵的线性方程的解，要求给出矩阵A、向量b和一个探测向量x0。(This method gives a CG algorithm for linear equations of symmetric positive definite matrix, requiring to give a matrix A, a vector b and a probe vector x0.)

论述了风力发电并网的PLL技术，基于传统PI-PLL技术基础上，以双同步旋转坐标系建立电网电压不平衡的数学模型。(mbalance of power grid voltage phase lock loop)

灰色预测GM（1，1）模型的matlab源代码，包括预测模型的建立，以及模型的精度检验指标c,p的计算(Gray prediction GM (1,1) model matlab source code, including the prediction model, as well as indicators of model accuracy test c, p calculation)

采用LDPC编码对随机的二进制序列进行纠错编码和基带调制，在AWGN信道上进行传输，然后在接收端并采用合适的方式进行解调和译码，最后对纠错编码的性能加以分析。(LDPC codes using random binary sequence of error-correcting coding and base-band modulation, for transmission in the AWGN channel, and then at the receiving end and use the appropriate manner demodulation and decoding, error correction coding on the performance of the final analysis.)

CML for LTE matlab simulation

一个演化博弈的小程序。非常有用。用matlab写的，非常非常有用。(a evolution game thoery simulation,a matlab program,it s very useful.really usefull,really really usefuly. kao,20 zifu?)

混沌粒子群及自适应粒子群算法，需自行编译适应度函数(Chaos particle swarm and adaptive particle swarm optimization, the need to compile the fitness function)

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)