DigitalAudioWatermarking

对声音信号的时域分析频域分析采样并显示时域分析结果，还有录音并显示(Time-domain analysis of the sound signal frequency domain analysis of the sample and displays the time domain analysis, as well as recording and display)

希尔伯特-黄变换的matlab代码，仅供参考(Hilbert-Huang Transform)

时域波束形成实现早期乳腺肿瘤检测,采用超宽带一阶高斯波(time beamforming for early breast cancer detection by UWB )

一个关于检点检测的matlab程序 已经成功实现(A point detection matlab procedures have been successfully)

语音减噪，自适应滤波器减噪，谱减法减噪，维纳滤波法减噪等(Voice noise reduction, the adaptive filter, noise reduction, and reduce the noise spectrum subtraction, wiener filtering method of noise reduction, etc)

进一步采用基于距离和曲线形态的双尺度相似性度量谱聚类算法进行聚类处理，通过求n 个二次规划问题，就可以求得相似度矩阵W，降低了谱聚类算法对参数的敏感性，使算法更稳定(Furthermore, we use the two-scale similarity measure spectrum clustering algorithm based on distance and curve shape to carry out clustering. By finding n quadratic programming problems, we can obtain the similarity matrix W and reduce the sensitivity of the spectral clustering algorithm to the parameters Sex, make the algorithm more stable)

离散Hopfield神经网络的数字识别，可作为学习参考(Digital identification of discrete Hopfield neural networks can be used as a reference to learn)

频率步进matlab仿真:包括模糊图的生成，距离像的生成及允余去除拼接，最后还有雷达测角的matlab仿真，包括单目标及多目标(Frequency Step matlab simulation: including fuzzy map generation from the generation and Yun-I like the removal of splicing and, finally, the radar angle measurement of matlab simulation, including the single-objective and multi-objective)

感知器神经网络设计，通过matlab实现，可以作为一个较好的示例(Perceptron neural network design)

Imagescn plot two or more imagesc into the same axes. Deeper layers are visible, were higher layers have the value NaN. This is helpful e.g. to show measurement (=foreground) results "above" a map (=background), to separate NaN values within a 2D matrix from the lowest value (standard image shows the same color at NaN as it does for the lowest value). The number of layers is only limited by memory space :-).(Piled images Imagescn plot two or more imagesc into the same axes. Deeper layers are visible, were higher layers have the value NaN. This is helpful e.g. to show measurement (=foreground) results "above" a map (=background), to separate NaN values within a 2D matrix from the lowest value (standard image shows the same color at NaN as it does for the lowest value). The number of layers is only limited by memory space :-).)