frequency-evaluation

最近邻分类器是一个用来聚类的算法，可以用来对iris数据进行聚类(k-means is a neanest alogorim)

蚁群算法的matlab实现程序代码，经过本人实际验证，能够很好的解决一些问题(Ant colony algorithm matlab implementation program code, after I actually verified, can well solve some of the problems)

相关信号的相位差检测，并用matlab 进行仿真验证(Related to the phase difference signal detection, and to carry out simulation matlab)

该代码是在MATLAB下运行的GBVS，用于图像特征点匹配，进一步用于图像处理，图像检索等多个领域。(GBVS the code is run in MATLAB for image feature point matching, and further used in various fields of image processing, image retri .)

本书详细介绍了如何利用matlab进行算法运行时间的分析和比较，并附很多例子，简单易懂。(This E-book covers mainly about how to use matlab to analyse the algorithm running time, and easy to understand)

详细画出了时域和频域的相关图，采用的是脉冲对消法，采用偏最小二乘法，最终的权值矩阵就是滤波器的系数，有小波分析的盲信号处理，最大似然（ML）准则和最大后验概率（MAP）准则，供做算法研究人员参考。( Correlation diagram shown in detail the time domain and frequency domain, It uses a pulse of consumer law, Partial least squares method, The final weight matrix is ??the filter coefficient, There Wavelet Analysis Blind Signal Processing, Maximum Likelihood (ML) criteria and maximum a posteriori (MAP) criterion, Algorithm for researchers to do reference.)

this program is for binary shift keying.

节点定位，仿真锚节点和未知节点的定位，用不同的符号表示。(Deploy Nodes………………………………)

本程序利用matlab实现利用压缩感知进行波达方向估计(This procedure using matlab to achieve the use of compressed sensing for DOA estimated)

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)