activeContour

由Manchester大学编写的电阻抗成像系统软件 版本3.3(Manchester University, prepared by the electrical impedance imaging system software)

Computes the modified hausdorff distance

matlab gui 自动滤波 iir和fir(matlab gui automatically filter)

rectifier design in matlab

matlab实现的短时过零率，mfcc和lpc源代码语音段都有(matlab to achieve short-term zero crossing rate, mfcc and lpc voice segment has source code)

线性规划的程序，能够解决显性规划问题，应用于实际中(Linear programming the main program, to solve the issue of explicit planning)

数字图像处理和机器识别C++和matlab识别书籍第三部分，讲解详尽，适合学习(Digital image processing and machine identification C++ and matlab identification books first part, explain the detailed, suitable for learning)

实现了 SVM 在医学图像分类中的应用，通过医学图像的实验结果验证了该方法的有效性。 对 SVM 算法进行了深入研究，结合量子行为的粒子群算法的优点，提出了一种量子行为的粒子群算法和 SVM 结合的混合分类模型，应用于医学图像的分类中。 深入研究了 K-means 算法，结合了无监督聚类和监督分类方法的优点，提出了一种 K-means 和 SVM 结合的混合分类模型，并应用于医学图像的分类中。 利用粒子群和量子行为的粒子群算法对 K-means 算法进行了改进，并将改进的K-means 算法与 SVM 算法结合形成全自动分类模型，并且把它应用于医学图像的分类中。 (SVM in the classification of medical image applications, medical images through the experimental results verify the effectiveness of the method. SVM algorithm conducted in-depth research, combined with the quantum behavior of the advantages of particle swarm optimization, PSO and SVM combined behavior of a quantum mixed classification model, applied to medical image classification. In-depth study of the K-means algorithm combines the advantages of unsupervised clustering and supervised classification method, propose a hybrid combination of a K-means and SVM classification model, and applied to the classification of medical images. Improved particle swarm algorithm using particle swarm and quantum behavior of the K-means algorithm, and improved K-means algorithm and SVM algorithm combined with the formation of a fully automatic classification model, and apply it to medical image classification.)

随机产生二进制序列，进行128QAM调制，画出星座图，通过加性高斯白噪声的信道，再画星座图，并计算误比特率。(128QAM modulate with MATLAB)

对目标轨迹数据加噪声，设置雷达经纬度，提取雷达可视部分(Add noise to target track data, set radar latitude and longitude, extract radar visible part)