xingchengbianma

本程序实现对车牌的检测识别，大部分的车牌为蓝色或黄色.通过蓝色或黄色在RGB色彩空间的表示范围，对寻找的目标进行标记，分割检测识别出车牌.希望对大家有帮助。(License plate detection and recognition of this program, most of the license plate in blue or yellow, blue or yellow in the RGB color space, said range, find the target marker, split detection to identify the license plate.)

图论中最短路径算法Dijkstra算法实现，很有用的(Shortest path algorithm Dijkstra algorithm in graph theory, useful)

说明：  基于matlab程序，对普通PID控制和模糊自适应PID控制进行仿真(Based on MATLAB program, the general PID control and fuzzy adaptive PID control are simulated)

图像分割图像分割基于MATLAB像素邻域自适应阀值分割图像 适合用本程序处理用MATLAB的图像处理的任务(Image segmentation image segmentation MATLAB adaptive threshold image segmentation of neighborhood pixels for this procedure for image processing tasks based on MATLAB)

在双轮平衡车中进行极点配置的Matlab仿真的实验(Matlab simulation of pole placement in a two wheeled balancing vehicle)

DTW（Dynamic Time Warping，动态时间归整）算法，该算法基于动态规划（DP）的思想，解决了发音长短不一的模板匹配问题，是语音识别中出现较早、较为经典的一种算法。用于孤立词识别，DTW算法与HMM算法在训练阶段需要提供大量的语音数据，通过反复计算才能得到模型参数，而DTW算法的训练中几乎不需要额外的计算。所以在孤立词语音识别中，DTW算法仍然得到广泛的应用。 (DTW (Dynamic Time Warping, dynamic time warping) algorithm based on dynamic programming (DP) ideas, sounds of varying lengths to solve the template matching problem, is speech recognition appeared earlier, more classic kind of algorithm. For isolated word recognition, DTW algorithm and HMM algorithm in the training phase need to provide a large number of voice data, obtained by repeated calculations to model parameters, while the DTW algorithm is almost no additional training calculations. Therefore, in isolated word speech recognition, DTW algorithm is still widely used.)

visual c++ FOR EMBEDDED SYSTEMS AND FOR REAL TIME APPLICATIONS

模糊控制方面的ＭＡＴＬＡＢ应用，ＰＤＦ格式(Fuzzy control in MATLAB applications, PDF format)

实现白噪声、正弦信号和带有白噪声的正弦信号的自相关函数的分析。(Achieve white noise, white noise with a sinusoidal signal and a sinusoidal signal of the autocorrelation function.)

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.)